Category: Artificial intelligence

Format: text file (either word or pdf)
Length: 1-2 pages (but not more!)
Task:
C

Format: text file (either word or pdf)
Length: 1-2 pages (but not more!)
Task:
Choose the direction: GenAI, NLP, CV, ASR.
Choose a specific task inside the chosen direction (example: direction is GenAI and the task is Image Generation)
Find 2 solutions: classical one (before Deep Learning era) and the new one (Neural Net-based), briefly describe them (including links/sites)
Make a rigorous comparison of two solutions, emphasize their pros and cons
Conclude your research with your takeaway
DO NOT COPY OR USE CHAT GPT WILL CHECK!!!!!!

Will Robots Replace Human Employment? Essay

The robotics revolution has started! Since robotics have been manufactured, the efficiency and productivity in working conditions are growing rapidly. They formed a huge leap in our lives as they became part of many workplaces. Moreover, they can implement a wide variety of tasks successfully in a record time compared to human performance. Also, they are playing a pivotal role in helping laborers in their work to be faster and more effective. “When automation or computerization makes some steps in a work process more reliable, cheaper or faster, this increases the value of the remaining human links in the production chain” (Autor, 2015). However, some employees believe that robotics will have an unfavorable impact on the workplace, all because they thought that their jobs are in danger. For instance, now accountants, laborers of construction, farmers, housekeepers, and chauffeurs are worrying about losing their jobs due to robotics “Up to 20 million manufacturing jobs around the world could be replaced by robots by 2030” (Oxford Economics, 2019). So, this will decrease laborer wages and increase the rate of unemployment in the future. From another perspective, robotics may be the main source for real disasters that are waiting to face the world in the future. This essay will discuss the impact of robotics on the workforce and how employees need to cooperate with them in fields such as accounting, workers of construction and farming.

There are so many high-risk fields that still depend on manual laborers as construction workers, who consistently facing hard and perilous working conditions. As a result, the numbers of wounds and death are constantly incrementing. “317 million nonfatal professional injuries and 321,000 occupational fatalities occur all around the world each year, so, that 151 workers sustain a work-related accident every 15 seconds” (Abdalla, Spenser Apramian, Linda and Mark, 2013). However, replacing them with robots will increase safety and permit errands to be finished precisely. For example, in China, they started to use the robot-welder, which is manufactured to pick up a big pile of boards and take them into an elevator to increase safety, instead of hiring workers, because if a laborer is injured while working particularly on this job, it could be a serious issue. Robots also will be cost-efficient in the long run as they can work for long periods with the same efficiency without taking rest. On the contrary, laborers need time to take a break, and they get bored due to the same task that they do every day, so it became a routine for them, and this affects the quality of their work.

Recently, several researches have shown that robots are superior in accounting functions rather than humans, and this affects accountants who are concerned about where they are going to fit if this occurs in the future. “Software programs attempting to replicate human experts, behavior, and expertise, store human knowledge and experience and transform it into rules thus trying to solve accounting problems and perform some accounting tasks” (Stancheva, 2006). Robots not only can be used in banking fields as they will arrange meetings, collect and count money but also, they will be quite useful in business fields to produce a higher quality of output for companies. As well as, they help to utilize the data and understand information faster and accurately so the human error will be reduced and data can be easily checked later on. Moreover, an individual’s positions will turn out to be less stressful, so employee’s tasks can be more centered around significant works that need greater liability. However, replacing accountants who are working as risk-takers with robots cannot be ideal because this type of position needs creativity and from its qualification to be able to take specific decisions when there is an issue, and robots will struggle to achieve this. From another perspective, accountants and businesses have to find the best way in which workers can interact with robots effectively. “Businesses should review their organization’s activities to assess where potential value from automation is highest and create a strategic plan that includes both capital investment and reskilling workers” (McKinsey Global Institute, 2017).

Agrarian robots which related to agriculture became one of the key patterns that will profoundly impact farming and may replace farmers too. “There is a three-in-four chance that artificial intelligence (AI) will replace farmers” (Oxford University, 2019), because they can perform many tasks, especially through planting, fertilizing corn, weed control, and scouting operations. They also can take care of farmland, reap plants, and expanding crop yields. From another perspective, they can help to assist ranchers in dealing with the issue of a waning labor force and permit them to work more proficiently. Needless to say, some countries started to prepare for the TerraSentia robot, that can be wandered autonomously through fields and analyze plants with advanced sensors to identify which is strongest and healthiest and announce back to human officials while inspecting crops. Besides, a French designer created the Wall-Ye robot, that assists with pruning and collecting grapes at vineyards, utilizing infrared sensors and scissor-hands.

On the other hand, robots have their darker side that has to be taken into consideration. For instance, preparing for the design, and intelligent software system requires many types of researches, which is quite expensive and needs an extended time to complete. Also, they are complicated in their maintenance. Moreover, you cannot assemble human knowledge in a machine because it is an endowment of nature. “Humans will always need for effective and embodied interactions with other humans, which can never be replaced by robots” (Lin, 2016). As a result, they cannot interact with unexpected situations. No matter how insightful a machine may become, it can never replace a human. Also, workers can improve and develop their working through age and experiences. However, this cannot be said about robots as they are machines that cannot have creativity or imagination. From another perspective, robots require manual labor to control them because if they become out of control, they may cause colossal disasters.

To sum it all up, it is better to manufacture robots to assist workers in achieving their tasks successfully rather than replacing them. Furthermore, as indicated by most specialists, the sort of assignments that can most effectively be robotized is those that have a serious level of redundancy in either physical tasks or data processing. Besides, with each employment taken over by the machines, there will be an equivalent number of chances for responsibilities to be finished by individuals. “Rather than robots replacing medical or accounting professionals, the latter need to work hand in hand with robots, to continue raising the value of work within their profession” (Lee, Loke, 2007). Moreover, people will wind up in harmonious connections with robots after they comprehend how to interact with them in an ideal manner, because robots perform real tasks that require human intelligence. For having a productive future, numerous specialists recommended that people and robots need to work with each other as robots need to do tasks that can be mechanized while people need to take care of the responsibilities that require an individual or imaginative touch. Furthermore, the government needs to create real solutions to survive those workers who may lose their jobs by providing educational courses and aptitudes retraining for existing and future specialists.

References

1. Abdalla, S., Spenser S., Linda F., and Mark R. (2013). Injury Prevention and Environmental Health. (3rd ed.). https://www.ncbi.nlm.nih.gov/books/NBK525209
2. Autor, D., (2015). Why Are There Still So Many Jobs? The History and Future of Workplace Automation. Journal of Economic Perspectives Volume 29, Number 3. https://ifr.org/downloads/papers/IFR_The_Impact_of_Robots_on_Employment_Positioning_Paper_updated_version_2018.pdf
3. Boston Consulting Group. (2015). The Robotics Revolution. https://ifr.org/downloads/papers/IFR_The_Impact_of_Robots_on_Employment_Positioning_Paper.pdf
4. Fook, L., Loke, H. (2017). WILL ROBOTS REPLACE ACCOUNTANTS? Journal.isca.org.sg. https://journal.isca.org.sg/2017/07/25/will-robots-replace-accountants/pugpig_index.html
5. Lin, P. (2016). Relationships With Robots: Good Or Bad For Humans? Forbes. https://www.forbes.com/sites/patricklin/2016/02/01/relationships-with-robots-good-or-bad-for-humans/?sh=5fb0843c7adc
6. Matthews, K. (2019). 5 Ways Robotics Will Disrupt The Construction Industry In 2019. Robotics Business Review. https://www.roboticsbusinessreview.com/news/5-ways-robotics-will-disrupt-construction-industry-in-2019
7. Oxford Economics (2019). Robots ‘to replace up to 20 million factory jobs’ by 2030. BBC News https://www.bbc.com/news/business-48760799
8. Schilperoort, L. (2020). 10 Common Jobs To Be Replaced By Robots Within 5 Years – Job Tradition. Jobtradition.com. https://www.jobtradition.com/10-common-jobs-to-be-replaced-by-robots-within-5-years
9. Stancheva, E. (2018). HOW ARTIFICIAL INTELLIGENCE IS CHALLENGING ACCOUNTING PROFESSION. https://ifr.org/downloads/papers/IFR_The_Impact_of_Robot_on_Employment_Positioning_Paper_updated_version_2018.pdf

Essay on Artificial Intelligence Economy

In recent years, society has witnessed robots and machines replacing many jobs that were once conducted by humans. The questions on everyone’s minds are when will the development and advancement of artificial intelligence stop, will robots have the potential to replace every aspect of humans’ lives, and what jobs will be left? Artificial intelligence is making its way into numerous industries and as a result, disrupting the workplace environment. Industries are currently realizing the economic benefits of Artificial intelligence such as the potential to improve productivity, efficiency, and accuracy across many job disciplines- but is this entirely beneficial? As technology continues to advance, robots are becoming more intelligent and learn to perform tasks more efficiently than humans in the workplace. As a result, many people fear that the rise and advancement of the technology associated with Artificial intelligence will lead to robots and machines replacing human workers, disrupting human interactions in the process as well as them viewing the technology as a threat rather than a tool to benefit the workplace. The main question then becomes, whether society is looking at a future where artificial intelligence will become more valuable than humans in the workplace and overall beneficial for the economy.

The contribution of the technology associated with artificial intelligence in the business industry has a significant impact on the global economy. According to the September 2018 report conducted by the McKinsey Global Institute on the impact of Artificial Intelligence on the global economy, artificial intelligence has the potential to gradually add 16 percent or roughly $13 trillion by the year 2030 to the current global economic output which is an annual contribution to the productivity growth of about 1.2 percent between 2018 and 2030(Bughin, 2018, p. 1). The report further showed that approximately 70% of businesses have the potential to incorporate the use of artificial intelligence technology in some way, shape, or form by the year 2030(Bughin, 2018, p. 2). As the business industry grows, artificial intelligence could have the potential to positively affect the global economy from different perspectives such as innovating products and services and increasing the activity in the global economy(Bughin, 2018).

Cami Rosso, a journalist who writes about science, innovation, and leadership explores these positive effects of artificial intelligence on the economy by introducing the unusual concept of automated psychological services powered by the technology associated with artificial intelligence(Rosso, 2018). Rosso discusses how automated psychological services will most likely be in the form of a technologically innovative service provided through mobile smartphone apps. According to Rosso “The potential advantages of a smartphone-based psychology wellness app include lower barriers to adoption, cost, access, availability, confidentiality, privacy, adherence and a lack of perceived stigma” (Rosso, 2018, p.2). Rosso highlights the fact that the cost of using psychological wellness apps is much lower and affordable than visiting a psychologist due to a session potentially costing a couple of hundred dollars while not even factoring in the costs related to transportation and the waiting room (Rosso, 2018). Rosso further supports her argument by incorporating research conducted on the cost of smartphone apps from Satista, a German database company which states that “The average price to purchase an app in the Apple App store on September 2018, is less than a dollar (89 cents) (Rosso, 2018, p.2). This shows that consumer demand is more likely to be driven by the availability and affordability of personalized and higher-quality artificial intelligence products and services(Rosso, 2018).

Based on the research conducted by the McKinsey Global Institute and Rosso, both make a fair argument for artificial intelligence playing an increasingly important role in our global economy in many different ways. From their perspective, they see artificial intelligence as an engine of productivity and economic growth. They both further argue how artificial intelligence can efficiently generate new products and services as well as industries and markets which therefore increases consumer demand. While these may be positive aspects from a business perspective, it is very clear why this is worrisome for those working in jobs that are at risk of displacement from artificial intelligence.

On the contrary to this debate, the disruptive effects of artificial intelligence have the potential to influence income distribution, employee wages, and overall economic inequality which Tracy Staedter, a science writer, editor, writing coach, and consultant, explores in her article “How Artificial Intelligence Will Transform Our Economic Future.” According to Staedter, “the rising demand for high-skilled workers capable of using artificial intelligence technology could increase their wages meanwhile mid-skilled and lower-skilled workers’ wages may decrease because higher-skilled workers are not only more productive but are also capable of completing more tasks thanks to artificial intelligence”(Staedter, 2019, p.1). Therefore the changes in demand for labor could potentially worsen income distribution and overall affect employees’ wages. Staedter further argues that depending on the pace of advancing technology associated with artificial intelligence, if society advances artificial intelligence at a faster rate then it will create more undesirable effects due to economic market imperfections. Furthermore, Staedter states that “as more artificial intelligence machines replace routine labor, the more productivity and overall income growth will rise and the more sharply inequality will increase”(Staedter, 2019, p.1). Based on Staedter’s article it is arguable that society would be more wealthy due to incorporating artificial intelligence in the workplace, but for many workers at risk of losing their jobs due to artificial intelligence, this technological change would only strengthen economic inequalities(Staedter, 2019).

The concern for artificial intelligence replacing many jobs that are normally conducted by humans is becoming increasingly more urgent as the latest artificial intelligence breakthroughs such as self-driving cars, robots and many more attract society’s attention. As the technology associated with artificial intelligence advances, some believe that it will consistently and inevitably take over large organizations in the workforce and will immensely increase the employment rate and create social turmoil.

Similarly, Cami Rosso also explores in her article how artificial intelligence will create massive job displacement. The majority of jobs that are at risk of being displaced shortly are process-driven jobs. These are jobs that can easily be automated such as transportation, customer service, and manufacturing. More specifically, according to Rosso “the jobs more at risk include bookkeepers, tellers, telemarketers, legal secretaries, bill and account collectors, and even postal workers” (Rosso, 2018, p.1). Overall the technology associated with artificial intelligence can easily do these types of jobs more efficiently and faster than humans. As a result, society must realize that artificial intelligence will play a role in everyone’s jobs shortly- whether that means making their jobs easier or unfortunately replacing them.

The possibility of artificial intelligence competing with humans will not only lead to a fight for jobs on an economic level but potentially even intrude on human relationships in a way that an artificial intelligence companion will focus only on its owner’s needs meanwhile a human relationship thrives between the exchange of favors. In the article “Robots will probably help care for you when you’re old,” Corinne Purtill, a behavioral science, health, and technology senior reporter for Quartz explores the issues associated with robotic caregivers and companions to the elderly. Purtill describes her conversations with Robert and Linda Sparrow, professors of philosophy at Australia’s Monash University. According to Robert and Linda Sparrow “The demands that our friends even pets make on us are unpredictable, sometimes unexpected and often inconvenient,” “This is an essential part of what makes relationships with other people or animals, interesting, involving and rewarding.” “Any reduction of what is often already minimal human contact would, in our view, be indefensible” (Purtil, 2018, p.15). It is therefore arguable that the relationships between artificial intelligence and humans pose an existential threat that could potentially lead to an end of human interaction as we know it.

Likewise, in the article “Why these friendly robots can’t be good friends to our kids,” Sherry Turkle, a professor of Social Studies and Science and Technology at MIT researched the interactions between children and robots in which robots are acting as companions by imitating human interactions, evoking emotions and overall replacing the roles that are usually conducted by other human beings. Based on Turkle’s research conducted on the effects of sociable robots on children, her findings conclude that sociable robots exploit human vulnerability in particular “exercising in emotional deception” (Turkle, 2017, p.5). By allowing children to interact and build fake relationships with these sociable robots, Turkle argues that they should not be expected to learn how to interact and create real, mutual relationships with other human beings(Turkle, 2017). Although Turkle explores the interactions between children and robots rather than adults and robots, the idea can be related to the workplace environment in which these intelligent robots have the potential to manipulate human workers when collaborating and overall change the nature of human employment.

Similar to the idea of Turkle’s concern with robots not being able to understand humans’ emotional lives because “they have not been born and don’t know pain or mortality or fear”(p.5); robots can’t be friendly or emotional like human workers and as a result, there will not be a pleasant working environment filled with employee relationships. Since robots are not capable of communicating like humans, the relationships and bonds between robots and employees will remain low. Furthermore, robots in the workplace will create a negative impact on employees’ relationships and attitudes towards their jobs and employers. When the human-dominated workforce is replaced by robots, the faith that the employees had developed in the organization for which they work will be undermined as employees will begin to think that their employers are only concerned about the efficiency and productivity of the work rather than the relationships and friendly working environment.

On the other side of the debate, however, there is a strong argument for incorporating artificial intelligence in the workplace, which will come through the expansion of employees’ skills rather than the substitution of jobs. There is the potential for human workers and robots to work together flawlessly by complementing each other’s skills. Robots would also have the potential to realize when human workers are having difficulty and will be more than ready to step in and assist the worker if their job is beyond their capabilities. The best job performance will be achieved through the collaboration between humans and robots in which a good example of this would be robotic caregivers.

Lousie Aronson, a practicing geriatrician and Professor of Medicine at the University of California, San Francisco, argues in her article “The Future of Robot Caregivers” that the quality of her patients’ everyday lives would be substantially better by incorporating the use of a robotic companion as well as a caregiver rather than the alternative of her patients being left alone. Aronson specifically states that her patients’ everyday lives consist mainly of “loneliness and disability” (p.1) because “we do not have anywhere near enough human caregivers for the growing number of older Americans” (Aronson, 2014, p.3). Aronson further argues that robots could help solve the employment shortage of caregivers by strategically incorporating them into human caregiving practices(Aronson, 2014). Aronson acknowledges the fact that “caregiving is hard work”(p.1), which is a full-time tedious job that can put a strain on a caregiver’s health, job, and relationships. Ensuring that an elderly patient is properly fed, bathed, dressed, and medicated can be “awkwardly intimate and physically and emotionally exhausting”(p. 1). Additionally, Aronson imagines a robot that is capable of working throughout the day while taking care of the patient’s responsibilities such as chores, and ensuring the patient can move around safely. Aronson further imagines a robotic caregiver who can interact with the patient by even telling jokes and reading aloud. As a result, the desire for a caregiver to hand over some of these responsibilities involved to a robot becomes more crucial.

Artificial intelligence has the potential to remove the stress of tedious and manual work for employees by making things automatic while allowing employees to keep their jobs and apply new skills, working in collaboration with robots. But artificial intelligence can take a turn for the worse and make the employee’s fears of losing their jobs a reality. The future of artificial intelligence isn’t exactly clear but it will have an impact on the workplace and economy- whether that impact is positive or negative, society must have to wait and see what the future holds.

Although I do not have an affirmative stance on this issue, based on the research conducted by the various authors, I can conclude that as artificial intelligence transforms the workplace and economy it will also transform society as a whole. However, I would argue that it is too soon to come to a definitive answer on whether society is looking at a future where artificial intelligence will become more valuable than humans in the workplace and overall beneficial for the economy because we have not seen the evolution of artificial intelligence in the long-term- whether it’s leading humanity towards making the world a better place or leading to a disaster. As humans, we always embrace new technologies that change our everyday lives, but it is important to remember that the kind of technological changes we are embracing will create positive and negative outcomes that will affect humanity for generations to come.

The Big Four’s Implementation of Artificial Intelligence

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.

Existing Systems and Future of Artificial Intelligence

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.

Construction Project Management Using Artificial Intelligence (AI)

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.

Artificial Intelligence In Construction Project Management

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.

Challenges And Opportunities Of Business In Artificial Intelligence

From numerous years technical advancements are the major drivers of monetary development. The most vital universally useful technology of our period is Artificial Intelligence. AI is very nearly turning into a basic piece of each business foundation, settling on it crucial for company leaders to see how this technology can, and will, upset traditional business model. The impact of AI is increasing in manufacturing, retailing, transportation, finance, health care, law, advertising, insurance, entertainment, education. This paper offers a point of view on how these advances are affecting business and society, and presents a system for seeing how Artificial Intelligence can convey an incentive for the association and industry. This paper talks about how AI is spurring workforce change, data management, privacy, cross-entity collaboration, and generating new ethical challenges for business. This paper underscores on the requirement for business to change its core process and business models to take advantage of AI. And it tries to enable managers to comprehend and follow up on the gigantic opportunity from the blend of human and machine knowledge. The paper also focus on challenges that should be tended to while grasping AI.

INTRODUCTION

AI technology is evolving faster than expected and it is proving to be most effective in producing dramatic results in business today. AI is on the verge of becoming a critical part of every business infrastructure, making it vital for company decision-makers to understand how this technology can, and will, disrupt traditional business models. Adopting to artificial intelligence can make business cost-effective , more productive by cutting down the time we spend doing basic administrative tasks and better customer engagement. Artificial Intelligence has the potential to streamline business processes, improve customer services and leverage sensor-driven data for marketing and advertising. Today, there are numerous applications of artificial intelligence in the consumer and business spaces, from Apple’s Siri to Google’s DeepMind. Siri, for example, uses natural language processing (NLP) to interpret voice commands and respond accordingly. Google’s DeepMind, on the other hand, uses deep learning.

ARTIFICIAL INTELLIGENCE AND ITS TECHNOLOGIES

AI is defined as an area of computer science that emphasizes the creation of intelligent machines that work and react like humans.[3] The term ‘artificial intelligence’ commonly applies to devices or applications capable of carrying out specific tasks in human ways, by mimicking cognitive functions such as learning reasoning problem-solving, visual perception and language-understanding. Artificial Intelligence (AI) is defined as intelligence exhibited by an artificial entity to solve complex problems and such a system is generally assumed to be a computer or machine. Accenture defines AI as information systems and applications that can sense, comprehend and act which captured the attention of Business Executives along with technologists and research scientists. AI can be broadly classified into Applied AI & Generalized AI. Applied AI includes systems designed to intelligently carry out a single task, eg move a driverless vehicle, or trade stocks and shares. Generalized AI includes systems or devices that can theoretically handle any task, as they carry enough intelligence to find solutions to unfamiliar problems. [1] The important AI Technologies are Natural Language Generation Speech Recognition Virtual Agents Machine Learning Platforms AI-optimized Hardware: Decision Management Deep Learning Platforms Biometrics Robotic Process Automation Text Analytics and NLP:

• Machine Learning (ML) – uses computer algorithms based on mathematical models using probability to make assumptions and can make predictions about similar data sets.
• Cognitive Computing – builds upon ML using large data sets with the goal to simulate human thought process and predictive decisions.
• Deep Learning – builds on ML using neural nets to make predictive analysis. The use of neural nets is what is differentiating Deep Learning from Cognitive Computing right now. Deep Learning is also helping improve image and speech recognition.
• Predictive application programming interfaces (APIs) – A predictive API basically uses AI to provide a predictive output (from a standardized set of outputs), when you have data sets.
• Natural Language Processing (NLP) – programming computers to understand written and spoken language just like humans, along with reasoning and context, and finally produce speech and writing. Many machine learning companies use NLP for training on unstructured data.
• Image Recognition – recognizing picture and objects as humans, as well patterns in visually represented data, which may not be apparent.
• Speech Recognition – converting spoken language to data sets that can be processed by NLP.

CURRENT IMPACT OF AI IN BUSINESS

AI is capable of automating business intelligence and analytics processes, providing a holistic end-to-end solution. Below are a few examples of how AI is Impacting Business being used to improve efficiency:

Improved customer services: Apps are developed for mobile users to facilitate easy transaction to customers. Analyst firm Gartner predicted that 85% of all customer interactions will take place without a human agent by 2020. Chatbot is a Light weight AI program designed to provide communication with users the way a human assistant would

Workload automation and predictive maintenance: With Internet of Things and AI solutions, companies can reduce operating costs, increase productivity and eventually create a knowledge-based economy;

Effective data management and analytics: By the end of this year, there will be billion connected gadgets worldwide. As more companies start using IoT solutions for business purposes, the amount of data generated increases Artificial Intelligence can perform effective data management and also analyze data to obtain meaningful insights into asset and personnel management.

Evolution of marketing and advertising: New technologies have changed the way marketers have been working for decades. With Artificial Intelligence, marketers can automate a great share of routine tasks, acquire important data and devote more time to their core responsibilities— that is, increasing revenues and customer satisfaction.

Artificial Intelligence in HR: It is widely believed that the role of managers is becoming a key determinant for enterprises’ competitiveness in today’s knowledge economy era. The main purposes of the study are to discuss the appointment of managers in enterprises through fuzzy neural network, to construct a new model for evaluation of managerial talent, and accordingly to develop a decision support system in human resource selection.

APPLICATIONS OF AI USED IN BUSINESS

The Most Important Applications of AI are

• Commuting: It is observed that most of the commuters waste lot of time in traffic. Google’s AI-Powered Predictions is an application that predicts the traffic and gives right information to the commuter. Ridesharing Apps Like Uber and Ola use AI to calculate fare by estimating time, distance and traffic.
• Email: Email is one of the means used for exchanging messages. Spam Filters and Smart Email Categorization are AI based application programs that filter and categorize the mails in the Inbox
• Grading and Assessment: Applications designed for grading and assessment of student performance also use AI. Plagiarism Checkers is an application used for Plagiarism checking on research articles.
• Banking/Personal Finance: Everyday financial transactions are also heavily reliant on AI. Banking/Personal Finance sector . Most large banks offer the ability to deposit checks through a smartphone app ‘Mobile Check Deposits’, eliminating a need for customers to physically deliver a check to the bank. AI is used to create systems that learn what types of transactions are fraudulent. FICO is AI Application used by many banks for making credit decisions, and in determining the specific risk assessment for individual customers.
• Social Networking: It is found that Social networking sites uses AI in recognising faces and also in personalizing news feeds. Facebook uses AI to recognize faces. Facebook also uses AI to personalize our newsfeed and ensure us in seeing posts that interest us. Facebook announced a new AI initiative: DeepText, a text understanding engine that, the company claims “can understand with near-human accuracy the textual content of several thousand posts per second, spanning more than 20 languages. Pinterest uses computer vision, an application of AI where computers are taught to “see”, in order to automatically identify objects in images (or “pins”) and then recommend visually similar pins.
• Online Shopping: online shopping sites use AI for Search relevant product and also to get recommendations on nearing products. AI is also used in securing online transactions.

LITERATURE SURVEY

Artificial Intelligence is a branch of Science which deals with helping machines, finds solutions to complex problems in a more human-like fashion. This generally involves borrowing characteristics from human intelligence, and applying them as algorithms in a computer friendly way. [6] Artificial Intelligence is defined as the scientific studies that computers can think, do, interact and act in many fields as a human that people are good at[4] Artificial intelligence describes the work processes of machines that would require intelligence if performed by humans. The term ‘artificial intelligence’ thus means ‘investigating intelligent problem-solving behaviour and creating intelligent computer systems’.[1] AI has a broad discipline in today’s world that promises to simulate numerous inherent human skills such as automatic programming, case-based reasoning, neural networks, decision-making, expert systems, natural language processing, pattern recognition, speech recognition and market competition due to technological advancement etc.[1]

The exponential rise of this technology can be attributed to 3 key factors:

• The emergence of smarter, modern-day algorithms
• Easy access to a huge volume of data because of increased mobile usage, connected devices and sensors
• Cloud enabling cheaper and easier access to large scale compute power and bigger storage[2]

AI technologies bring more complex data analysis features to existing applications. Enterprises that utilize AI- enhanced applications are expected to become more diverse, as the needs for the ability to analyze data across multiple variables, fraud detection and customer relationship management emerge as key business drivers to gain competitive advantage.[1]

The developments in technologies, different sciences and disciplines, by the help of convergence among them, would support works to achieve these goals and obviously more discoveries would be seen in coming years that will cause disruptive changes in business, life and global economy[4] Dr. Vishal Sikka, CEO, Infosys said that ‘ As managers and employers, as citizens in our communities, we bear the great responsibility that comes with transformation, to ensure we are driving a purposeful approach to AI. He also said that ‘We must now think beyond how we’ve been approaching our education, to recast it as a holistic, continuous and lifelong process of learning — one in which problem-finding is as important as problem solving, and digital literacy is taken as seriously as language literacy.'[3] With new developments in technology, and the emergence of buzzwords like cognitive computing, machine learning and VR, merging human interactions with AI seems to be a possible solution for managing customers’ needs.[2]

When combined and designed with the consumer in mind, AI technologies can deliver solutions that drive customer loyalty, engagement, consumption, and satisfaction. And the faster today’s companies wake up to the real potential of AI, the better. [2] Google CEO Sundar Pichai predicted that we will move from a mobile-first to an AI-first world. Data is the oil that powers AI.[2]

More than four out of five companies view AI to be essential, and nearly half see it as a transformative technology. Only a few are making bold investments today, which may trigger a competitive imbalance tomorrow.[5] The biggest AI is helping employees do better work, and companies do work they couldn’t do before. The technology is seen producing many new jobs but automating jobs as well.

Elon Musk, Tesla Motors CEO said that ‘I’m increasingly inclined to think that there should be some regulatory oversight, maybe at the national and international level, just to make sure that we don’t do something very foolish … just keep an eye on what’s going on with artificial intelligence. I think there is potentially a dangerous outcome there.” Bill Gates , Microsoft co-founder said that ‘I am in the camp that is concerned about super intelligence. First the machines will do a lot of jobs for us and not be super intelligent. That should be positive if we manage it well. A few decades after that though the intelligence is strong enough to be a concern. I agree with Elon Musk and some others on this and don’t understand why some people are not concerned.” However, as with all adoption of advancing technology, challenges do exist. The use of AI presents an extraordinary mix of technical and ethical hurdles. Organizations using AI to some extent find themselves struggling to really get the most they can out of it.[3]

FINDINGS AND RECOMMENDATIONS

FINDINGS

Although AI is already in use in thousands of companies around the world, most big opportunities have not yet been tapped. The bottleneck now is in management, implementation, and business imagination of what AI is already doing and how quickly it is improving. When viewed AI through the lens of a futures market the following opportunities and challenges were identified

OPPORTUNITIES

• Organization that embraced AI can a leader in market as AI is going to be an integral part of business and world in near future.
• To improve productivity by building Successful Virtual Assistants which perform repetitive and administrative tasks.
• To use Human Skill for intelligent tasks rather than repetitive and administrative tasks.
• To integrate and redesign Business Process that leverage potentials of AI .
• To Build Applications that generate business value and also helps in cost reduction
• To increase customer base and also provide better customer service.

CHALLENGES

• Failure to include artificial intelligence as part of Business Strategy by Managers
• The gap between ambition and execution of AI is large at most companies.
• Workload automation and predictive maintenance is required .
• Talent availability is a serious inhibiter of AI growth.
• Legacy technology is hindrance for implementing AI applications .
• Unavailability of AI infrastructure in the country. Currently Organizations are using AI Infrastructure provided by other governments that might be risky.
• No Proper Law or standards that govern or monitor the activities of organization involved with AI.

RECOMMENDATIONS

• Companies need to understand the importance of AI and its impact on business.
• To include artificial intelligence as part of Business Strategy .
• Introduce changes into Traditional Educations System such that students acquire skills to adapt to changing technological enhancements.
• Government need to take required initiatives in providing required infrastructure that motivates organization to embrace AI.
• Incorporating AI into Government Policy.
• Enforcement of Standards and Law by the Governments is required.

CONCLUSION

Artificial intelligence is moving rapidly from relevancy in technologically niche areas to impacting every industry in the world. There is an urgent need for changing traditional education Systems System such that students acquire skills to adapt to changing technological enhancements. Using uncontrolled AI for certain business functions may cause regulatory and ethical issues that could lead to liability. So in conclusion, the usage, development and governance of artificial intelligence must be spearheaded in a sensitive way at all times.

REFERENCES

1. IBA Global Employment Institute Artificial Intelligence and Robotics and Their Impact on the Workplace GIAN JYOTI E-JOURNAL, Volume 1, Issue 2 (Jan – Mar 2012) ISSN 2250-348X
2. Artificial Intelligence: Business Paradigm Reimagined BY SAPIENTRAZORFISH PUBLISHED: 18, Nov 2017 By Prashant Mehta, Group Vice President, Global Service Line Lead – Systems Integration & Data, SapientRazorfish
3. Amplifying Human Potential Towards Purposeful Artificial Intelligence, Report 2016 , Infosys, https://www.infosys.com/aimaturity/Documents/amplifying-human-potential-CEO-report.pdf
4. The Impacts of Robotics, Artificial Intelligence On Business and Economics Cüneyt Dirican © 2015 The Authors. Published by Elsevier Ltd. Procedia – Social and Behavioral Sciences 195 ( 2015 ) 564 – 573
5. Getting Smarter by the Day: How AI is Elevating the Performance of Global Companies TCS Global Trend Study : Part 1 http://sites.tcs.com/artificial-intelligence/wp-content/uploads/TCS-GTS-how-AI-elevating-performance- global-companies.pdf
6. Artificial Intelligence and Robotics Chetan Sakharam Tirgul, Mangesh Raghunath Naik International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 5, Issue 6, June 2016 ISSN: 2278 – 1323.

An Exploration On Artificial Intelligence Application: From Safety, Smart Security & Intelligence

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

1. D. Crevier, ‘AI: The tumultuous history of the search for artificial intelligence,’ Basic Books, London and New York, 1994.
2. M. Flasinski, ‘History of artificial intelligence,’ Introduction to the History of Computing, Springer-Verlag New York Inc, New York, 2016.
3. Executive office of the president of the United States, ‘The National Artificial Intelligence Research and Development Strategic Plan,’ Washington, October 2016.
4. Pannu, M. T. Student, ‘Artificial intelligence and its application in different areas,’ International Journal of Engineering and Innovative Technology (UEIT), Vol. 4(10), April 20lS, pp. 79-84.
5. S. S. Sikchi, S. Sikchi, and M. Ali, ‘Artificial intelligence in medical diagnosis,’ International Journal of Applied Engineering Research, Vol. 7, Jan. 2012, pp. IS39-1543.
6. X. F. Hui, J. Sun, ‘An application of support vector machine to companies’ Financial Distress Prediction,’ Third International Conference on Modeling Decisions for Artificial Intelligence (MDAI 2006), Tarragona, Spain, April 2006, pp. 274-282, doi: 10.1007/11681960 27.
7. M. Kandlhofer, G. Steinbauer, S. Hirschmugl-Gaisch and P. Huber, ‘Artificial intelligence and computer science in education: From kindergarten to university,’ 2016 IEEE Frontiers in Education Conference (FIE2016), Eire, PA, Oct. 2016, pp. 1-9.
8. P. Patil. ‘Artificial intelligence in cyber security,’ International Journal of Research in Computer Applications and Robotics, VoI.4(S), 2016, pp. 1-5.

Artificial Intelligence In Marketing

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].