Category: Artificial intelligence

Chef Watson is a machine ‘chef’ who can create recipes from almost any ingredient. We’re familiar with Siri and Alexa, virtual assistants that do everything from setting alarms to translating foreign phrases. Tesla’s electric cars come with the Autopilot feature that lets them auto-steer, change lanes, navigate, and self-park. What do these three things have in common? Artificial intelligence. Whether they come as software solutions or embodied in products, AI systems are everywhere we look, and they have become a driving force in most sectors of the global economy. The use of artificial intelligence raises several important legal questions, but to analyze these issues it is important first to understand what it is and how it has evolved.

Intelligence is defined by the Merriam-Webster Dictionary as “the ability to learn or understand or to deal with new or trying situations; the skilled use of reason.” Intelligence has also been described as “the ability to think, to learn from experience, to solve problems, and to adapt to new situations.” It may become obvious from a closer examination of these definitions that one could, with these terms, just as well describe a robot as one could a human being. For example, when Google Maps factors in congestion delays and extends the arrival time from what was first displayed, the application has taken a new situation of things, analyzed it, and adjusted accordingly, displaying a level of intelligence. This is not to say that machines and humans display the same type of intelligence: natural and artificial intelligence differ in numerous ways, but they are fundamentally similar in that they involve the application of learned information or data to solve a problem or carry out a task.

Artificial intelligence does not lend itself easily to a definition, but many scholars adopt the following definition given by Elaine Rich: “Artificial intelligence is the study of how to make computers do things at which, at the moment, people are better.” In simple terms, artificial intelligence is a simulation of natural intelligence. Thus, the ability of a machine to carry out high-level cognitive functions that require intelligent behavior would qualify as artificial intelligence. Artificial intelligence can have simple or complex utility functions. Artificial intelligence is built on algorithms: it is, essentially, layers of algorithms, some even self-programming. An algorithm is a set of specific instructions for solving a defined problem or solving a task: the internet has made them ubiquitous in everything from the apps on our phones to video games. For example, social media such as Twitter and Instagram use algorithms to sort content and decide its visibility to each user. Complex algorithms support the structure of strong artificial intelligence.

How does artificial intelligence depend on algorithms? The answer is quite simple: machine learning. Machine learning is a central sub-field of artificial intelligence that focuses on algorithms or systems that learn from data and improve automatically. In other words, artificial intelligence becomes more efficient because the algorithms that make it up can get better at identifying patterns in data and making decisions with minimal human intervention. Advancements in the field of artificial intelligence have what is known as ‘the AI effect’: when a function becomes significantly mainstream, it is no longer considered artificial intelligence. For example, optical character recognition (OCR) is no longer considered AI because it has become a routine technology.

Artificial intelligence, much like any scientific development, is built on foundations that far precede it. A discussion on its history will take us way back to ancient times, but actual developments started in about the 19th century. Studies in (philosophical) logic and theoretical computer science far predate artificial intelligence. 1884 in particular is a very important year for artificial intelligence. It was in this year that Charles Babbage worked on a mechanical machine that should exhibit intelligent behavior. However, he later decided that he would not be able to produce a machine that would exhibit as intelligent behaviors as a human being, and considered his work suspended. In 1937, Alan Turing, a British mathematician, developed a halting problem that pointed out the limits of intelligent machines. In 1950, he created the Turing test to assess the intelligence of computers, and the intelligence level of the machines that passed the test at that time was considered adequate. Alonzo Church, an American mathematician, further contributed to Turing’s work, birthing what is known as the Church-Turing thesis.

Artificial intelligence officially became an academic pursuit at the historic Dartmouth College conference in 1956 where the term was first used. The first artificial intelligence applications were introduced during this period. These applications were based on logic theorems and chess games. The programs developed during this period were distinguished from the geometric forms used in the intelligence tests, which supported the idea that intelligent computers could be created. Professor John McCarthy, an American computer scientist referred to as the ‘father of artificial intelligence’, is credited with coining the term as well as starting the AI laboratory at MIT around 1956 and at Stanford University in 1963. He developed LISP, a programming language described as “the most important tool for the implementation of symbol-processing AI systems.”

ABSTRACT

Artificial intelligence is a advanced technology, which drivevariationof economy and society ominously in the proximate future. It tinbe employed to switch human labour ship effecting various perilous then dreary chores, providing us through additional convenient and competent life. It can be a slice from the wide-ranging application of the emerging technology. In the paper, we make discussions on the smart security, privacy, safety and innovative issues in artificial intelligence applications and plug out the possiblehazards and threats. Securityactions in research and supervision are proposed and our anticipation for artificial development will be clearly explained below.

Introduction

AI techniques, present drive likely stand new robots or brainy programs that can support as human’s assistants also ensure a slice for us, such as reading email, housework, or even driving cars. This one will also fetch us control on privacy, security and ethic. Several uncontrolled significances might ascend from AI applications if we be unsuccessful to find and avoid related threats in advance. In this study, we deliberate the latent hazards and threats of artificial intelligence, elevation warnings and stretch suggestions.

Artificial Intelligence and Applications

Artificial intelligence can be categorized by two types. Weak AI and strong AI. In the category of strong AI, AI system must be considered as human-like high level perception ability, such as common sense, self-awareness and creativity, while weak AI simulates human intelligent processes passively without physical understanding. Weak AI is designed to finish a particular task, while strong AI is usually understood a general AI system, which takes the skill to achieve several kinds of intelligent tasks. Current AI systems are entirely at the stage of weak AI and strong AI sort out not yet exist so far. It is imaginary that it would take spans for human to realize strong Atypical AI techniques take in machine learning, speech Recognition, natural languagedispensation,robot,etc. artificial intelligence has remained used universally in our life, from speech text input and tailored network shopping, to several smart answering systems. AI applications in these fields such as education, science, engineering, business, medical care and manufacturing etc.

Health care

In AI techniques, smart home-based system tin monitor in our daily life, including sleep time, isometrics, diet predilections, and seizure signs of their changes. Future lavatory perhaps can detect through excreta to regulate if body is healthy, and provide relevant information to doctor. Such system can assist doctors in making decision without consulting specialists which are unusual resources in rural areas and in many developing countries. Finance: Manufacturing and administration can use artificial intelligence to grasp early detection of anomalous financial hazards and to reduce spiteful actions such as manipulating markets, fraud and unusual transactions. There are many AI techniques, like artificial neural networkand support path machine, used by commercial bankers and business consultants to make bankruptcy prediction and company financial distress prediction.

Education

I can ranking and assess students in an intelligent way and help them learn at their own pace.AI technique can build a new education system that includes intelligent evaluation and interactive learning. Through the help of smart education assistant, tutor can be provided with additional support by knowing the status and learning progress of each student at any time.

Smart Security, Privacy in AI Applications

In artificial intelligence systems are facing these types of problems.

Security Problems:

There are different types of security problems of AI including security threats of technology abuse, security problem made by technical flaws and self-aware intelligence induced security problems.

• Security threats of technology exploitation: It is believed that artificial intelligence is a neutral technique. If it is harmed by spiteful people, the technique may fetch us problems of security, discretion and tenet. Research shows that attackers can blastoff a large-scale attack with only a slight resource using intelligent methods. Invaders may also use AI technology to access remote information illegally.AI systems may do something harmful to people in control of criminals.
• Security problems induced by technical faults: In recent artificial intelligence system is future from perfect. Sometimes AI system could not be secure as it seems due to some technical faults. Solitary is technical imperfection as robots, tools, controllers and related mechanisms are not correctly designed or tested. One more reason is improper management; affecting many robotic fates occur under unusual working conditions such as software design, maintenance, testing, installation or alteration.

Secrecy Problems:

In current centuries, big data focused prototype has conquered artificial intelligence research and has ran to a new tint of AI development. present machine learning, the number and quality of data sets will affect in high degree the training results, so maximum of successful AI applications depend on heavily onbig data. As secrecy problem is a main threat of data analysis, inevitably, there will also be privacy problems in the applications of artificial intelligence. data acquirement: In AI,wide use of smart home devices, multiplicity of data can be kept for years or even decades. These data, if used correctly, will type life better. But some of private information would also be used illegally by technology businesses for commercial purposes. cloud computing: In cloud computing, many corporations and government organizations are drifting the data into cloud, because it is cheap, easy to use, and suitable in getting on-demand system access to a communal tarn. our private information is also stored in cloud, our secrecy need to be make sure.

Decent Problems:

the most special problem that rapidly changing AI technology may bring to us, almost all scientific and industrial personnel will believe tenet is the focus of attention, due to the human like brainpower ability of this powerful technology. Decent problems might be induced by following issues.

Conduct rule: Artificial intelligence robots must study rules formerly making decisions. If the design of intelligent mediator is not unified with the human restraints, it is likely to track a dissimilar logic with human actions and prime to rotten values. To make new systems help the whole communal, not fair the regulator of the system, through compelling actions of AI systems to fulfill with predefined communal tenet rules.

Destroy of robots. There is a stimulating Query around the tenet of AI system can kill an intelligent robot to grasp that robots are risky for us. The first tricky in this subject is that can kill robots as we famine. The killing of robots in the earlier may be an fate. But, it may be planned someday in the future. If these robots are not pleasant or don’t comply with human beings, must control them or kill them.

All these glitches must be careful before in design process of such AI systems.

Hostage measures And Deliberations

Artificial intelligence is able to make human life more powerful, so far with security, privacy, ethic and additional risks at the same time..

Highlighting Safety, Secrecy and Tenet Research

Owing to latent abilities and difficulty of AI, and its close connections with human users, research on the safety of artificial intelligence technology is particularly important. Scholars and academics need to highlight additional on safety protection and try their best to make artificial intelligence more secure.

Implanting tenet rule: the artificial intelligence systems take movements by their own,their performance will be gifted to fulfill with strict and casual rules that humans need to follow, counting ethical, legal rules. These rules should be measured and drive in in the AI system during expansion period.

Refining safety and heftiness: In many gears, artificial intelligence system is intended to operate in a compound situation. AI system should be healthy to contract with surprising conditions and must be safe sufficient to manage with a wide range of deliberate attacks. Beforehand putting an artificial intelligence system into a wide range of applications, it is necessary to make sure that the system is safe, steadfast and controllable.

Strengthening Regulation

The artificial intelligence is also significant for conduct the safety, secrecy and decent problems of AI besides the technology research itself. Research on tuning, lawmaking and rule should be accepted out to make sure that the application of artificial intelligence is in control.

Law & Policy creation: artificial intelligence may bring potential terrorizations and risks, the administrations can make laws and related policies to define artificial intelligence can do or what is not permissible. Organization needs to recover terms of laws and lay down rules for AI industry and products usage, such as the concern for the accident of unmanned vehicles, air UAV’s assault of special privacy and so on.

Conclusion

Artificial intelligence is developing at an stirring speed. It can fetch us adeptness and suitability, but we essential to evade damage to humans. While wide range AI applications and their influential impact have not seemed in our lives, it is needed to discourse the public and decent issues potency be elevated by AI in advance. Highlighting on security, secrecy and tenet issues had better waged enough care by AI researchers. It is not advisable to carry out severe observation on artificial intelligence especially at present application stage, so as not to build difficulties to technology revolution.

References

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Artificial intelligence (AI) became a topic of interest these days. AI is a broad area of computer science that makes machine. In simple words, AI refers to systems or devices that simulate human intelligence to perform tasks [1]. Nowadays, due to the world’s improvements and to our abilities in teaching machines to act like humans, artificial intelligence applications can be seen in many areas such as health, education and business . AI is used in a variety of day to day activities such as social media, email communications and digital assistants. Collecting data from emails, social media and web is called AI marketing. In this literature review, examples of AI marketing will be mentioned , in addition to the impact of using AI on marketers.

According to henry Schuck , CEO of DiscoverOrg: “Any part of the marketing world where a marketer has to read data and make decisions based on that data will be affected by AI in one way or another in the near future” [2]. Marketing teams spend most of their time on drafting social media updates, preparing reports, personalizing emails and managing paid media spent. These tasks are considered repetitive and complex, and they could be done more efficiently by using AI. AI technology helps to ensure that your consumers are only receiving the most relevant, valuable and personalized content. Several consumers won’t interact and may ignore non-personalized marketing. According to A report by management consulting firm Accenture ,over 40% of customers switched brands due to the absence trust and poor personalization [3]. with AI, marketers will be able to understand and know exactly what consumers are thinking, saying, and feeling about the brand. For instance, when you log in to Netflix or amazon you will find a list of suggestions and recommendations based on what you watched recently, this application was able to uses predictive technology to offer recommendations on the basis of your reaction and interests.

one of the important applications of AI in the realms of content marketing is speech recognition. If you are using apple devices then you will definitely know Siri. Siri is a virtual assistant that uses AI and it is available in all apple devices. once your request is received, your microphone will record your voice and it will be translated to a code. Furthermore, Siri is designed to offer you smooth way of interacting with your devices. You can ask her to show you something or issue her with commands, hands-free. She can, text, suggest nearby places and has the access to all other application on your apple device. In addition, you can ask her to carry out a task just by saying hey Siri. Another example is Amazon’s Alexa, a virtual assistant that uses AI too. However, in compare to apple’s Siri, Alexa is a device, it is not a voice assistant only. It can perform a variety of tasks such as playing music or setting an alarm, and controlling a smart home by locking doors or dimming the lights. These are just two of more than 70,000 skills that Alexa can perform. Recently there are more than 28,000 smart home devices that work with Alexa[3].some companies uses Alexa to schedule their meetings and to join conference calls as well.

As a marketer, technology is here to enhance your role and simplify your tasks. Many fear that AI will take over the need for marketers. AI will transform and improve the life of marketers, but will never replace them [5]. In fact, AI will change the way marketers work by helping them to be more precise and efficient as it forces them to be more data driven. On the other hand, by automating the complex tasks, humans who work in the marketing sector will have the opportunity to concentrate more on other important key components of marketing such as advertising, customer services and creativity. According to [6], when humans and machines worked together, companies achieved powerful developments and improvements. This confirms the positive impact of humans working with machines in marketing.

The reviewed literatures suggest that artificial intelligence can have an effective impact on marketing field without replacing humans. Whereas humans working with machines will increase the productivity and creativity. To add on that ,AI and humans will enhance each other’s strengths and most of companies takes full advantage of this collaboration.

References

1. “Artificial Intelligence Essay for Students and Children: 500 Words Essay,” Toppr, 25-Oct-2019. [Online]. Available: https://www.toppr.com/guides/essays/artificial-intelligence-essay/. [Accessed: 20-Sep-2020].
2. Mullan, E., 2020. The Uses Of Artificial Intelligence To Marketers. [online] Blog.hurree.co. Available at: [Accessed 21 September 2020].
3. Brenner, M. and Brenner, A., 2020. 5 Essential Benefits Of AI For Digital Marketers. [online] Marketing Insider Group. Available at: [Accessed 21 September 2020].
4. Bernard Marr. 2020. Are Alexa And Siri Considered AI?. [online] Available at: [Accessed 21 September 2020].
5. L. Kinthaert, “Will AI Replace Marketers? Seven Experts Weigh In,” Informa Connect, 22-Oct-2018. [Online]. Available: https://informaconnect.com/will-ai-replace-marketers-seven-experts-weigh-in/. [Accessed: 23-Sep-2020].
6. H. James Wilson and Paul R. Daugherty, “How Humans and AI Are Working Together in 1,500 Companies,” Harvard Business Review, 19-Nov-2019. [Online]. Available: https://hbr.org/2018/07/collaborative-intelligence-humans-and-ai-are-joining-forces. [Accessed: 23-Sep-2020].

ABSTRACT

Artificial intelligence has been in place since the year 1956, but considering the recent momentous acceleration in the accounting and finance industry it has become the vital topic in business. It plays a foremost role in the way the functions are performed in an organisation. Recently, Artificial Intelligence has revolutionised the efficiency, quality and time taken to accomplish these functions in contrary to manual performance. In fact, the very same difference has sparked controversial debates.

There is no doubt the structure, working mechanics, required skill level etc. is altogether transforming with the operation of Artificial Intelligence to remain profitable in the economy and competitive in the market. In support to the well-known theory, the survival of the fittest, now professionals and employees are also expected not only to work in par with technology but also to control them. This, largely affects the attitude of individuals working in an organisation. Also, it is a discerning move from the management to manage the cost, learning curve and all other uncertainties.

Therefore, this paper mainly focuses on the aspiration of understanding the use of Artificial Intelligence in Accounting and Finance industries which in turn assess its effectiveness and performance. Since, employment is another major factor affecting our economy it is also essential to comprehend their value and their knowledge regarding the recent developments. Thus, this paper would be imperative to analyse the attitude and expectation of the professionals and also, the efficiency and evolution of these industries with the employment of Artificial Intelligence.

INTRODUCTION

Artificial Intelligence (AI) implies to the simulation of human intelligence that are programmed to think like humans and impersonate their activities. It presents opportunities to complement and supplement human intelligence and enrich the way people live and work.

India being one of the fastest growing economy and having second largest population in the globe has a substantial stake in the revolution of AI. It is not distinctive when it comes to the significance of AI in Accounting and Finance Industry. AI is more than merely robots that comes into one’s mind when trying to picture the word. It has been transforming these industries through various applications available, such as, data collection, chatbots, personal assistance, consumer interaction, risk assessment, cybersecurity, etc. without emotional and psychological factors affecting them. All the applications can be classified under the different categories of Artificial Intelligence – weak or strong or general centred on its capabilities and reactive machines, limited memory, theory of mind, self-awareness based on its functionality. Whatever maybe the application or the category of AI used by the industries, the aim of AI includes, 1. Learning, 2. Reasoning, 3. Problem solving, 4. Perception and 5. Language.

With technological advancements, AI has been evolving to beat its own previously set benchmarks and benefits. Since, the world seems to be transforming with AI, it is no doubt that it has created a fear of employment and replacement among the people as. In contradiction, it is also said that AI is only to eliminate tedious mundane jobs enabling professionals to perform more higher-level tasks, lucrative analysis and counselling. Also, it is believed that new jobs will be created through micro-economic and macro-economic effects. As of October 2019, a joint research conducted by the National Business Research Institute and Narrative Science stated that only about 32% of monetary service providers have adapted or embraced AI. In addition, a joint survey conducted by EY and Invesco stated that the adaptation of AI is expected to be 64% in the ensuing two years. Thus, AI has become the inevitable by gaining constant attention from everyone including the Government of India. The Indian Government conducted and published a discussion paper on National Strategy for AI with the help of NITI Aayog. Therefore, it is imperative to recognize the level or degree of impact AI has on these industries and the work force to analyse the current work environment and the future.

CONCEPT OF ARTIFICIAL INTELLIGENCE USED

Almost all the industries are now aware of the existence of AI and the possible benefits they could gain from its application if due care and diligence is taken. The following are some ways in which this fast-moving technology has been put to use for everyday activities by the accounting and finance industry. To begin with, AI based invoice management systems helps in payment/receiving process by saving time, cost and errors. Secondly, AI can examine a supplier’s tax details and credit scores all by themselves without a need for human hand and provide the suppliers with required data by setting up query portals. Thirdly, with the use of application programming interface, records maintained in different systems can be processed together which helps avoid massive amount of paperwork. Fourthly, with digitalization financial transactions can be both recorded and audited. It has highly improved the efficiency and productivity of an organization by helping them meet their goals and make better judgements. It also helps in acquiring and securely consolidating financial statement. If any breach or fraud has been committed then the AI is powered to alert the management. Fifthly, the most recent development is the introduction of chatbots. It has been made user friendly that consumers find their queries solved within minutes rather than waiting in line for customer care services.

AI also helps companies in making smart credit and underwriting decisions which helps them reduce predicted risks and losses. It also helps them review a client and provide loans more securely and avoid the issues of non-payment. Machine learning, a subset of AI is also used to create models which helps in forecasting and prediction of data. AI is also used in identifying large data sets and analysing patterns that can be used to make strategic trades. It has been used by financial institutions and especially brokers. Most important of all, AI is a major key to improve cybersecurity and fraud detection since everything is now stirring online. Thus, AI has become a necessity.

IMPACT OF ARTIFICIAL INTELLIGENCE

With the rise of neo banking and never-ending expectations of consumers spread around distinct geographical locations accounting and finance industries started gracing AI at the staff level which has steadily climbed the ladder and tends to continuously do so. It has greatly transformed and impacted the industries in the following manner;

Positive Impact of AI

• It has enabled hyper-personalisation which helps them provide customised services suitable to every individual needs. AI has shaped these industries by enabling self-service which provides customised solutions to them anywhere and anytime. This led to the rise of online banking and e-trading etc. Also, the organisations method of consumer interaction has vastly changed. No longer are there queues to get basic questions answered or phone calls on hold. Chatbots and online query services which are accessible 24/7 has changed the game.
• It has created a provision wherein the back-office operations can be fully automated which reduces the human requirement in those areas. In fact, it has been augmenting the human work. The industry has also witnessed a shift in talent from financial institutions to service providers. Also, large cost was involved in this shift from human intelligence to artificial which in turn allowed organisations to chase high margins and advance innovation and growth.
• The scale of operations has highly increased as large volumes of data can be recorded, classified and analysed much faster than done by man. AI is also being used to keep track of consumer behaviour in online platforms which further changed the way in which data is collected and analysed regarding apps usage, pattern, fraud, anomalies etc. Moreover, with rapid use of AI, e-technology has started to normalise traditional metrics like price.
• There is an upcoming uniformity in the manner in which tasks are performed across institutions as they are now able to consume the same capabilities and hunt new differences. Conferring to the WEF report, only 7% of professional services respondents said that advances in AI and machine learning are making it possible to automate knowledge-worker tasks that have long been regarded as impossible or impractical for machines to perform.
• AI has created a whole new market where innovative and creative entrepreneurs step up and bring in a whole new level of financial institutions such as Fintech’s.

Negative impacts of AI

• Mid-sized firms also faced a negative impact as they became less profitable and a prey to the large-scale players. Usually, small scale firms follow the lead of large firms. The cost involved males it difficult for them to compete in the market.
• It’s being predicted that AI may replace about 9% of incumbent financial service jobs by 2030 while on the other side of the coin Fintech anticipates AI to expand their workforce by 19% within the same period.
• AI has also shed light on new skill required by the workforce. In case of skill deficit, institutions spend on trainings and sometimes individuals are put out of work.
• The calculator, using research by the University of Oxford, said accountants have a 95% chance of losing their jobs as machines take over the number crunching and data analysis.

PERCEPTION OF FINANCE PROFESSIONALS TOWARDS ARTIFICIAL INTELLIGENCE

Employment has always been a ride through the hills and job security is considered in the human minds as its safety belt. AI has been making drastic changes in the world that it has caused a sense of doubt regarding the future of jobs within the minds of not all but some employees.

Many have been open and approved the idea of AI in their work environment. While some feel that it is just the beginning of human intelligence replacement. Yes, AI might be the reason for some labour turnover in an organisation but it is also said be the reason for job creation. To elaborate, AI can remove jobs at the lower level which requires no human intervention and is repetitive in nature but there is still not enough progress to completely take over judgement-intensive, advisory or consultative jobs. Artificial Intelligence and Human Intelligence are said to be complementary to each other for instance, AI can collect and analyse data but individuals are required to interpret them according to the requirements and make a decision.

It is the responsibility of the organisation and human workforce to work together in breaking the common misconception of AI taking over and replacing individuals and bringing in positive attitude towards it. AI has created a transformation in the skills to be adapted by the work force. More refined and advanced are now required to work alongside with AI and also use them to their advantage such as increase productivity and efficiency thereby saving cost and time. Thus, adequate training, learning and understanding of AI will be huge step towards successful implementation of AI.

OPPORTUNITIRES OF AI

The disruptive potential of Artificial Intelligence are as follows:

• Cloud based technology is being used for storing and transferring data as it enables constant monitoring while Blockchain technology is gaining momentum by simply enabling users to get to the fine records, create smart contracts etc.
• Cost saving is definitely a core opportunity for companies setting expectations and measuring results for AI initiatives. Also, AI creates opportunity for achieving revenue enhancement goals including creation new products and enhancing existing ones.
• It has increased transactional and account security and is highly capable of reducing or eliminating transaction fees due to avoidance of intermediary. Whereas, face recognition using real-time camera images and advanced AI techniques such as deep learning can be used at ATMs to detect and prevent frauds/crimes.
• Cognitive computing helps digital assistance and apps to improve themselves.
• The level of transparency can be increased with AI and more systematic check systems can be implemented.
• AI can completely transform the procurement process which is a major step towards paperless entries. It simplifies accounts payable and receivable process thus enabling efficient accounts and proper audit reports. It also helps maintain the financial statements of the firm and make a possible comparative analysis with rival firms.
• It enhances marketing through real time analysis which provides the firms opportunity to target ideal clients and pursue new markets.
• Personalized portfolios can be managed by Bot Advisors for clients by taking into account lifestyle, appetite for risk, expected returns on investment, etc.

CHALLENGES OF AI

Artificial Intelligence can be challenging too which is elaborated as follows:

• There is a need for computing power which is difficult for start-ups and small businesses.
• The assessment and forecasting abilities are dependent on the inputs fed into its system. If there is a bias there’s a chance for it to stay hidden.
• Though AI is said to replicate the human mind the algorithms, if, are designed for a specific task it does not deviate.
• AI is not given full autonomy in an organisation as there still lies the question of responsibility when things go wrong.
• AI implementation requires huge capital investment but the returns are slow at the initial stages.
• The professionals no longer can sustain themselves with only accounting knowledge but also require additional skills and knowledge in information technology.
• Data volumes and quality are crucial to the success of AI systems. Without enough good data, AI models will simply not be able to learn.
• AI poses a global risk for all the incumbents as they can be beaten in competition by large firms.
• There’s the risk of privacy and security of information stored in automated platforms.

Thus, AI has its own share of opportunities and challenges in the accounting and finance industry which are still be explored and overcome.

CONCLUSION

The impact of AI on accounting and finance industry has been revolting. AI has proved its efficiency and productivity through various benefits provided by its very own applications. It acts a support system to the human minds in completing the tedious, repetitive tasks without much or no intervention. It has transformed the level of work done by the professionals in an organisation elevating them to do higher level tasks. It does hold certain drawbacks regarding the employee morale, learning curve, advancements and cost involved which might get sorted out over time.

The opportunities and challenges presented by AI is immense over all areas of work. This has only led to further research and progress of the present technologies. Also, AI replacing the human intelligence is a myth. Though it might lead to some basic level job losses it also creates sophisticated skilfully refined jobs. The scope of AI is only said to widen as institutions continuously try to remain competitive and profitable in the market.

Thus, it can be concluded that the challenges and risks of AI needs to be combated as artificial intelligence has overall created a positive impact on these industries and has set the future path for them as well.

REFERENCES

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With digital upheaval rippling across the world rapidly, transforming industries and revolutionizing businesses with its power, no sector can afford to get marooned to the sidelines. As every industry engages in designing and developing approaches and methods to remain relevant in a world steered by technology, the banking sector is no exception.

Customers, now familiarizing themselves with advanced technologies and techniques in their everyday lives, no longer expect banks to be characterized by long queues, frequent visits and excruciating degree of paperwork. They need transformations and they need them fast. To keep pace with these expectations banks have bolstered their industry outlook to retail, IT and telecom in order to facilitate services like mobile banking, e-banking as well as real-time money transfers.

As preached, “With great power comes great responsibility”. Ergo, as digital advancements hold the power to accelerate all banking related transactions and make procedures more convenient and accessible for the customers with the transfer of information through virtual networks, simultaneously it also escalates the vulnerability of critical information to cyber-attacks and fraudulence, endangering both the bank’s profitability and its goodwill.

Thus, the government regulations imposed on banks as a result of rising security threats and the compulsion of having to maintain the capital adequacy ratio as per the international regulatory framework guidelines restricts their capacity of keeping pace with the digital advancements. This subjects them to competition by the lithe financial technology (FinTech) players who are not obligated to preserve the capital adequacy ratio. Artificial intelligence, as a result, becomes a powerful and handy weapon that is wielded by banks for enabling the power of digitization for banks and support them to compete with the rising competition extended by FinTech players.

In a promptly digitalizing world, artificial intelligence has emerged as the future of banking, propagating the power of advanced data analytics in order to withstand fraudulent transactions and improve deference. It incorporates deep learning, predictive analytics, as well as machine learning for enabling an enhanced banking experience. AI assists in fraud detection, credit risk assessment, reduction of costs as well as risk management. Alongside these aspects, the sector is also leveraging for battling with frauds and hacks while simultaneously abiding with KYC and AML compliance regulations.

Artificial intelligence, in layman’s terms, is basically the simulation or imitation of human intelligence to use it in machines and program them to think in terms of humans and to mimic their actions. The term can also be applied to define any machine or software which manifests traits that are associated with the human mind. The AI algorithms can tackle learning, perception, problem-solving, language-understanding and logical reasoning.

Generally being the early embracers of most new technologies, banks leverage AI particularly in their front office (conversational banking), middle office (anti-fraud) and back office (underwriting). Following are some areas where artificial intelligence has been of prestigious value in the banking sector.

Chatbot

We’ve all come into contact with chatbots at some stage or the other such as while accessing e-commerce websites, while reaching out to customer support or while booking hotels or flights. Chatbots are AI-enabled conversational interfaces. They can handle compelling conversations on behalf of the bank with millions of consumers, at a fraction of the cost. They possess the potential of bolstering the bank’s customer’s experience and their convenience.

With people no longer holding the time and patience to be physically present at the bank for all tasks and also in cases where the banks are closed, the chatbots step in to serve as the saviors. Their 24×7 availability and efficient customer service makes them an excellent apparatus for the sector. Chatbots also assist customers in seeking their transaction details and all additional services that they are eligible to receive. They are programmed to comprehend the customer’s requirements and offer them the appropriate response. An example of a popular AI chatbot is virtual financial assistant Erica, introduced by Bank of America. Erica plays a crucial role in fulfilling the bank’s customer service requirements through numerous ways, be it by sending notifications to customers, providing balance information, sharing money-saving tips, providing credit report updates, facilitating bill payments and helping customers with simple transactions, etc. The chatbot’s capabilities have recently been expanded to help clients make astute financial decisions, by providing them with personalized and proactive insights.

Fraud Protection

Security is of paramount importance in all sectors, particularly in case of financial institutions like Banks that face an eternal threat of frauds and hacking. Through the combined use of supervised and unsupervised machine learning to interpret insights absorbed from trends, AI helps in decreasing false-positive rates, avoiding fraud attempts and reducing manual reviews of potential payment frauds. AI is used to fend off identity theft by incorporating biometric identification systems like voice and facial recognition, into the login module for strengthening the identity verification process.

As technology advances so do the complexity posed by payment fraud attacks. Having a digital footprint or sequence that makes the attacks undetectable through the sole use of predictive models enhances the importance of AI as it assists in mitigating these attacks and in providing a security layer to the bank. Its prompt and large-scale detection of payment frauds makes it an excellent asset for banks in handling such cases.

AI’s predictive analytics and machine learning allow for inconsistencies in large-scale data sets to be traced in seconds.

Mobile Banking

The option of mobile banking has been an easy and convenient resolution for the customers who no longer feel the necessity to be physically present in the bank for all menial tasks. Comprehending the extensive perks and benefits of mobile banking, users now enjoy this service owing to its safety, security and easy access.

An excellent example of mobile banking would be Varo Money, a company that has worked diligently on reinventing banking’s approach and merging financial experiences into their users’ daily lives. Their app Varo, is an intelligent mobile banking app that engages in enhancing consumers’ financial health by advocating positive spending, savings, and borrowing habits. Intelligent banking apps can provide customers with personalized insights and recommendations wherever and whenever they want. AI assists in personalizing the mobile banking by offering real-time customer support through the use of analytics and machine learning, offering advice and personalized communications through robo-advisors, assisting in personal planning, personal reminders etc.

Customer Engagement

Massively impacting the goodwill of any organization, customer’s experience is one of the most crucial aspects to be considered. This is especially in cases of banks where 24/7 availability and swift transaction is required. AI therefore assists in ensuring that the banking transactions flow smoothly and effortlessly. This is done through the development of various AI powered features such chatbots and biometrics.

An example of one such feature is when NatWest, became the first major U.K. bank to allow its customers to open accounts remotely with a selfie. The AI-powered biometrics which the firm developed with its software partner HooYu, match an applicant’s selfie to a passport, government-issued ID card or other official photo identification documents in real time.

Credit Risk Assessment

“Speed is of the essence in credit risk management. The earlier we detect any risk, the quicker and better we can serve clients to prevent losses. Through machine learning, the EWS scans financial and non-financial information, such as news items from all over the world”, – Anand Autar, project leader, ING.

AI-driven models are capable of facilitating immediate assessments for credit risk evaluation of a client. This helps banks in providing the right offer to their customers. In case of pricing and underwriting services, artificial intelligence can cut down the turnaround time and escalate the whole process. AI increases the efficiency of client proposals and boosts the overall customer experience.

Cost Reduction

Banks could save a humongous $447 billion by 2023 by deploying artificial intelligence (AI), as stated by AI IN BANKING research report from Business Insider Intelligence. Employment of AI allows banks the scope of cutting down on 3 main areas:

1. Reduces cycle time. With the automation of the digitization process the time spent on digitizing, discovering and onboarding document templates is reduced which allows the bank to redeploy its employees to more paramount projects.
2. Minimizes rate of errors. The automation in banking systems allows for errors to be reduced without there being any escalation in the cost. AI systems quality of excelling at handling unstructured data awards them the advantage of lower error rates.
3. Solution costs. As per IBM data the traditional onboarding process for document digitization costs over hundreds of millions of dollars for a single department. By leveraging AI tools that can be 80% automated and have the potential of 90% accuracy, cut down their onboarding process, putting more focus on data validation over physical presentation and scanning. This would help curtail error rates while also making more competent use of employee effort.

Conclusion

I would conclude by stating that while AI and ML have the power to continue to hugely offer an edge to the banking industry, yet the technology’s full potential can only be experienced if there is full infrastructural support. As banks increase their reliance on ML to provide predictive analytics, they will need to meet new regulatory and interconnectivity demands.

Innovation usually refers to changing processes or creating more effective processes, products, and ideas. Innovation can act as a catalyst for growth and development which can help one secure success in the marketplace. The importance of innovation in creating competitive advantage and improving organizational growth cannot be understated. Technological innovation is often misunderstood as people believe it’s solely related to computers or electronic products such as cellular telephones or international networks. Technological innovation also doesn’t only occur in complex products, processes, or systems. Technological innovation does not have to be complex, but it has to be new and aim to implement the technology it embodies in the marketplace. In short, technological innovations comprise new products and processes, as well as technological changes in products and processes. Even as new technologies are developed, innovation around the application of existing technology is rapidly changing how organizations operate and how we interact with the world. Some of the technological innovations can be listed as artificial intelligence, Blockchain, and automation. Some of the emerging technological trends are citizen development, self-powered data centers, drones, and done ops centers. Artificial intelligence is about machines with human attributes. Using algorithms that adapt to various factors like location, speech, and user-history machines can perform tasks that are tedious, more accurate and much faster than humans without exerting much of an effort. Within a few years, analysts predict that all software will use AI at some level, according to US research and advisory firm Gartner. The field of AI research was first founded at a workshop held on the campus of Dartmouth College in 1956. Investment and interest in AI rocketed in the late 1900s, more precisely in the first decades of the 21st century when machine learning was successfully applied to many problems in academia and industry.

The beginning of the field of AI was founded in 1956, at a conference at Dartmouth College, in Hanover, where the green ‘artificial intelligence’ we first coined. MIT cognitive scientist Marvin Minsky and others who attended the conference were extremely optimistic about AI’s future. After several reports criticizing progress in AI, government funding and interest in the field dropped off from the period of 1974-1980’s that became known as the ‘AI winter’. It was later revived in the 1980s when the British government started funding it again in order to compete with the Japanese one. Research began its pace after 1993, and in 1997, IBM’s Deep Blue became the first computer to beat a chess champion when it defeated Russian grandmaster Gary Kasparov. And in 011, the computer giant’s question-answering system Watson won the quiz show ‘Jeopardy’, beating reigning champions Brad Rutter and Ken Jennings (Lewis, 2014).

The goal of AI in the early days was to recreate the working of the human mind in a machine (and hence the oxymoronic term): this goal has evolved over the years into a more attainable one, namely, that of making computer systems easier to use by humans whatever their training and understanding. The current goal of the AI community is to merge AI smoothly into existing software and systems, making them easier to use. Thus, expert systems, the most prodigious product of AI research, are mated with existing systems, like automatic teller machines, to make the latter more expert in allowing a withdrawal or an advance without the intervention of a human banker. Through the use of algorithms, AI can provide the necessary security and help to create a layered security system that enables a high-security layer within the systems. Through the use of advanced algorithms, AI helps identify potential threats and data breaches, while also providing the necessary provisions and solutions to avoid such loopholes. Often, the hosting server is bombarded with millions of requests on a day-to-day basis. The server, in turn, is required to open web pages that are being requested by the users. Due to the continuous inflow of requests, servers can often become unresponsive and end up slowing down in the long run. AI, as a service, can help optimize the host server to improve customer service whilst enhancing operations. As IT needs progress, artificial intelligence will be increasingly used to integrate IT staffing demands and provide seamless integration of the current business functions with technological functions.

From natural language generation and voice or image recognition to predictive analytics, machine learning, and driverless cars, AI systems have applications in many areas. These technologies are crucial to bring about innovation, while also providing new business opportunities and reshaping the way companies operate. Many modern AI applications are enabled through a sub-field of AI known as ‘machine learning’, which works without being explicitly programmed. ML uses algorithms and statistical models to perform a specific task without using explicit instructions, instead relying on patterns and inference, basically, it retains the ability to automatically learn and improve from sheer experience. For example, ML can read a text and decide if the author is making a complaint or an order. It can also translate large volumes of text in real-time. With time, AI research has enabled many technological advances like virtual agents and chatbots, suggestive web searches, targeted advertising, pattern recognition, predictive analytics, autonomous driving, automatic scheduling, and so on. Many businesses take up artificial intelligence (AI) technology to try to increase efficiency and improve customer experience. AI helps to improve customer services, automate workloads, optimize logistics, increase manufacturing output and efficiency, manage and analysis of data, and more. By deploying and implementing the right AI technology, a business can save time and money by optimizing routine processes, making faster decisions based on outputs from cognitive technologies, avoiding mistakes and human error, increase revenue by identifying and maximizing sales opportunities. AI is always around us. One might not notice it, but AI has a massive effect on our daily life. Many organizations use AI for business management, e-commerce, and marketing. It is effective in spam filters, smart email categorization, voice-to-text features, security surveillance, fraud detection, dynamic price optimization, smart searches and relevance features, content curation, customer segmentation, social semantics, and so on. Rather than serving as a replacement for human intelligence and ingenuity, artificial intelligence is generally seen as a supporting tool. Although artificial intelligence currently has a difficult time completing commonsense tasks in the real world, it is adept at processing and analyzing troves of data far more quickly than a human brain could. Artificial intelligence software can then return with synthesized courses of action and present them to the human user. In this way, humans can use artificial intelligence to help game out possible consequences of each action and streamline the decision-making process.

“Artificial intelligence is kind of the second coming of software. It’s a form of software that makes decisions on its own, that’s able to act even in situations not foreseen by the programmers. Artificial intelligence has a wider latitude of decision-making ability as opposed to traditional software”, – the CEO of machine learning company SparkCognition, Amir Husain.

Artificial intelligence is also progressively changing customer relationship management (CRM) systems. Software like Salesforce or Zoho requires heavy human intervention to remain up-to-date and accurate. But when you apply artificial intelligence to these platforms, a normal CRM system transforms into a self-updating, auto-correcting system that stays on top of your relationship management (Uzialko, 2019).

Results of a recent survey indicate that artificial intelligence can assist businesses in areas ranging from customer support to personalization.

As shown above, AI not only solves the workload problem but also contributes to revenues, investments, customer service, productivity, and efficiency. From better chatbots for customer service to data analytics to making predictive recommendations, deep learning and artificial intelligence in their many forms are seen by business leaders as an essential tool (Dern, 2019). In the present context, organizations are already using artificial intelligence to make practical decisions. For example, Coca-Cola released Cherry Sprite, which was derived from its AI product analysis. Furthermore, the soft drink company is planning to create its own virtual assistant to incorporate into its vending machines (Matskevich, 2019).

Innovations in information and technology are driving both globalization and the change of value creation toward services. They are most importantly challenging companies to adapt to their business model, organization, and corporate culture continuously and simultaneously to stay competitive and innovative. An analysis of IBM’s transformation reveals the opportunities and risks associated with innovations, and it also describes that mastering professional change management will become a core issue for many organizations and companies as they will not be accustomed to sudden changes. Disruptive technological innovations regularly force entire industries to adapt their business to new ways and processes which usually go against their established technology (Christensen, 1997, pp.125–131; Picot et al., 2008, p.7). Technological change can bring about advantages and opportunities for businesses as everything has its own set of pros and cons. Obviously, new technology can create new products and services, thereby creating entirely new markets and opening new aspects for a business. Moreover, improvements in technological products and processes can increase productivity and reduce costs, which can be considered as the main goal of any company. A disruptive technology is something that significantly alters the way businesses or entire industries used to operate and leads them in a risky yet promising direction. This is the reason why often companies fear changing the way they approach their business for fear of losing market share or becoming irrelevant. Recent examples of disruptive technologies include e-commerce, the Internet of Things, and ride-sharing to mention a lot. Artificial Intelligence itself can be considered as a disruptive technology as it forces significant changes in the ways and processes of an organization. A disruptive technology may take a longer duration of time to get developed compared to the existing technology and it also involves more risk than the existing technology, but it can achieve a faster penetration and replaces the established technology with prominent results so significant that it leaves a huge impact. Every company must carefully consider which disruptive innovations might influence their value chain and plan to respond to them or figure out whether they should use them in their business and if they can produce significant results with the new adaptation. They should understand their capability and limits to meet the outside dangers and opportunities of digitization. The invention of the Spinning Jenny 250 years ago, expanded the speed with which cotton could be transformed into yarn, impacting the textile business, which resulted in the era of the Industrial Revolution. The revelation of penicillin in the mid-1900s permitted already fatal contaminations to be dealt with, opening the door to modern surgical methods. In the mid-twentieth century, the creation of the transistor started a revolution that is still driving economic and social development (Agrawal, 2016).

A recent article by the online technology site Good Audience states that organizational leaders should disrupt themselves before technology disrupts their business, meaning that it is very crucial for a company to first realize the advantages as well as risks of innovation. Understanding the power of innovations such as information technology or artificial intelligence can increase business opportunities and growth, but can also lead to major losses if not utilized properly. Intuit’s Alex Chris states that disruptive technology can automate back-office tasks, make smarter business decisions, deliver highly personalized customer experiences, gain customer insights for product development, and employ a virtual assistant, be it a small company or a flourishing organization. Thus, no matter the size of the organization, technology always has both tangible and intangible benefits that will help a company progress actively and produce the results that customers demand. Although innovation can have some undesirable consequences, change is inevitable, and, in most cases, innovation creates positive change. Generally speaking, the main purpose of innovation is to improve people’s lives. When it comes to managing a business, innovation is the key to making any kind of progress.

References

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7. Matskevich, D. (2019). Council Post: Preparing Your Business for the Artificial Intelligence Revolution. [online] Forbes.com. Available at: https://www.forbes.com/sites/forbestechcouncil/2018/07/12/preparing-your-business-for-the-artificial-intelligence-revolution/#4aa23be7ac83 [Accessed 4 Aug. 2019].

i. Introduction

The beginning of auditing can be traced as far back as to ancient times, however the financial audit that we know of today is a relatively new practice and is constantly changing. As technology advances, we move further away from manual audit procedures and towards an automated audit. One of the most important technologies playing a role in automating the audit is artificial intelligence. Artificial intelligence is the development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, and decision-making (Lexico Dictionaries, 2019). It has already been applied in areas such as driverless cars, home-energy systems, investment portfolio management, and many more. The accounting industry is no exception, artificial intelligence is transforming the way we perform audits. This technology has the ability to process massive amounts of data and report on behavior, trends, and anomalies, making it easier for auditors to identify potential audit risks. The purpose of this report is to discuss how the deep learning function of artificial intelligence is being applied to traditional audit procedures. We will look at existing and developing technology at the Big Four accounting firms that has been created as a result of this new initiative, as well address some risks that this technology may introduce to the industry.

ii. Deep Learning

One major function of artificial intelligence is deep learning. Deep learning is a subdivision of machine learning and is concerned with algorithms that are inspired by the human brain (Brownlee, 2019). Deep learning creates artificial neural networks which can analyze unstructured data such as emails, social media, and conference calls which can provide crucial information for companies, along with auditors. When provided with sets of big data, deep learning technology can recognize patterns at a size and speed that would be impossible for humans. Deep learning technology has multiple hidden layers, the neural networks automatically “learn” from massive amounts of data, which can be structured or unstructured, received in the input layer (e.g., millions of images, years’ worth of speeches, tera-bytes of text files). From there it can recognize data patterns in more and more abstract representations as the data is processed and transmitted from one layer to the next and classify the data into predefined categories in the output layer (Sun, 2017).

Most big data us semi-structured or unstructured, which auditors use to make decisions and to freely explore the status of their clients’ products, services, and operations, reducing their dependence on the client for information. This information needs categorizing and labeling, however, auditors cannot do this manually due to the volume of big data sets. Deep learning allows artificial intelligence to mine and extract meaningful patterns from these big data sets which creates great value for audit decision-making and risk assessment. Auditors can now take larger samples and analyze larger sets of data, all while cutting down on tedious and repetitive audit processes and enhancing audit effectiveness and efficiency. Checking inventories, processing paperwork, reviewing contracts, and drafting audit reports are only some of the tasks that have become automated through deep learning that were traditionally done manually during an audit.

iii. Applications to the Audit

No matter what industry you are in, cognitive technologies fall into three main categories: product, process, or insight. AI systems that fall under the product category use the technology to increase the value of a product or service by making them more effective, convenient, safer, faster, etc. The process category consists of systems that have been automated that used to be done manually, these are typically internally focused and benefit the organization rather than the customers. Lastly, the insight category uses artificial intelligence to learn from information, draw conclusions, and generate insights for companies that can help reduce costs, improve efficiency, or enhance customer service (Schatsky, 2015). When applied to an audit, most of the technology being used falls under the process or insight category.

One of the most common applications of artificial intelligence to audit procedures is text analysis. Through a company’s operations, a high amount of written data/reports are generated which can be analyzed through text analysis. Transcripts of conference calls, press releases, MD&As, earnings announcements, business contracts, and social media messages are only some of the input textual data that artificial intelligence can analyze and create meaningful output for auditors to base decisions off. Output features of text analysis can be a sentiment, emotion, entity, topics, concepts, keywords, and more. The real value of text analysis comes from when it is applied to improvised content such as conference call transcripts, status updates, and Q&A’s rather than prepared content (press releases, presentations). This is because improvised content consists of more linguistic clues that reflect the cognitive process of the speaker which the deep learning technology can pick up on and point out areas of potential risk in the output layer. Textual analysis can be applied to audit procedures such as inspection of documents, confirmations with third parties, and analytical procedures.

Similar to text analysis is speech recognition, however, this application can actually analyze audio files of speech which may provide more insight than transcripts or text files. During an audit, managers, employees and other professionals who have relationships with the client (bankers, prior auditors, shareholders, etc.) are interviewed in order to gain information and identify areas of risk in the company. Artificial intelligence can analyze the recordings of these interviews and how the individuals answer the questions asked to them. This is similar to why text analysis is better with improvised textual data, because terms used while speaking can indicate dishonesty or uncertainty. Speech recognition does not only transcribe audio files, it can also translate one language to another, which is useful if the client being audited has operations overseas such as a foreign subsidiary. The auditor would not need to hire a translator or find someone who could speak the language, making the audit more efficient. The input data for speech analysis deep learning could be recordings of interviews (as mentioned before), phone calls, meetings, and presentations. The output features of speech recognition is similar to those of test analysis, however, it includes areas of possible deception. This application is relevant to audit procedures such as inquiry of personnel and management, as well as inspection of (audio) files.

Another function of deep learning that artificial intelligence can apply to an audit is image and video analysis. Deep learning systems have the ability to extract a series of predefined numerical attributes describing the content of the image or video, attach searchable tags accordingly, and save both the attributes and the images to the auditor’s output data (Sun, 2017). For example, this function can be useful to count and check the condition of inventory, as well as identify human faces and identify activity such as theft. The input data for the image and video analysis function includes inventory counting and other control activity, interviews, and videos taken in the office, warehouse, store, etc. Output features include objects, human faces (identities), concepts, scenes, and activities. This function is applicable to audit procedures such as observation, inquiry, and inspection of documentation.

iv. The Big Four’s Implementation of Artificial Intelligence

It is no surprise that artificial intelligence technology is very costly to implement and operate, which is part of the reason why the Big Four have been the first to have access to it. The Big Four accounting firms (Deloitte, PwC, EY, KPMG) are the largest accounting firms in the world, thus they have the resources to implement innovative and expensive technology to improve the quality of their work. The Big Four serve some of the largest companies in the world, so the data sets that they receive for audit, tax, or consulting work are often way too large for a group of people, yet along a single person, to comprehend without the help of technology. The Big Four have all introduced artificial intelligence to their work recently, embracing the new era of automation in accounting. However, not all the technology that has been implemented at these firms are the same, they have invested large amounts of money in order to come up with their own proprietary technology that is unique to their firm.

Deloitte has a number of artificial intelligence-enabled processes that have been introduced over the past decade. For example, their document-review platform has automated the process of reviewing and extracting all the relevant information from contracts, which reduces exhaustive and difficult human efforts. The application of this technology has reduced the time spent retrieving crucial information of documents such as contracts, invoices, financial statements, and meeting minutes by fifty percent or more (Faggella, 2019). However, Deloitte is not only using artificial intelligence for audit procedures. They recently created a system for employees called “Vitals” which collects information from internal systems to see when workers may need a break. “Employees can see a complete picture of how many hours they are working… how much of that time they’re spending away from home, how many flights they have taken in the past week, and when they last took PTO. It also allows employees to share energy levels with their coach.” (Kohll, 2019). This tool helps identify employees that may be at risk of burnout before they crash, which is just another benefit of artificial intelligence in the workplace.

Another Big Four firm, EY, has created its own proprietary Robotic Process Automation (RPA) system to help with audits. This uses robots that mimic human actions and automate repetitive tasks across multiple business applications without altering existing infrastructure and systems. In an audit, this technology can be used to account audit requests, perform data analytics, and assess internal controls (Robotic Process Automation, 2016).

EY has also launched artificial intelligence that uses computer vision to enable airborne drones to monitor inventory during the auditing process (Faggella, 2019). The use of drones will allow more data to be captured and analyzed during the audit, focusing the auditor’s attention more on risk areas rather than having to manually count inventory. The drones can observe and physically examine evidence while the auditors can apply their minds to issues that are more strategy or judgement-oriented. This initiative has not been set in practice yet, it is still in research and development, however it is a promising technology that gives us insight to the future of auditing.

PwC has some unique audit technology as well. In October 2017, the International Accounting Bulletin named PwC’s “GL.ai” ‘Audit Innovation of the Year’. This technology examines every uploaded transaction, user, amount, and account to find unusual transactions in the general ledger, which could indicate error or fraud, without bias or variability (PricewaterhouseCoopers, 2019). The interesting thing about this technology is that it uses PwC’s experience within its algorithms, so it actually gets smarter the more they use it. GL.ai increases the efficiency of the audit and provides the auditors with comfort that they are focuses on areas of true risk.

Lastly, KPMG is no short comer when it comes to artificial intelligence, they have built their own portfolio of AI tools which they refer to as KPMG Ignite. Some examples of what lies in KPMG’s AI portfolio are a call center analytics engine, AI anomalous event predicting tool, and document compliance assessment engine. The call center analytics engine uses artificial intelligence to create a transcript of customer calls, and can identify keywords, sentiment, and predict future trends. The AI anomalous event predicting tool also uses an AI model that can predict future business events. And the document compliance assessment engine uses artificial intelligence to read the documentation and extract appropriate information, similar to technology found at some of the other Big Four firms (Faggella, 2019).

v. Risks of Artificial Intelligence

There are obvious benefits to artificial intelligence, as stated above, however with new technology often comes new risks. The main goal of artificial intelligence is to mitigate risk, however, does it bring any new risks with its implementation? Yes, there have been risks identified that stem from artificial intelligence, however, if you can identify the risks beforehand then they can be managed. One risk associated with machine learning is algorithmic bias. The algorithms used in artificial intelligence technology identify patterns in data, and if those patterns or data reflect an existing bias, the bias is likely to be amplified and the results will support the existing patterns of discrimination. Artificial intelligence systems are very complex so they are prone to programmatic errors, and if these errors are not identified then the results can be misleading which can have serious consequences if relied on too heavily. Also, AI systems are commonly the target of cyber-attacks, so they must be well secured or they run the risk of allowing hackers access to personal data or confidential information. One last major risk of artificial intelligence is that it is an increasingly new concept, although the idea has been around since the 1950s, recent advancements are extremely remarkable and unforeseen. That being said, there is little legislation governing this technology, but there will be soon, and systems that are currently able to analyze these large sets of data may not comply with future regulations (Boillet, 2018).

vi. Conclusion

Artificial intelligence may seem like the wave of the future, however, for many industries, it is already here. The accounting industry is not the first to implement machine learning and it will not be the last. Although we are mostly seeing deep learning technology being implemented at large accounting firms who have the resources to do so, there is no doubt that it will make its way into firms of all sizes relatively soon.

Artificial intelligence is becoming exponentially more advanced and will be a major part of anybody’s day-to-day whether they work in accounting or any other industry. The audit process will be streamlined due to this technology, allowing for more efficient and reliable audits, which will not only benefit the accounting firms, but also businesses and society as a whole.

Introduction

Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. Generation AI will rely on artificial intelligence to assist them through all the milestones in their lives. While many people think of AI as a futuristic technology, AI is something we encounter today in ways that some people may not even realize. For example, when you use an internet search engine, the search terms and predictive text are powered by AI.

This new generation will be much more aware of their AI interactions. They will converse with digital assistants, learn new skills from robots and be driven around in cars that are controlled by AI. Generation AI will become more independent as they grow up, thanks to assistance from AI, which will actually force them to become interdependent on the technology. Discover more about Generation AI and how AI will play a major role in milestones at each stage of life.

Artificial intelligence was founded as an academic discipline in 1956, and in the years since has experienced several waves of optimism, followed by disappointment and the loss of funding (known as an ‘AI winter”), followed by new approaches, success and renewed funding. For most of its history, AI research has been divided into subfields that often fail to communicate with each other. These sub-fields are based on technical considerations, such as particular goals (e.g. ‘robotics’ or ‘machine learning), the use of particular tools (‘logic “or artificial neural networks), or deep philosophical differences. Subfields have also been based on social factors (particular institutions or the work of particular researchers).

Artificial intelligence designed on

Python is widely used for artificial intelligence, with packages for a number of applications including General AI, Machine Learning, Natural Language Processing and Neural Networks. Haskell is also a very good programming language for AI. The language’s features enable a compositional way of expressing the algorithms.

Future of Artificial intelligence

The machine can react or act like us only if they have plenty of knowledge of Human Beings. It would be impossible for us to live without computer systems. Cars, ATM machines, and everything which works automatically have a computer system inbuilt. Soon Artificial intelligence machines can do all those things that we can barely do. An important advantage of Artificial intelligence is that every work will be perfectly and precisely done. Artificial intelligence drone can replace humans in doing risky task, things like going to areas where terrorist activity is at its highest.

Although people are still in fear that it will dominate the world and replace humans. There will no job left for human to feed their family. Anything that needs human touch will be done by machines or robots. Most harmful technology of computer science is the Autonomous weapon systems; it can harm us in lot because it will do whatever a careless person wanted it to do.

Existing systems on Artificial Intelligence

Siri

Everyone is familiar with Apple’s personal assistant, Siri. She’s the friendly voice-activated computer that we interact with on a daily basis. She helps us find information, gives us directions, add events to our calendars, and helps us send messages and so on. Siri is a pseudo-intelligent digital personal assistant. She uses machine-learning technology to get smarter and better able to predict and understand our natural-language questions and requests.

Alexa

Alexa’s rise to become the smart home’s hub, has been somewhat meteoric. When Amazon first introduced Alexa, it took much of the world by storm. However, its usefulness and its uncanny ability to decipher speech from anywhere in the room has made it a revolutionary product that can help us scour the web for information, shop, schedule appointments, set alarms and a million other things, but also help power our smart homes and be a conduit for those that might have limited mobility.

Netflix

Netflix provides highly accurate predictive technology based on customers’ reactions to films. It analyses billions of records to suggest films that you might like based on your previous reactions and choices of films. This tech is getting smarter and smarter by the year as the dataset grows. However, the tech’s only drawback is that most small-labeled movies go unnoticed while big-named movies grow and balloon on the platform.

Google AI

Google AI can process commands from a user, make phone calls silently in the background and handle natural conversation to request information or book appointments. Some critics are reserved or opposed to the directions Google is taking with AI. Because the Assistant software does not declare itself as a digital assistant, critics say it deceives answering parties who may not wish to speak to an AI. Privacy is also a concern with Google’s AI updates. For example, because Assistant no longer requires users to say “OK, Google” to alert the Assistant before issuing commands, critics argue that this change could enable constant data mining.

Proposed or upcoming AI systems

1. Natural language generation

Natural language generation is an AI sub-discipline that converts data into text, enabling computers to communicate ideas with perfect accuracy. It is used in customer service to generate reports and market summaries and is offered by companies like Attivio, Automated Insights, Cambridge Semantics, Digital Reasoning, Lucid works, Narrative Science, SAS.

3. Virtual agents

A virtual agent is nothing more than a computer agent or program capable of interacting with humans. The most common example of this kind of technology are chatbots. Virtual agents are currently being used for customer service and support and as smart home managers. Some of the companies that provide virtual agents include Amazon, Apple, Artificial Solutions, Assist AI, Creative Virtual, Google, IBM, iSOFT, OpenAI and Microsoft.

4. Machine learning platforms

These days, computers can also easily learn, and they can be incredibly intelligent! Machine (ML) is a sub-discipline of computer science and a branch of AI. Its goal is to develop techniques that allow computers to learn. By providing algorithms,APIs (application programming interface), development and training tools, big data, applications and other machines, ML platforms are gaining more and more traction every day. They are currently mainly being used for prediction and classification. Some of the companies selling ML platforms include Amazon, Fractal Analytics, Google, and Microsoft.

5. AI-Optimized Hardware

AI technology makes hardware much friendlier. Through new graphics and central processing units and processing devices specifically designed and structured to execute AI-oriented tasks You can get access to this technology through Alluviate, Cray, Google, IBM, Intel, and Nvidia.

Basic Types of Artificial Intelligence

Narrow AI: Sometimes referred to as ‘Weak AI,’ this kind of artificial intelligence operates within a limited context and is a simulation of human intelligence. Narrow AI is often focused on performing a single task extremely well and while these machines may seem intelligent, they are operating under far more constraints and limitations than even the most basic human intelligence.

Few examples of Narrow AI include:

• Google search
• Image recognition software
• Siri, Alexa and other personal assistants
• Self-driving cars
• IBM’s Watson

Artificial General Intelligence (AGI): AGI, sometimes referred to as ‘Strong AI,’ is the kind of artificial intelligence we see in the movies, like the robots from Westworld or Data from Star Trek: The Next Generation. AGI is a machine with general intelligence and, much like a human being, it can apply that intelligence to solve any problem.

a). Advantages of Artificial Intelligence

1. Error reduction.
2. Difficult exploration (use in Data mining).
3. Daily application.
4. Digital assistant.
5. Repetitive jobs.

b). Disadvantages of Artificial intelligence

1. High cost.
2. No Replicating Humans.
3. No Improvement with Experience.
4. No Original creativity.
5. Unemployment among Humans.

Conclusion

In its short existence, AI has increased understanding of the nature of intelligence and provided an impressive array of applications in a wide range of areas. It has sharpened my understanding of human reasoning, and of the nature of intelligence in general. At the same time, it has revealed the complexity of modelling human reasoning providing new areas and rich challenges for the future.

Abstract

The field of artificial intelligence (AI) has shown an upward trend of growth in the 21st century (from 2000 to 2015). The evolution in AI has advanced the development of human society in our own time, with dramatic revolutions shaped by both theories and techniques. However, the multidisciplinary and fast-growing features make AI a field in which it is difficult to be well understood. In this paper, we study the evolution of AI at the beginning of the 21st century using publication metadata extracted from 9 top-tier journals and 12 top-tier conferences of this discipline. We find that the area is in sustainable development and its impact continues to grow. From the perspective of reference behaviour, the decrease in self-references indicates that the AI is becoming more and more open-minded. The influential papers/researchers/institutions we identified outline landmarks in the development of this field. Last but not least, we explore the inner structure in terms of topics’ evolution over time. We have quantified the temporal trends at the topic level and discovered the inner connection among these topics. These findings provide deep insights into the current scientific innovations, as well as shed light on funding policies.

Introduction

The term ‘Artificial Intelligence was first coined in 1956 by prominent computer and cognitive scientist John McCarthy, then a young Assistant Professor of Mathematics at Dartmouth College, when he invited a group of academics from various disciplines including, but not limited to, language simulation, neuron nets, and complexity theory, to a conference entitled the ‘Dartmouth Summer Research Project on Artificial Intelligence’ which is widely considered to be the founding event of artificial intelligence as a field. At that time, the researchers came together to clarify and develop the concepts around “thinking machines” which up to this point had been quite divergent. McCarthy is said to have picked the name artificial intelligence for its neutrality; to avoid highlighting one of the tracks being pursued at the time for the field of “thinking machines” that included cybernetics, automata theory and complex information processing. The proposal for the conference stated, “The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.”

Today, modern dictionary definitions credit Artificial Intelligence as a sub-field of computer science focussing on how machines might imitate human intelligence — being human-like, rather than becoming human. Merriam-Webster provides the following definition: “a branch of computer science dealing with the simulation of intelligent behaviour in computers.”

The term ‘Aritifical Intelligence’ has been overused in recent years to denote artificial general intelligence (AGI) which refers to self-aware computer programs, capable of real cognition. Nevertheless, most AI systems, for the foreseeable future, will be what computer scientists call “Narrow AI,” meaning that they will be designed to perform one cognition task well, rather than “think for themselves”.

While most of the major technology companies haven’t published a strict dictionary-type definition for Artificial Intelligence, one can extrapolate how they define the importance of AI by reviewing their key areas of research. Machine learning and deep learning are a priority for Google AI and it’s tools to “create smarter, more useful technology and help as many people as possible;” from translations and healthcare, to making smartphones even smarter. Facebook AI Research is committed to “bringing the world closer together by advancing artificial intelligence” whose fields of research include Computer Vision, Conversational AI, Natural Language Processing, and, Human & Machine Intelligence.

IBM’s three main areas of focus include AI Engineering, building scalable AI models and tools; AI Tech, where the core capabilities of AI such as natural language processing, speech and image recognition and reasoning are explored and AI Science, where expanding the frontiers of AI is the focus.

In 2016, several industry leaders in Artificial Intelligence including Amazon, Apple, DeepMind, Google, IBM and Microsoft joined together to create Partnership on AI to Benefit People and Society to study and formulate best practices on AI technologies, to advance the public’s understanding of AI, and to serve as an open platform for discussion and engagement about AI and its influences on people and society. Those working with AI today make it a priority to define the field for the problems it will solve and the benefits the technology can have for society. It’s no longer a primary objective for most to create AI techniques that operates like a human brains, but to use its unique capabilities to enhance our world.

Algorithms use a large amount of data to adjust their internal structure such that, when new data is presented, it gets categorised in accordance with the previous data given. This is called “learning” from the data, rather than operating according to the categorisation instructions written strictly in the code.Imagine that we want to write a program which can tell cars apart from trucks. In the traditional programming approach, we would try and write a program which looks for specific, indicative features, like bigger wheels or a longer body. We would have to write code specifically defining how these features look and where they should be found in a photo. To write such a program and make it work reliably is very difficult, likely yielding both false positives and false negatives, to a point where it may not be usable in the end at all.

This is where Artificial Intelligence become very useful; once an AI algorithm is trained, it can be shown many images of cars and trucks, clearly labeled as such, and will adjust its internal structure to detect features relevant to the successful classification of the pictures instead of relying on static, prescribed feature definitions.

A core concept regarding AI systems is that their decisions are only as good as their data. Humans are not great at dealing with large volumes of data, and the sheer volume of data available to us sometimes prevents us from using it directly. For example, an algorithm with a million data inputs will outperform the same algorithm with only 10,000 data inputs. With this knowledge in tow, preparing and cleaning data is something that will become more prevalent in the process of applying artificial intelligence techniques.

This step is often the most labour-intensive part of building an AI system, as most companies do not have the data ready in the correct format(s). It can take much longer to build the right data infrastructure and prepare the data to be used than actually constructing the model to run the data. Machine learning will soon allow software applications to synthesise vast amounts of engineering knowledge in seconds. Architects and engineering professionals, by contrast, take years acquiring the skills and experience needed to design buildings, leaving them unable to compete.

Then again architects, regulators, and engineers have a way of increasing the amount of work delivered/energy it takes to produce documents. AI likely will be specialised at first to automate menial tasks, coordinate, and perform quality control. Many tools are starting to display potential in these areas, as AI improves these areas of the field and others will loose billable hours per project.

AEC software is highly monopolised and Revit, for example, has allowed you to run a team with less staff than you might’ve needed 20 years ago, but you pay upwards of £2,200 per individual in software subscription fees per year, so instead of labour cost you have very high software cost paid to companies with market capture.

I think that alongside maybe rendering software is the best example of automation currently, and it hasn’t delivered much savings in the end just higher quantity or quality and a transfer of cost to software. Any construction professional that realises what a regulatory quagmire the industry operates in knows that AI will never be able to fully integrate this context, it is a shifting mosaic that would first require complete incorporation – even building codes.

More broadly, computational design is in practice at every large and medium, as well as some small, architecture firms around the world. We use it to do heavy lifting of analysing and optimising our work. And today, combined with BIM, we have the ability to do more with less people. This trend is not going away. We should all get more savvy with technology as it will be the best assistant to our work. Those who can’t will be forced to retire, or leave the profession like those who still wanted to use pencil on drawing boards after CAD was well established.

In the data-driven future of project management, construction project managers will be augmented by artificial intelligence that can highlight project risks, determine the optimal allocation of resources and automate project management tasks. According to Gartner, by 2020, AI will generate 2.3 million jobs, exceeding the 1.8 million that it will remove—generating $2.9 trillion in business value by 2021. Google’s CEO goes so far as to say that “AI is one of the most important things humanity is working on. It is more profound than […] electricity or fire.” With applications of artificial intelligence already disrupting industries ranging from finance to healthcare, construction project managers who can grasp this opportunity must understand how AI project management is distinct and how they can best prepare for the changing landscape.

Human coorperation with intelligent machnies will define the next era of history; using a machine which is connected through the Internet, that can work as a collaborative, creative partner.

Pattern Recognition, Reinforcement Learning, and Machine Learning

Artificial intelligence (AI) is ubiquitous. Whether we are consciously aware of it or unknowingly using it, AI is present at work, at home and in our everyday transactions. From our productivity in the office to the route we take home to the products we purchase and even the music we listen to, AI is influencing many of our decisions. Those decisions are still ours to make, but soon enough the decisions will be made by AI-enabled systems without waiting for the final approval from us.

Machine Learning (ML) is a subset field of artificial intelligence that uses statistical techniques to give computers the ability to learn from data without being explicitly programmed.. Humans learn from experience, so ML is basically learning from experience, where experience is data; taking input from the world (e.g. text in books, camera images from a car, or a complex mathematical function), and then has an output – a decision. ML is transforming many industries and applciations, especially in areas where there’s a lot of data, and predicting outcomes can have a big payoff: finance, sports, and medicine come to mind. AI and ML have been used interchangeably by many companies in recent years due to the success of some machine learning methods in the field of AI. To be clear, machine learning denotes a program’s ability to learn, while artificial intelligence encompasses learning along with other functions.

Deep Learning and Neural Networks

Deep learning is part of a broader family of machine learning methods based on learning data representations, as opposed to classical task-specific algorithms. Most modern deep learning models are based on an artificial neural network, although they can use various other methods. A neural network is a virtual, much simpler, version of the human brain. The brain is the most complex system in the human body; with 85 billion neurons, each of which fire non-stop, receiving, processing, and sending information. Neural Networks are nowhere near as complex, but that’s the goal. Instead of neurons, we have nodes. The more the nodes are exposed to, the more they learn. Neural networks are biologically inspired, connected mathematical structures which enable AI systems to learn from data presented to them.

There are multiple types of neural networks, each accompanied by its own specific use cases and level of complexity. You might see terms like CNN (convolutional neural network) or RNN (recurrent neural network) used to describe different types of neural network architecture. To better understand how they look and function, here is a great 3D visualization of how neural networks “look” while they are active.

Artificial General Intelligence and Conclusion

Gene Roddenberry would argue Karl Marx was a fool. Money isn’t needed if society has a machine that can not only provide all that is needed, it can build its own replacement parts. And we already have the beginnings of other machines that enable fast travel and communication of all forms including video across great distances, cultural barriers and language. We’re going to realise something like what was imagined in the Star Trek universe, and it will likely look a lot different. The effect is the same. AI will transform society. Or destroy it. It’s a tool, and the choice is collectively ours.

Machine learning will soon allow software applications to synthesise vast amounts of engineering knowledge in seconds. Architects and engineering professionals, by contrast, take years acquiring the skills and experience needed to design buildings, leaving them unable to compete. AI likely will be specialised at first to automate menial tasks, coordinate, and perform quality control. Many tools are starting to display potential in these areas, as AI improves these areas of the field and others will loose billable hours per project.

AEC software is highly monopolised and Revit, for example, has allowed you to run a team with less staff than you might’ve needed 20 years ago, but you pay upwards of £2,200 per individual in software subscription fees per year, so instead of labour cost you have very high software cost paid to companies with market capture. I think that alongside maybe rendering software is the best example of automation currently, and it hasn’t delivered much savings in the end just higher quantity or quality and a transfer of cost to software.

Any construction professional that realises what a regulatory quagmire the industry operates in knows that AI will never be able to fully integrate this context, it is a shifting mosaic that would first require complete incorporation – even building codes.

More broadly, computational design is in practice at every large and medium, as well as some small, architecture firms around the world; used to do heavy lifting of analysing and optimising work and today, combined with BIM, we have the ability to do more with less people. This trend is not going away. We should all get more savvy with technology as it will be the best assistant to our work. Those who can’t will be forced to retire, or leave the profession like those who still wanted to use pencil on drawing boards after CAD was well established. Human coorperation with intelligent machnies will define the next era of history; using a machine which is connected through the Internet, that can work as a collaborative, creative partner.

In the data-driven future of project management, construction project managers will be augmented by artificial intelligence that can highlight project risks, determine the optimal allocation of resources and automate project management tasks.

Google’s CEO goes so far as to say that “AI is one of the most important things humanity is working on. It is more profound than […] electricity or fire.”

With applications of artificial intelligence already disrupting industries ranging from finance to healthcare, construction project managers who can grasp this opportunity must understand how AI project management is distinct and how they can best prepare for the changing landscape.

In the data-driven future of project management, construction project managers will be augmented by artificial intelligence that can highlight project risks, determine the optimal allocation of resources and automate project management tasks.

Construction is a $10 trillion industry and accounts for approx. 10% of worldwide employment. This industry consumes 25-40% of all raw materials so in other words it is ENORMOUS! But even though we are such a large industry, we are among the least digitalised. In general, we spend less than 1% of turnover on IT, much less than most other industries. This is unfortunately not because we are so awesome that we have no need for change. The average construction worker only spends 30% of his or her time on-site actually building something and the rework rate is 7-15%. So in other words there are a big pile of money just waiting to be taken for those who can improve efficiency.

No matter if we build houses or huge civil engineering projects, a lot of the processes are repetitive. And when we have repetitive activities, we can start consolidating the data and making assumptions based on facts and not gut feelings. Learnings can then be shared across the company and the industry which would contribute to radical efficiency improvements.

This data is our hard-earned knowledge, data-based, that can be shared between people involved in the project. The more knowledge we have amassed, the more likely the project will be delivered on budget and time. The client will know what it will cost to build as he has experience from previous projects, the advisors will know exactly how to create a buildable design as they know which elements to put together in the BIM model and the contractor will be able to tell exactly how long it will take to build as they have done it several times before and have captured the data. This is where data and machine learning/AI is going to help a lot.

If we model a project, machine learning can tell us if we miss something that we normally use to make a project like this. And if we want to start projects in country X in month Y, we can already take weather conditions into account as we know how the weather normally is (these data points have been available for the past 50 years). So the “system” tells you that if you want to start your construction phase at this time, there is a X% chance of rain, snow etc. So now we go from having great experience (single person knowledge) to actually sharing information based on knowledge across the company and projects we have been involved in – a massive amplification of joint knowledge.

How the application of AI can impact the construction project management, and in particular the BIM project, is still unknown. Designers, architects, and engineers find more questions than answers. What is clear is that the processes for simulation of the building and BIM produce so much data that the majority of the organizations do not know what to do with them.

Hence, it is fundamental to understand the amount of data that is produced in the process of drawing, BIM modelling, construction, and building maintenance. The architects, engineers, and other construction professionals are not using all of this data for their own benefit, or that of their customers. The data stream generated by construction is not usually used, or at least it is not used in the proportion of the possibilities provided by AI.

The tendency in a sector not accustomed to the standardized methods and processes, is to move on to the next project without considering how to use the collected data for improvement. The expert in construction technology Nicholas Klokhol explains the possible implications of AI and Big Data applied to the context of BIM in the construction sector, and its main current problem: once the architectural project is built, 95% of the generated data is either deleted or not properly archived, hampering future analyses and exploitation.

When the construction process begins, plans must be made and this is where AI is first introduced. Autonomous equipment is considered as AI as it is aware of its surroundings and is capable of navigation without human input. In the planning stages, AI machinery can survey a proposed construction site and gather enough information to create 3D maps, blueprints and construction plans. Before AI was introduced, this was a process that would take a while to complete – weeks, in fact – but now, this can be accomplished within one day. This helps to save firms both time and money in the form of labour.

A job that was regularly carried out by physical workers, AI is now able to control and manage a project. For example, workers can input sick days, vacancies and sudden departures into a data system and it will adapt the project accordingly. The AI will understand that the task must be moved to another employee and will do so on its own accord.

AI is also good for communication, as this type of system can help direct engineers with how to carry out specific projects and better their performance. For example, if engineers were working on a proposed new bridge, AI systems would be able to advise and present a case for how the bridge should be constructed. This would be based on past projects over the last 50 years, as well as verifying pre-existing blueprints for the design and implementation stages of the project. By having this information to hand, engineers can make crucial decisions based on evidence that they may not have previously had at their disposal. Construction sites can be dauting, with huge structures and risky heights, but with the introduction of autonomous machines – workers can now be outside of the vehicle. Using sensors and GPS, the vehicle can calculate the safest route.