Daily Archives: March 31, 2020

Theres no question that Artificially Intelligence (AI) and Automation will change the way we live; the question isnt if, its how and when. In this post, Ill be exploring both optimistic and pessimistic views of how artificial intelligence and automation will impact our future workforce.

Technology-driven societal changes, like what were experiencing with AI and automation, always engender concern and fearand for good reason. A two-year study from McKinsey Global Institute suggests that by 2030, intelligent agents and robots could replace as much as 30 percent of the worlds current human labor. McKinsey suggests that, in terms of scale, the automation revolution could rival the move away from agricultural labor during the 1900s in the United States and Europe, and more recently, the explosion of the Chinese labor economy.

McKinsey reckons that, depending upon various adoption scenarios,automation will displace between 400 and 800 million jobs by 2030, requiring as many as 375 million people to switch job categories entirely. How could such a shift not cause fear and concern, especially for the worlds vulnerable countries and populations?

The Brookings Institution suggests that even if automation only reaches the 38 percent means of most forecasts, some Western democracies are likely to resort to authoritarian policies to stave off civil chaos, much like they did during the Great Depression. Brookings writes, The United States would look like Syria or Iraq, with armed bands of young men with few employment prospects other than war, violence, or theft. With frightening yet authoritative predictions like those, its no wonder AI and automation keeps many of us up at night.

The Luddites were textiles workers who protested against automation, eventually attacking and burning factories because, they feared that unskilled machine operators were robbing them of their livelihood. The Luddite movement occurred all the way back in 1811, so concerns about job losses or job displacements due to automation are far from new.

When fear or concern is raised about the potential impact of artificial intelligence and automation on our workforce, a typical response is thus to point to the past; the same concerns are raised time and again and prove unfounded.

In 1961, President Kennedy said, the major challenge of the sixties is to maintain full employment at a time when automation is replacing men. In the 1980s, the advent of personal computers spurred computerphobia with many fearing computers would replace them.

So what happened?

Despite these fears and concerns, every technological shift has ended up creating more jobs than were destroyed. When particular tasks are automated, becoming cheaper and faster, you need more human workers to do the other functions in the process that havent been automated.

During the Industrial Revolution more and more tasks in the weaving process were automated, prompting workers to focus on the things machines could not do, such as operating a machine, and then tending multiple machines to keep them running smoothly. This caused output to grow explosively. In America during the 19th century the amount of coarse cloth a single weaver could produce in an hour increased by a factor of 50, and the amount of labour required per yard of cloth fell by 98%. This made cloth cheaper and increased demand for it, which in turn created more jobs for weavers: their numbers quadrupled between 1830 and 1900. In other words, technology gradually changed the nature of the weavers job, and the skills required to do it, rather than replacing it altogether. The Economist, Automation and Anxiety

Looking back on history, it seems reasonable to conclude that fears and concerns regarding AI and automation are understandable but ultimately unwarranted. Technological change may eliminate specific jobs, but it has always created more in the process.

Beyond net job creation, there are other reasons to be optimistic about the impact of artificial intelligence and automation.

Simply put, jobs that robots can replace are not good jobs in the first place. As humans, we climb up the rungs of drudgery physically tasking or mind-numbing jobs to jobs that use what got us to the top of the food chain, our brains. The Wall Street Journal, The Robots Are Coming. Welcome Them.

By eliminating the tedium, AI and automation can free us to pursue careers that give us a greater sense of meaning and well-being. Careers that challenge us, instill a sense of progress, provide us with autonomy, and make us feel like we belong; all research-backed attributes of a satisfying job.

And at a higher level, AI and automation will also help to eliminate disease and world poverty. Already, AI is driving great advances in medicine and healthcare with better disease prevention, higher accuracy diagnosis, and more effective treatment and cures. When it comes to eliminating world poverty, one of the biggest barriers is identifying where help is needed most. By applying AI analysis to data from satellite images, this barrier can be surmounted, focusing aid most effectively.

I am all for optimism. But as much as Id like to believe all of the above, this bright outlook on the future relies on seemingly shaky premises. Namely:

As explored earlier, a common response to fears and concerns over the impact of artificial intelligence and automation is to point to the past. However, this approach only works if the future behaves similarly. There are many things that are different now than in the past, and these factors give us good reason to believe that the future will play out differently.

In the past, technological disruption of one industry didnt necessarily mean the disruption of another. Lets take car manufacturing as an example; a robot in automobile manufacturing can drive big gains in productivity and efficiency, but that same robot would be useless trying to manufacture anything other than a car. The underlying technology of the robot might be adapted, but at best that still only addresses manufacturing

AI is different because it can be applied to virtually any industry. When you develop AI that can understand language, recognize patterns, and problem solve, disruption isnt contained. Imagine creating an AI that can diagnose disease and handle medications, address lawsuits, and write articles like this one. No need to imagine:AI is already doing those exact things.

Another important distinction between now and the past is the speed of technological progress. Technological progress doesnt advance linearly, it advances exponentially. Consider Moores Law: the number of transistors on an integrated circuit doubles roughly every two years.

In the words of University of Colorado physics professor Albert Allen Bartlett, The greatest shortcoming of the human race is our inability to understand the exponential function. We drastically underestimate what happens when a value keeps doubling.

What do you get when technological progress is accelerating and AI can do jobs across a range of industries? An accelerating pace of job destruction.

Theres no economic law that says You will always create enough jobs or the balance will always be even, its possible for a technology to dramatically favour one group and to hurt another group, and the net of that might be that you have fewer jobs Erik Brynjolfsson, Director of the MIT Initiative on the Digital Economy

In the past, yes, more jobs were created than were destroyed by technology. Workers were able to reskill and move laterally into other industries instead. But the past isnt always an accurate predictor of the future. We cant complacently sit back and think that everything is going to be ok.

Which brings us to another critical issue

Lets pretend for a second that the past actually will be a good predictor of the future; jobs will be eliminated but more jobs will be created to replace them. This brings up an absolutely critical question, what kinds of jobs are being created and what kinds of jobs are being destroyed?

Low- and high-skilled jobs have so far been less vulnerable to automation. The low-skilled jobs categories that are considered to have the best prospects over the next decade including food service, janitorial work, gardening, home health, childcare, and security are generally physical jobs, and require face-to-face interaction. At some point robots will be able to fulfill these roles, but theres little incentive to roboticize these tasks at the moment, as theres a large supply of humans who are willing to do them for low wages. Slate, Will robots steal your job?

Blue-collar and white-collar jobs will be eliminatedbasically, anything that requires middle-skills (meaning that it requires some training, but not much). This leaves low-skill jobs, as described above, and high-skill jobs that require high levels of training and education.

There will assuredly be an increasing number of jobs related to programming, robotics, engineering, etc.. After all, these skills will be needed to improve and maintain the AI and automation being used around us.

But will the people who lost their middle-skilled jobs be able to move into these high-skill roles instead? Certainly not without significant training and education. What about moving into low-skill jobs? Well, the number of these jobs is unlikely to increase, particularly because the middle-class loses jobs and stops spending money on food service, gardening, home health, etc.

The transition could be very painful. Its no secret that rising unemployment has a negative impact on society; less volunteerism, higher crime, and drug abuse are all correlated. A period of high unemployment, in which tens of millions of people are incapable of getting a job because they simply dont have the necessary skills, will be our reality if we dont adequately prepare.

So how do we prepare? At the minimum, by overhauling our entire education system and providing means for people to re-skill.

To transition from 90% of the American population farming to just 2% during the first industrial revolution, it took the mass introduction of primary education to equip people with the necessary skills to work. The problem is that were still using an education system that is geared for the industrial age. The three Rs (reading, writing, arithmetic) were once the important skills to learn to succeed in the workforce. Now, those are the skills quickly being overtaken by AI.

For a fascinating look at our current education system and its faults, check out this video from Sir Ken Robinson:

In addition to transforming our whole education system, we should also accept that learning doesnt end with formal schooling. The exponential acceleration ofdigital transformation means that learning must be a lifelong pursuit, constantly re-skilling to meet an ever-changing world.

Making huge changes to our education system, providing means for people to re-skill, and encouraging lifelong learning can help mitigate the pain of the transition, but is that enough?

When I originally wrote this article a couple of years ago, I believed firmly that 99% of all jobs would be eliminated. Now, Im not so sure. Here was my argument at the time:

[The claim that 99% of all jobs will be eliminated] may seem bold, and yet its all but certain. All you need are two premises:

The first premise shouldnt be at all controversial. The only reason to think that we would permanently stop progress, of any kind, is some extinction-level event that wipes out humanity, in which case this debate is irrelevant. Excluding such a disaster, technological progress will continue on an exponential curve. And it doesnt matter how fast that progress is; all that matters is that it will continue.The incentives for people, companies, and governments are too great to think otherwise.

The second premise will be controversial, but notice that I said human intelligence. I didnt say consciousness or what it means to be human. That human intelligence arises from physical processes seems easy to demonstrate: if we affect the physical processes of the brain we can observe clear changes in intelligence. Though a gloomy example, its clear that poking holes in a persons brain results in changes to their intelligence. A well-placed poke in someones Brocas area and voilthat person cant process speech.

With these two premises in hand, we can conclude the following: we will build machines that have human-level intelligence and higher. Its inevitable.

We already know that machines are better than humans at physical tasks, they can move faster, more precisely, and lift greater loads. When these machines are also as intelligent as us, there will be almost nothing they cant door cant learn to do quickly. Therefore, 99% of jobs will eventually be eliminated.

But that doesnt mean well be redundant. Well still need leaders (unless we give ourselves over to robot overlords) and our arts, music, etc., may remain solely human pursuits too. As for just about everything else? Machines will do itand do it better.

But whos going to maintain the machines? The machines.But whos going to improve the machines? The machines.

Assuming they could eventually learn 99% of what we do, surely theyll be capable of maintaining and improving themselves more precisely and efficiently than we ever could.

The above argument is sound, but the conclusion that 99% of all jobs will be eliminated I believe over-focused on our current conception of a job. As I pointed out above, theres no guarantee that the future will play out like the past. After continuing to reflect and learn over the past few years, I now think theres good reason to believe that while 99% of all current jobs might be eliminated, there will still be plenty for humans to do (which is really what we care about, isnt it?).

The one thing that humans can do that robots cant (at least for a long while) is to decide what it is that humans want to do. This is not a trivial semantic trick; our desires are inspired by our previous inventions, making this a circular question. The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future, by Kevin Kelly

Perhaps another way of looking at the above quote is this: a few years ago I read the book Emotional Intelligence, and was shocked to discover just how essential emotions are to decision making. Not just important, essential. People who had experienced brain damage to the emotional centers of their brains were absolutely incapable of making even the smallest decisions. This is because, when faced with a number of choices, they could think of logical reasons for doing or not doing any of them but had no emotional push/pull to choose.

So while AI and automation may eliminate the need for humans to do any of thedoing, we will still need humans to determine what to do. And because everything that we do and everything that we build sparks new desires and shows us new possibilities, this job will never be eliminated.

If you had predicted in the early 19th century that almost all jobs would be eliminated, and you defined jobs as agricultural work, you would have been right. In the same way, I believe that what we think of as jobs today will almost certainly be eliminated too. But this does not mean that there will be no jobs at all, the job will instead shift to determining, what do we want to do? And then working with our AI and machines to make our desires a reality.

Is this overly optimistic? I dont think so. Either way, theres no question that the impact of artificial intelligence will be great and its critical that we invest in the education and infrastructure needed to support people as many current jobs are eliminated and we transition to this new future.

Originally published on April 1, 2017. Updated on January 29, 2020.

Here is the original post:

The Impact of Artificial Intelligence - Widespread Job Losses

Although AI is undoubtedly multifaceted, there are specific types of artificial intelligence under which extended categories fall.

What are the four types of artificial intelligence?

There are a plethora of terms and definitions in AI that can make it difficult to navigate the difference between categories, subsets, or types of artificial intelligence and no, theyre not all the same. Some subsets of AI include machine learning, big data, and natural language processing (NLP); however, this article covers the four main types of artificial intelligence: reactive machines, limited memory, theory of mind, and self-awareness.

These four types of artificial intelligence comprise smaller aspects of the general realm of AI.

Reactive machines are the most basic type of AI system. This means that they cannot form memories or use past experiences to influence present-made decisions; they can only react to currently existing situations hence reactive. An existing form of a reactive machine is Deep Blue, a chess-playing supercomputer created by IBM in the mid-1980s.

Deep Blue was created to play chess against a human competitor with intent to defeat the competitor. It was programmed with the ability to identify a chess board and its pieces while understanding the pieces functions. Deep Blue could make predictions about what moves it should make and the moves its opponent might make, thus having an enhanced ability to predict, select, and win. In a series of matches played between 1996 and 1997, Deep Blue defeated Russian chess grandmaster Garry Kasparov 3 to 2 games, becoming the first computerized program to defeat a human opponent.

Deep Blues unique skill of accurately and successfully playing chess matches highlight its reactive abilities. In the same vein, its reactive mind also indicates that it has no concept of past or future; it only comprehends and acts on the presently-existing world and components within it. To simplify, reactive machines are programmed for the here and now, but not the before and after.

Reactive machines have no concept of the world and therefore cannot function beyond the simple tasks for which they are programmed. A characteristic of reactive machines is that no matter the time or place, these machines will always behave the way they were programmed. There is no growth with reactive machines, only stagnation in recurring actions and behaviors.

Limited memory is comprised of machine learning models that derive knowledge from previously-learned information, stored data, or events. Unlike reactive machines, limited memory learns from the past by observing actions or data fed to them in order to build experiential knowledge.

Although limited memory builds on observational data in conjunction with pre-programmed data the machines already contain, these sample pieces of information are fleeting. An existing form of limited memory is autonomous vehicles.

Autonomous vehicles, or self-driving cars, use the principle of limited memory in that they depend on a combination of observational and pre-programmed knowledge. To observe and understand how to properly drive and function among human-dependent vehicles, self-driving cars read their environment, detect patterns or changes in external factors, and adjust as necessary.

Not only do autonomous vehicles observe their environment, but they also observe the movement of other vehicles and people in their line of vision. Previously, driverless cars without limited memory AI took as long as 100 seconds to react and make judgments on external factors. Since the introduction of limited memory, reaction time on machine-based observations has dropped sharply, depicting the value of limited memory AI.

GIF courtesy of ProStock/Getty via Tesla

What constitutes theory of mind is decision-making ability equal to the extent of a human mind, but by machines. While there are some machines that currently exhibit humanlike capabilities (voice assistants, for instance), none are fully capable of holding conversations relative to human standards. One component of human conversation is having emotional capacity, or sounding and behaving like a person would in standard conventions of conversation.

This future class of machine ability would include understanding that people have thoughts and emotions that affect behavioral output and thus influence a theory of mind machines thought process. Social interaction is a key facet of human interaction, so to make theory of mind machines tangible, the AI systems that control the now-hypothetical machines would have to identify, understand, retain, and remember emotional output and behaviors while knowing how to respond to them.

From this, said theory of mind machines would have to be able to use the information derived from people and adapt it into their learning centers to know how to communicate with and treat different situations. Theory of mind is a highly advanced form of proposed artificial intelligence that would require machines to thoroughly acknowledge rapid shifts in emotional and behavioral patterns in humans, and also understand that human behavior is fluid; thus, theory of mind machines would have to be able to learn rapidly at a moments notice.

Some elements of theory of mind AI currently exist or have existed in the recent past. Two notable examples are the robots Kismet and Sophia, created in 2000 and 2016, respectively.

Kismet, developed by Professor Cynthia Breazeal, was capable of recognizing human facial signals (emotions) and could replicate said emotions with its face, which was structured with human facial features: eyes, lips, ears, eyebrows, and eyelids.

Sophia, on the other hand, is a humanoid bot created by Hanson Robotics. What distinguishes her from previous robots is her physical likeness to a human being as well as her ability to see (image recognition) and respond to interactions with appropriate facial expressions.

GIF courtesy of GIPHY

These two humanlike robots are samples of movement toward full theory of mind AI systems materializing in the near future. While neither fully holds the ability to have full-blown human conversation with an actual person, both robots have aspects of emotive ability akin to that of their human counterparts one step toward seamlessly assimilating into human society.

Self-aware AI involves machines that have human-level consciousness. This form of AI is not currently in existence, but would be considered the most advanced form of artificial intelligence known to man.

Facets of self-aware AI include the ability to not only recognize and replicate humanlike actions, but also to think for itself, have desires, and understand its feelings. Self-aware AI, in essence, is an advancement and extension of theory of mind AI. Where theory of mind only focuses on the aspects of comprehension and replication of human practices, self-aware AI takes it a step further by implying that it can and will have self-guided thoughts and reactions.

We are presently in tier three of the four types of artificial intelligence, so believing that we could potentially reach the fourth (and final?) tier of AI doesnt seem like a far-fetched idea.

But for now, its important to focus on perfecting all aspects of types two and three in AI. Sloppily speeding through each AI tier could be detrimental to the future of artificial intelligence for generations to come.

TIP: Find out what AI software currently exists today, and see how it can help with your business processes.

Ready to learn more in-depth information about artificial intelligence? Check out articles on the benefits and risks of AI as well as the innovative minds behind the first genderless voice assistant!

More:

4 Main Types of Artificial Intelligence - G2

Over the last two years, we have witnessed a steady increase in our percent of readership in India. Sometime in 2017, Bangalore became one of our largest sources of job applicants, and our single biggest city in terms of readers overtaking both London and NYC.

Given the Indian governments recent focus on developing a plan for artificial intelligence, we decided to apply our strengths (deep analysis of AI applications and implications) to determine (a) the state of AI innovation in India, and (b) strategic insights to help India survive and thrive in a global market with the help of AI initiatives.

We traveled to Bangalore in an effort to speak with experts from the Government of India, Indian AI startups, AI academic researchers in India and data science executives at some of the largest companies operating in India, including Reliance ADA, Amazon, AIG, Equifax, Infosys, NVIDIA and many more.

Through the course of this research our objective was threefold:

We have broken our analysis down into the following sections below:

Well begin by examining what we learned about AI adoption in India:

Since the early 90s, the IT and ITeS services sector in India has been of tremendous importance to its economy eventually growing to account for 7.7% of Indias GDP in 2016. In an attempt to capitalize on this foundation, the current Indian administration announced in February 2018 that the government think-tank, National Institution for Transforming India (NITI) Aayog (Hindi for Policy Commission), will spearhead a national programme on AI focusing on research.

This development comes on the heels of the launch of a Task Force on Artificial Intelligence for Indias Economic Transformation by the Commerce and Industry Department of the Government of India in 2017.

The industry experts we interviewed seemed to agree that artificial intelligence has certainly caught the attention of the Indian government and the tech community in recent years. According to Komal Sharma Talwar, Co-founder XLPAT Labs and member of Indias AI Task Force:

I think the government has realized that we need to have a formal policy in place so that theres a mission statement from them as to how AI should evolve in the country so its beneficial at large for the country.

Indeed its comments like Komals that made us realize that we should aid in determining a strategic direction for artificial intelligence development in India and learn as much as possible about the possible strategic value of the technology.

In our research and interviews, we saw consensus (from executives, non-profits, and researchers alike) that healthcare and agriculture would be among the most important sectors of focus in order to improve living conditions for Indias citizens.

Just as Google, Oracle, Microsoft, and Amazon are battling to serve the cloud computing and machine learning needs of the US government, the next three to five years may lead to a similar dynamic within India. As the Indian government pushes for digitization and enacts more AI initiatives, private firms will flock to win big contracts adding to the pool of funds to develop new technologies and spin out new AI and data science-related startups.

Mayank Kapur, CTO of Indian AI startup Gramener, says that the government is still the largest potential customer for data science services in the country. Other experts we spoke with have enunciated that more and more Indian startups and established tech firms are beginning to implement AI in their products and services.

Mr. Avik Sarkar, the Head of the Data Analytics Cell for NITI Aayog explains that the think-tank which has been tasked with spearheading Indias AI strategy is currently engaged in the following public sector initiatives:

The current areas of focus for AI applications in India are majorly focused in 3 areas:

With the governments growing interest around AI applications in India, Deepak Garg the Director at NVIDIA-Bennett Center of Research in Artificial Intelligence (andDirector LeadingIndia.ai) believes that there has been a significant growth in interest levels around AI across all industry sectors in India.

He explains that although AI attention is considerably smaller in India than in China or the USA, the increased AI interest has manifested itself in the following three ways:

1) Industries have started working to skill their manpower to enable themselves to compete with other global players

2) Educational institutions have started working on their curricula to include courses on machine learning and other relevant areas

3) Individuals and professionals have started acquiring these skills and are comfortable investing in upgrading their own skills.

Despite the initial enthusiasm for AI, there were also a few opinions from experts about a sense of unfulfilled potential and that the country could be doing far more to adopt and integrate AI technologies.

Another common theme we heard often during our interviews was that culturally speaking the cost of failure is much higher in India than the West. While failing in an attempt at bold innovation and grand goals might be seen as noble or brave in Silicon Valley or New York City (or even Boston), failure often implies a loss of face in India and some Asian countries. This has historically meant a lack of room for innovative experimentation.

Dr. Nishant Chandra, the Data Science Leader of Science group at AIG adds a valuable insight about the high stakes for failure in India and that cultural and economic factors play into raising these stakes:

Indian society is not as forgiving to failure in entrepreneurship as US or Europe. So far, this has led to ideas borrowed from other places and implemented after customization. Yet I believe, entrepreneurs will build upon the success of IT services industry and establish globally competitive AI companies in near future.

We caught up with Professor Manish Gupta at IIIT Bangalore Manish is also a startup founder (VideoKen) and former AI researcher at Xerox and Goldman Sachs India. He expressed his disappointment in Indias lack of global AI participation:

I think that we are not doing enough justice to our potential [in India]; I think we are really far behind some of the other leaders. I see a lot of American and Chinese companies at global AI conference like NIPS / AAAI and these two countries seem to be far ahead of the rest of the pack. I look at India as a country that ought to be doing a lot more.

A number of our interviewees mentioned the prevalence of copy-catting business models in India (taking a famous or successful business model in the USA or Europe and reconstructing it in India), as opposed to the invention of entirely new business models.

Google is not the copy-cat of another business in another country, nor is Facebook, Amazon, or Microsoft and many of the same interviewees we spoke with are hopeful that India will have its own global trend-setters as its technology ecosystem develops.

Our previous research on AI enterprise adoption seems to indicate that it may be another 2-5 years until AI adoption becomes mainstream in the Fortune 500 and even that is only at the level of pilots and initiatives, not of revolutionary results.

This learning phase evident given the state of AI adoption the Western markets may last longer in Indias relatively underdeveloped economy.

Aakrit Vaish, CEO of Haptik, Inc. also seems to suggest that in the next 10 years we can expect that understanding of AI and how it works will potentially be more commonplace among most technical industry executives:

India may go in the direction that China has gone, become their own economies. There are probably going to be pockets, Bangalore might be good at deep tech like robotics or research / Hyderabad being good at data/ AI training, Mumbai being good at BFSI and Delhi for agriculture and government. Like China, most solutions will probably be applied to the local economy.

Indias services sector (call centers, BPOs, etc roughly 18% of the Indian GDP) have a significant potential opportunity to cater to the coming demand for data cleaning and human-augmented AI training (data labeling, search engine training, content moderation, etc).

Komal Talwar from Government of Indias AI Task Force added her views on what the Indian governments future strategy around AI might be focused on:

We think AI could have a great impact in health sector. There is a scarcity for good doctors and nurses, with AI the machine can do the first round of diagnostics. Staff can carry machines with them to help cut down in the physical presence needed for doctors.

The government is really encouraging startups to have AI applications that really have a social impact (AI in health, AI in education, etc), where startups compete to solve social problems.

Has India woken up to artificial intelligence? Expert opinions on this topic seem mixed, yet through our analysis, we managed to distill the following themes:

Interested readers can learn more about AI applications in India today from our other articles about AI traction in some of Indias largest sectors:

The majority of our Indian AI respondents and interviewees showed optimism about Indias potential to be one of the key global players in the future of AI. Optimism about the prospects of ones own nations success seems a natural bias (and one that weve seen before in our geography-specific coverage in Montreal, Boston, and more) but Indias optimism isnt unwarranted.

Since the early 90s when the Indian economy opened up to foreign investment, the country has been considered by some economists as the dark horse among the larger economies in the world.

Historically, the slower adoption of IT services by domestic Indian companies (in some cases by even by a period of around 10 years) as compared to global competitors was an indicator of the unfulfilled potential according to some experts we spoke to.

Yet, most of the interviewees seemed bullish on the fact that this time around in the wave of AI, India is firmly backing its strengths as represented in the quote below from Aakrit Vaish Co-founder and CEO of Haptik, Inc.

The Indian foundation of IT services and business process outsourcing makes me believe that such AI training jobs will be even more lucrative for India than elsewhere in the future.

During the interview with him, Aakrit explained his stance with an example about the possibility that Indian BPO services providers could potentially be attractive in terms of skills and cost for tasks (which he believes will for a long time remain a manual effort) like cleaning and tagging of data in the near future.

We heard opinions from other experts favoring the view that India may be positioned well to take advantage of the AI disruption. Sundara Ramalingam Nagalingam, Head of Deep Learning Practice at NVIDIA India, shares his thoughts on some of the advantages India may have over other countries in terms of AI:

India is the third largest startup ecosystem in the world, with three to four startups being born here daily. We believe India has a major advantage over other countries in terms of talent, a vibrant startup ecosystem, strong IT services and an offshoring industry to harness the power of AI.

Kiran Rama, the Director of Data Sciences at the VMware Center of Excellence (CoE) in Bangalore also seems to agree that the cost-competitive talent in India will be an opportunity for companies looking to open offices in India:

There seems to be a lot of opportunity for companies that are setting u shop in India. Especially since there is a supply of data science talent at a good cost advantage. I also think there Indians are starting to contribute to the advancement of machine learning libraries and algorithms.

Subramanian Mani, who heads the analytics wing at BigBasket.com, an online Indian grocery e-commerce firm, reiterates the idea that the IT services background in India is an advantage.

He believes that the major difference between the software and AI waves is that although India was slow to adopt software service as compared to America, this time around with the AI wave, adoption will be much faster and only slightly behind the leading countries.

This is the second wave. The software wave was 30 years ago. Folks in India realized that theyve been able to scale software and I think AI / ML is an extension of software development.

While software was often taught through books and in classrooms exclusively, many of the latest artificial intelligence approaches are available to learn online along with huge suites of open-source tools (from scikit-learn to TensorFlow and beyond).

Going in, we knew that one of the key advantages for India would, in fact, be the very IT and ITeS sectors which will make it easy for Indian tech providers to transition into AI services, given that well-developed ecosystems have evolved over the past 25 years in cities like Bangalore and Hyderabad.

Manish Gupta, Director of Machine Learning & Data Science at American Express India, expressed optimism in Bangalore as an innovation hub:

Bangalore has always been seen as the Silicon Valley of India and today there are lots of analytics companies here. It has all the ingredients to be a leader in the AI space. The state government is interested in planning and grooming for startups in this space as witnessed by the launch of the Center for Excellence (CoE) in AI setup by the GOI and NASSCOM in Bangalore.

While the advantage from the existing Indian IT sector may have been more intuitive, Madhusudan Shekar, Principal Technology Evangelist at Amazon AWS explains through an example how Indias diversity and scale (generally considered a challenge) can be an opportunity to make the best out of a tough situation:

In India, people speak over 40+ formal languages in about 800+ dialects. There are 22 national languages and if you want to build a neural network for speech, India is the best place to build that neural net. If you can build for India, you can most likely build it for other parts of the world.

In this respect, India with all of its language challenges could be a petri dish for translation-oriented AI applications. The market for this technology especially when backed by the Indian government may well rival the kind of AI innovations developed around translation in other parts of the world.

Another insight that was oft repeated by the experts was around the potential to have access to vast amounts of data in India. To further explain, According to a report by the Telecom Regulatory Authority of India (TRAI) the total number of internet subscribers in the country as a percentage of the overall population increased by 12.01% from December 2013 to reach 267.39 million in December 2014.

Along these lines, Mayank Kapur Co-founder of Gramener cites the increased level of data collection and the scale to which it could potentially grow as an opportunity for India in public sector AI applications:

In the public sector, we have an advantage of scale the amount of data that can potentially be gathered is huge. For example, leveraging data to provide access to services is a huge differentiator in the healthcare sector for applications like disease prevention or nutrition.

Figure. Number of internet subscribers

in India in 2014 by access type (Source)

Juergen Hase the CEO of Unlimit- A Reliance Group Company, one of Indias largest private sector companies, expressed his thoughts during our research:

The direct switch to mobile platforms in India means that there are no legacy systems to deal with and new technologies can be developed from scratch.

As shown in the figure to the right, an overwhelming majority of Indias Internet subscribers gain access through mobile wireless networks.

As Juergen points out, what this means is that large-scale AI projects in India can be somewhat insulated from issues cropping up from legacy systems. This might also lead to a greater immediate mobile-fluency for Indias startup and developer communities, who need to appeal to an almost exclusively mobile market.

Juergen adds, in the future, we can expect that AI software will also potentially have this advantage in India as compared to developed countries where the ratio is more evenly distributed among mobile and fixed wireless users.

We think our business audience will indeed find the next quote from Avi Patchava, Vice President, Data Sciences, ML & AI InMobi, highly insightful in terms of gaining an overview of Indias biggest strengths with respect to the countrys ability to leverage AI. Avi neatly summed up what he believes are Indias four biggest strengths to face the upcoming AI disruption:

The following points became evident through our interviews about Indias AI strengths and opportunities:

While there were many favorable views on the future outlook of the Indian AI ecosystem, there seemed to be different views among experts regarding the challenges that the country might have to overcome to survive and thrive in the AI disruption.

We heard a significant number of experts allude to the fact that the hype around AI may still be very real in India and there exists here a common tendency to view AI as a discrete industry rather than the broad, core technology that it is (like the internet).

In addition to being misunderstood and not being properly leveraged, many of the experts we spoke with were candid about addressing what they see as relative weaknesses of the Indian AI ecosystem.

Aakrit Vaish from Haptik, Inc. shares his thoughts on the AI hype that he sees in the Indian tech scene today:

Today AI is getting a lot of attention in India but nobody knows what it is or what are the best applications for it. Theres a little of a spray-and-pray attitude across the board.

While AI hype is hard to escape in the tech press in any country our speaking engagements in India seemed to affirm the state ambiguity around AI. We received post-presentation questions from attendees (about AI taking jobs, about the definition of AI itself, about the ongoings of Google and Facebook) that seemed like less informed questions than we might hear from a similarly technical audience in Boston or San Francisco.

This may mostly be due to the fact that AI applications are less well understood, and genuinely knowledgeable AI talent is rarer. We might suspect that over the coming few years particularly in a tech hub like Bangalore wed see this knowledge lessen over time.

Co-founder of XLPAT Labs and member of Indias AI Task Force Komal Sharma specifically points out that even some of the government projects have faced issues in terms of receiving funding for initiating AI pilot projects. She seems to indicate that the current Indian AI and startup funding ecosystem is not mature enough to be comparable to the US or even China.

The problem that we have faced I think is funding in areas where our field is very niche. In India, IP is developing lots of interest, but were nowhere near the US or other countries.

Komal was far from being alone in her lamenting AIs lack of VC funding, and the sentiment of our respondents seems to be backed up by the data.

The World Economic Forum chart below features information from Ernst & Young:

Taken as a percent of GDP, Israels VC investments represent about 0.006% of GDP, while Indias investments represent around 0.002%. As the Indian economy continues to develop and if Indias entrepreneurship trend continues we should expect to see investment increase.

Madhu Gopinathan Vice President, Data Science at MakeMyTrip,Indias largest online travel company,touches on a point repeated by other experts as well. He thinks that the two underlying factors here are larger salaries lie in the corporate sector, which is potentially creating a dearth of mentors for the next generation of software developers looking to transition into AI and the availability of data.Academia and Industry collaboration is a serious issue in India. Although we have a lot of universities, the incentives are skewed towards the corporate sector. For example, people like me who have an understanding of the technology may not be inclined to teach the next generation at universities, since working at the larger companies is far more lucrative today.

Madhu believes that much of the AI upskilling of Indias development talent will occur on the job in the cutting-edge work environments of venture-backed companies, as opposed to in the classroom.

As Nishant Chandra from AIG puts it, the boom in the Indian IT services sector in the early 90s was partially born out of necessity India just did not have a good products ecosystem. India has historically not done well with products and according to the experts, there also seems to be a dearth of good talent specifically for design and user-interface functions.

Sumit Borar, Sr. Director Data Sciences at Myntra, the Indian fashion eCommerce firm, is of the opinion that the scale of AI talent in India is still very nascent although he expects this to change in the next three years:

Talent will be the biggest strength for India with respect to AI. But AI is still new, so current talent in the market is very limited but in 3 years time I think that will become a strength.

Industry-university partnerships where students can work with real world data science applications and reskilling of existing workforces (example: getting software engineers to look at statistics or vice versa) are just beginning to take shape in India (starting with the unicorns).

The cultural factors in India play a role in talent development here as explained by Nimilita Chatterjee SVP, Data and Analytics at Equifax:

I see issues in AI talent in India are at 3 levels:

The issues that Nimilita addresses above arent all that different from what we see in the United States (indeed in Silicon Valley) on a daily basis. It does seem safe to say, however, that experienced data science talent (more specifically: Talent who have applied data science and AI skills in a real business context) is much more sparse in India than it is in the USA at least for now.

Nilmilita also believes that another weakness for India today in terms of data access for AI applications in the finance sector stems from the fact that the Indian economy still operates primarily on cash. As of 2017, Indias Economic Times claims that cash comprises 95% of the Indian economy.

Although there is a small percentage of the population that is making the switch to digital transactions, she believes that this segment of the population is still not significant enough before AI adoption in this sector becomes widespread in India.

India moving away from cash and being comfortable on a mobile phone, however that part of the population is still small. It will come into play in the future, but today it is still an issue in the finance sector.

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Artificial Intelligence in India Opportunities, Risks ...

The positioning of the Global Artificial Intelligence in Aviation Market vendors in FPNV Positioning Matrix are determined by Business Strategy (Business Growth, Industry Coverage, Financial Viability, and Channel Support) and Product Satisfaction (Value for Money, Ease of Use, Product Features, and Customer Support) and placed into four quadrants (F: Forefront, P: Pathfinders, N: Niche, and V: Vital).

New York, March 28, 2020 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Global Artificial Intelligence in Aviation Market - Premium Insight, Competitive News Feed Analysis, Company Usability Profiles, Market Sizing & Forecasts to 2025" - https://www.reportlinker.com/p05871978/?utm_source=GNW

The report deeply explores the recent significant developments by the leading vendors and innovation profiles in the Global Artificial Intelligence in Aviation Market including are Intel, Micron, Nvidia, Samsung Electronics, Xilinx, Airbus, Amazon, Boeing, Garmin, GE, IBM, Lockheed Martin, Microsoft, and Thales.

On the basis of Technology, the Global Artificial Intelligence in Aviation Market is studied across Computer Vision, Context Awareness Computing, Machine Learning, and Natural Language Processing (Nlp).

On the basis of Offering, the Global Artificial Intelligence in Aviation Market is studied across Hardware, Services, and Software.

On the basis of Application, the Global Artificial Intelligence in Aviation Market is studied across Dynamic Pricing, Flight Operations, Manufacturing, Smart Maintenance, Surveillance, Training, and Virtual Assistants.

For the detailed coverage of the study, the market has been geographically divided into the Americas, Asia-Pacific, and Europe, Middle East & Africa. The report provides details of qualitative and quantitative insights about the major countries in the region and taps the major regional developments in detail.

In the report, we have covered two proprietary models, the FPNV Positioning Matrix and Competitive Strategic Window. The FPNV Positioning Matrix analyses the competitive market place for the players in terms of product satisfaction and business strategy they adopt to sustain in the market. The Competitive Strategic Window analyses the competitive landscape in terms of markets, applications, and geographies. The Competitive Strategic Window helps the vendor define an alignment or fit between their capabilities and opportunities for future growth prospects. During a forecast period, it defines the optimal or favorable fit for the vendors to adopt successive merger and acquisitions strategies, geography expansion, research & development, new product introduction strategies to execute further business expansion and growth.

Research Methodology:Our market forecasting is based on a market model derived from market connectivity, dynamics, and identified influential factors around which assumptions about the market are made. These assumptions are enlightened by fact-bases, put by primary and secondary research instruments, regressive analysis and an extensive connect with industry people. Market forecasting derived from in-depth understanding attained from future market spending patterns provides quantified insight to support your decision-making process. The interview is recorded, and the information gathered in put on the drawing board with the information collected through secondary research.

The report provides insights on the following pointers:1. Market Penetration: Provides comprehensive information on sulfuric acid offered by the key players in the Global Artificial Intelligence in Aviation Market 2. Product Development & Innovation: Provides intelligent insights on future technologies, R&D activities, and new product developments in the Global Artificial Intelligence in Aviation Market 3. Market Development: Provides in-depth information about lucrative emerging markets and analyzes the markets for the Global Artificial Intelligence in Aviation Market 4. Market Diversification: Provides detailed information about new products launches, untapped geographies, recent developments, and investments in the Global Artificial Intelligence in Aviation Market 5. Competitive Assessment & Intelligence: Provides an exhaustive assessment of market shares, strategies, products, and manufacturing capabilities of the leading players in the Global Artificial Intelligence in Aviation Market

The report answers questions such as:1. What is the market size of Artificial Intelligence in Aviation market in the Global?2. What are the factors that affect the growth in the Global Artificial Intelligence in Aviation Market over the forecast period?3. What is the competitive position in the Global Artificial Intelligence in Aviation Market?4. Which are the best product areas to be invested in over the forecast period in the Global Artificial Intelligence in Aviation Market?5. What are the opportunities in the Global Artificial Intelligence in Aviation Market?6. What are the modes of entering the Global Artificial Intelligence in Aviation Market?Read the full report: https://www.reportlinker.com/p05871978/?utm_source=GNW

About ReportlinkerReportLinker is an award-winning market research solution. Reportlinker finds and organizes the latest industry data so you get all the market research you need - instantly, in one place.

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The Global Artificial Intelligence in Aviation Market is ...

Artificial intelligence (AI) in business is rapidly becoming a commonly-used competitive tool. Clearly, companies are past debating the pros and cons of AI. From better chatbots for customer service to data analytics to making predictive recommendations, deep learning and artificial intelligence in their many forms is seen by business leaders as an essential tool.

That puts AI in the short-list of technologies that your company should not just be watching, but actively exploring how to take advantage of. It joins leading emerging technologies like Machine Learning, cloud computing and Big Data.

If you aren't convinced that AI is ready to handle a growing number and range of tasks, consider IBM's Watson's 2011 winning performance on Jeopardy. Or consider the various ways you are likely already using AI-enabled devices and services in your personal life, like smart assistant apps or devices like Amazon's Alexa or Apple's Siri. Not to mention other AI-supercharged apps, such as whatever GPS app you use while driving.

Here's a quick look at how your competitors are already using AI in their business, and some advice on how to get on board.

Jump to:

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

Odds are you can't just call up your competitors and ask how they are using AI in their company. But thanks to the Internet, you can find out a lot of what they have said. For example, web-searching "how is Staples using AI" yields informative results from about how that company is putting artificial intelligence technology to work for itself.

Next, check your competitors' web sites and social media presences (notably LinkedIn and Facebook). Browse their press releases, news coverage, and blogs. You might even go old-school, and get any hardcopy newsletters, annual reports or other literature from the past year that might not be available online.

Then cast a wider net, with an industry search, like "how are hospitals using AI," "how are grocery stores using AI," or even a more general search.

For example, when I did a search for "using AI in my business," I got various hits talking about business uses for AI including:

Another suggestion: research how other parts of your supply chain parts, shipping, support, and the like are using AI.

Don't forget other IRL (In Real Life)/non-digital avenues. If you are going to an industry event, look for AI-related sessions. Chat up whoever you're standing or sitting near. And, of course, you could always read a book or two although, by definition, that advice will be at least six to twelve months out of date.

If you know anybody at one or more competitors who's informed and amenable, buy them a lunch and pick their brain.

Businesses have high hopes that AI can help them predict a wide array of activities.

Based on your research, you should be able to build a list and frame a sense of what AI can do for businesses in general, and for companies in your industry and of your size.

You'll find several major key areas:

Based on this list, your next step is to come up with a short list of how artificial intelligence can help your business specific tasks and use cases.

To help make this list:

Then, prioritize that list based on a mix of estimated costs, time to implement, risk/benefit, and overall value.

In parallel, select one or two smaller tasks for trying artificial intelligence for your business. This could be a small piece of a larger task. Important: start with a task that is not business-critical. Another quick tip: Start with tasks that aren't customer-facing.

Now it's time to identify potential technology vendors. There is no shortage of top artificial intelligence companies.

In order to find and compare vendors, you first have to assess how you might add artificial intelligence capabilities to your company's IT, which in turn depends on factors like:

Vendors for AI capabilities spans several categories:

For some of the AI you're looking for, your current vendors may already offer. In other cases, you may outsource. For still others, you may end up doing internally. It all depends on what you want, how much developer bandwidth you have in-house, and how you provision your IT operations.

Your best bet will be to find one or more AI experts, either internally, or outside consultants. For the latter, start with ones who aren't part of a vendor... unless the vendor is offering AI that is a match for your criteria.

Once you have identified your initial AI projects the real fun begins: implementation. Essential milestones:

Key: Be ready to revisit constantly.

Just because you have AI projects out of development and testing, and contributing to your business, that doesn't mean you're done. Just as provisioning infrastructure or updating your company's web and social presence is never done.

In addition to tracking your selected AI vendors for improvements, new features you want to stay on top of other AI developments. For example, what new capabilities have become available? What improvements in infrastructure performance or price make existing or new AI offerings now viable?

And, of course, you want to keep up with what others in your industry, and the AI vendors serving your industry, are doing or have on their road map.

Adding AI your company's operations and business is a big change, and likely a big transformation. Here's some quick advice to lessen the challenges:

Plus, focus on AI that's available as a supported product/service, rather than something still in development.

Although AI as an area within computer science dates back to the 1950's, it's only been within the past decade that many types of AI have become available to companies of all sizes.

This is thanks to factors like continuing hardware price/performance improvements, cloud computing, and advances in AI techniques. At the same time, computing trends like big data, IoT, self-driving vehicles, and speech and image recognition are generating more "targets" to point AI tools at.

In particular, Keep an eye on cloud costs and capabilities, along with what the various players are doing or talking about, AI-wise. Like nearly everything involving computer technology, many of the next cool capabilities can't be anticipated or predicted. Bottom line: talk to professionals in your field nothing will help you quite as much.

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Artificial Intelligence in Business: How to Use AI in Your ...

In the grand scheme of things, artificial intelligence (AI) is still in the very early stages of adoption by most organizations. However, most leaders are quite excited to implement AI into the companys business functions to start realizing its extraordinary benefits. While we have no way of knowing all the ways artificial intelligence and machine learning will ultimately impact business functions, here are 10 business functions that are ready to use artificial intelligence.

10 Business Functions That Are Ready To Use Artificial Intelligence

Marketing

If your company isnt using artificial intelligence in marketing, it's already behind. Not only can AI help to develop marketing strategies, but it's also instrumental in executing them. Already AI sorts customers according to interest or demographic, can target ads to them based on browsing history, powers recommendation engines, and is a critical tool to give customers what they want exactly when they want it. Another way AI is used in marketing is through chatbots. These bots can help solve problems, suggest products or services, and support sales. Artificial intelligence also supports marketers by analyzing data on consumer behavior faster and more accurately than humans. These insights can help businesses make adjustments to marketing campaigns to make them more effective or plan better for the future.

Sales

There is definitely a side of selling products and services that is uniquely human, but artificial intelligence can arm sales professionals with insights that can improve the sales function. AI helps improve sales forecasting, predict customer needs, and improve communication. And intelligent machines can help sales professionals manage their time and identify who they need to follow-up with and when as well as what customers might be ready to convert.

Research and Development (R&D)

What about artificial intelligence as a tool of innovation? It can help us build a deeper understanding in nearly any industry, including healthcare and pharmaceuticals, financial, automotive, and more, while collecting and analyzing tremendous amounts of information efficiently and accurately. This and machine learning can help us research problems and develop solutions that weve never thought of before. AI can automate many tasks, but it will also open the door to novel discoveries, ways of improving products and services as well as accomplishing tasks. Artificial intelligence helps R&D activities be more strategic and effective.

IT Operations

Also called AIOps, AI for IT operations is often the first experience many organizations have with implementing artificial intelligence internally. Gartner defines the term AIOps as the application of machine learning and data science to IT operations problems. AI is commonly used for IT system log file error analysis, with IT systems management functions as well as to automate many routine processes. It can help identify issues so the IT team can proactively fix them before any IT systems go down. As the IT systems to support our businesses become more complex, AIOps helps the IT improve system performance and services.

Human Resources

In a business function with human in the name, is there a place for machines? Yes! Artificial intelligence really has the potential to transform many human resources activities from recruitment to talent management. AI can certainly help improve efficiency and save money by automating repetitive tasks, but it can do much more. PepsiCo used a robot, Robot Vera, to phone and interview candidates for open sales positions. Talent is going to expect a personalized experience from their employer just as they have been accustomed to when shopping and for their entertainment. Machine learning and AI solutions can help provide that. In addition, AI can help human resources departments with data-based decision-making and make candidate screening and the recruitment process easier. Chatbots can also be used to answer many common questions about company policies and benefits.

Contact Centers

The contact center of an organization is another business area where artificial intelligence is already in use. Organizations that use AI technology to enhance rather than replace humans with these tasks are the ones that are incorporating artificial intelligence in the right way. These centers collect a tremendous amount of data that can be used to learn more about customers, predict customer intent, and improve the "next best action" for the customer for better customer engagement. The unstructured data collected from contact centers can also be analyzed by machine learning to uncover customer trends and then improve products and services.

Building Maintenance

Another way AI is already at work in businesses today is helping facilities managers optimize energy use and the comfort of occupants. Building automation, the use of artificial intelligence to help manage buildings and control lighting and heating/cooling systems, uses internet-of-things devices and sensors as well as computer vision to monitor buildings. Based upon the data that is collected, the AI system can adjust the building's systems to accommodate for the number of occupants, time of day, and more. AI helps facilities managers improve energy efficiency of the building. An additional component of many of these systems is building security as well.

Manufacturing

Heineken, along with many other companies, uses data analytics at every stage of the manufacturing process from the supply chain to tracking inventory on store shelves. Predictive intelligence can not only anticipate demand and ramp production up or down, but sensors on equipment can predict maintenance needs. AI helps flag areas of concern in the manufacturing process before costly issues erupt. Machine vision can also support the quality control process at manufacturing facilities.

Accounting and Finance

Many organizations are finding the promise of cost reductions and more efficient operations the major appeal for artificial intelligence in the workplace, and according to Accenture Consulting, robotic process automation can produce amazing results in these areas for the accounting and finance industry and departments. Human finance professionals will be freed-up from repetitive tasks to be able to focus on higher-level activities while the use of AI in accounting will reduce errors. AI is also able to provide real-time status of financial matters to organizations because it can monitor communication through natural language processing.

Customer Experience

Another way artificial intelligence technology and big data are used in business today is to improve the customer experience. Luxury fashion brand Burberry uses big data and AI to enhance sales and customer relationships. The company gathers shopper's data through loyalty and reward programs that they then use to offer tailored recommendations whether customers are shopping online or in brick-and-mortar stores. Innovative uses of chatbots during industry events are another way to provide a stellar customer experience.

For more on AI and technology trends, see Bernard Marrs bookArtificial Intelligence in Practice: How 50 Companies Used AI and Machine Learning To Solve Problemsand his forthcoming bookTech Trends in Practice: The 25 Technologies That Are Driving The 4ThIndustrial Revolution, which is available to pre-order now.

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10 Business Functions That Are Ready To Use Artificial Intelligence - Forbes

Moving up the charts with AI

There is increasing awareness that the greatest problems with artificial intelligence are not primarily technical, but rather how to achieve value from the technology. This was a growing problem even in the booming economy of the last several years, but a much more important issue in the current pandemic-driven recessionary economic climate.

Older AI technologies like natural language processing, and newer ones like deep learning, work well for the most part and are capable of providing considerable value to organizations that implement them. The challenges are with large-scale implementation and deployment of AI, which are necessary to achieve value. There is substantial evidence of this in surveys.

In an MIT Sloan Management Review/BCG survey, seven out of 10 companies surveyed report minimal or no impact from AI so far. Among the 90% of companies that have made some investment in AI, fewer than 2 out of 5 report business gains from AI in the past three years.This number improves to 3 out of 5 when we include companies that have made significant investments in AI. Even so, this means 40% of organizations making significant investments in AI do not report business gains from AI.

NewVantage Partners 2019 Big Data and AI Executive surveyFirms report ongoing interest and an active embrace of AI technologies and solutions, with 91.5% of firms reporting ongoing investment in AI. But only 14.6% of firms report that they have deployed AI capabilities into widespread production. Perhaps as a result, the percentage of respondents agreeing that their pace of investment in AI and big data was accelerating fell from 92% in 2018 to 52% in 2019.

Deloitte 2018 State of Enterprise AI surveyThe top 3 challenges with AI were implementation issues, integrating AI into the companys roles and functions, and data issuesall factors involved in large-scale deployment.

In a 2018 McKinsey Global Survey of AI, most respondents whose companies have deployed AI in a specific function report achieving moderate or significant value from that use, but only 21 percent of respondents report embedding AI into multiple business units or functions.

In short, AI has not yet achieved much return on investment. It has yet to substantially improve the lives of workers, the productivity and performance of organizations, or the effective functions of societies. It is capable of doing all these things, but is being held back from its potential impact by a series of factors I will describe below.

Whats Holding AI Back

Ill describe the factors that are preventing AI from having a substantial return in terms of the letters of our new organization: the ROAI Institute. Although it primarily stands for return on artificial intelligence, it also works to describe the missing or critical ingredients for a successful return:

ReengineeringThe business process reengineering movement of the 1980s and early 90s, in which I wrote the first article and book (admittedly by only a few weeks in both cases) described an opportunity for substantial change in broad business processes based on the capabilities of information technology. Then the technology catalyst was enterprise systems and the Internet; now its artificial intelligence and business analytics.

There is a great opportunitythus far only rarely pursuedto redesign business processes and tasks around AI. Since AI thus far is a relatively narrow technology, task redesign is more feasible now, and essential if organizations are to derive value from AI. Process and task design has become a question of what machines will do vs. what tasks are best suited to humans.

We are not condemned to narrow task redesign forever, however. Combinations of multiple AI technologies can lead to change in entire end to end processesnew product and service development, customer service, order management, procure to pay, and the like.

Organizations need to embrace this new form of reengineering while avoiding the problems that derailed the movement in the past; I called it The Fad that Forgot People. Forgetting people, and their interactions with AI, would also lead to the derailing of AI technology as a vehicle for positive change.

Organization and CultureAI is the child of big data and analytics, and is likely to be subject to the same organization and culture issues as the parent. Unfortunately, there are plenty of survey results suggesting that firms are struggling to achieve data-driven cultures.

The 2019 NewVantage Partners survey of large U.S. firms I cite above found that only 31.0% of companies say they are data-driven. This number has declined from 37.1% in 2017 and 32.4% in 2018. 28% said in 2019 that they have a data culture. 77% reported that business adoption of big data and AI initiatives remains a major challenge. Executives cited multiple factors (organizational alignment, agility, resistance), with 95% stemming from cultural challenges (people and process), and only 5% relating to technology.

A 2019 Deloitte survey of US executives on their perspectives on analytical insights found that most executives63%do not believe their companies are analytics-driven. 37% say their companies are either analytical competitors (10%) or analytical companies (27%). 67% of executives say they are not comfortable accessing or using data from their tools and resources; even 37% of companies with strong data-driven cultures express discomfort.

The absence of a data-driven culture affects AI as much as any technology. It means that the company and its leaders are unlikely to be motivated or knowledgeable about AI, and hence unlikely to build the necessary AI capabilities to succeed. Even if AI applications are successfully developed, they may not be broadly implemented or adopted by users. In addition to culture, AI systems may be a poor fit with an organization for reasons of organizational structure, strategy, or badly-executed change management. In short, the organizational and cultural dimension is critical for any firm seeking to achieve return on AI.

Algorithms and DataAlgorithms are, of course, the key technical feature of most AI systemsat least those based on machine learning. And its impossible to separate data from algorithms, since machine learning algorithms learn from data. In fact, the greatest impediment to effective algorithms is insufficient, poor quality, or unlabeled data. Other algorithm-related challenges for AI implementation include:

InvestmentOne key driver of lack of return from AI is the simple failure to invest enough. Survey data suggest most companies dont invest much yet, and I mentioned one above suggesting that investment levels have peaked in many large firms. And the issue is not just the level of investment, but also how the investments are being managed. Few companies are demanding ROI analysis both before and after implementation; they apparently view AI as experimental, even though the most common version of it (supervised machine learning) has been available for over fifty years. The same companies may not plan for increased investment at the deployment stagetypically one or two orders of magnitude more than a pilotonly focusing on pre-deployment AI applications.

Of course, with any technology it can be difficult to attribute revenue or profit gains to the application. Smart companies seek intermediate measures of effectiveness, including user behavior changes, task performance, process changes, and so forththat would precede improvements in financial outcomes. But its rare for these to be measured by companies either.

A Program of Research and Structured Action

Along with several other veterans of big data and AI, I am forming the Return on AI Institute, which will carry out programs of research and structured action, including surveys, case studies, workshops, methodologies, and guidelines for projects and programs. The ROAI Institute is a benefit corporation that will be supported by companies and organizations who desire to get more value out of their AI investments

Our focus will be less on AI technology-though technological breakthroughs and trends will be considered for their potential to improve returnsand more on the factors defined in this article that improve deployment, organizational change, and financial and social returns. We will focus on the important social dimension of AI in our work as wellis it improving work or the quality of life, solving social or healthcare problems, or making government bodies more responsive? Those types of benefits will be described in our work in addition to the financial ones.

Our research and recommendations will address topics such as:

Please contact me at tdavenport@babson.edu if you care about these issues with regard to your own organization and are interested in approaches to them. AI is a powerful and potentially beneficial technology, but its benefits wont be realized without considerable attention to ROAI.

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Return On Artificial Intelligence: The Challenge And The Opportunity - Forbes

NEW YORK--(BUSINESS WIRE)--Ninety One, Inc., an augmented intelligence company developing innovative software and data science solutions designed to automate cardiac remote monitoring and further Precision Medicine, announced today, announced today that Dr. Thomas Hong will be the first in the state of Texas to utilize the technology that combines state-of-the-art surveillance with early warning detection capabilities.

Ninety One, Inc. utilizes a cloud-native platform that automates the collection of data and reports from implanted cardiac devices and wearables digitizes, structures, and analyzes them with applied data science in an single-point, easy-to-use interface for patient care and innovation in research. Ninety Ones Global team of data scientists, software engineers, and modern mathematicians utilize artificial intelligence on vast amounts of data produced by these devices to predict disease episodes and disease progression. Ninety Ones ability to improve patient's quality of life, improve mortality rates, and accelerate decision making in real-time impacting patient outcomes is game-changing for cardiology, said Dr. Thomas Hong.

We are extremely excited to have our technology being used for the first time in the State of Texas. Dr. Hong has long been an innovator working in the forefront of technology and research to identify treatment pathways that lead to better patient experiences and outcomes, said Matthew Werner, Chief Commercial Officer at Ninety One.

About Dr. Thomas Hong

Dr. Hong is a cardiac electrophysiologist, specializing in treating patients with heart rhythm disorders and has served as Assistant Professor of Clinical Cardiology at Baylor College of Medicine and Houston Methodist Hospital. He has published in several peer-reviewed journals including Heart Rhythm, Journal of Cardiovascular Electrophysiology, and American Journal of Medicine.

About Ninety One

Ninety One is a privately-held, data science and native-cloud technology company, focusing on clinical advancement in predictive analytics and Precision Medicine, and has established key, exclusive partnerships with leading research and healthcare institutions in the United States, Europe, and Asia. Pursuing this mission with vigorous commitment and passion, while leveraging innovations in science, Ninety One aspires to make a material impact on disease diagnosis, treatment, and prevention.

For more information please visit https://www.91.life

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Houston Cardiologist Becomes the First in State to Use Ninety One Inc.'s Artificial Intelligence and Precision Medicine Platform - Business Wire

The agency is looking at how to best apply curated data sets to new use cases.

The Department of Veterans Affairs is closer to expanding its use of artificial intelligence and developing novel use cases.

In looking back on the early stages of the VAs newly launched artificial intelligence program, the department's Director of AI Gil Alterovitz noted ongoing questions about how to best leverage AI data sets for secondary uses.

One of the interesting challenges is often that data is collected for maybe one reason, and it may be used for analyzing and finding results for that one particular reason. But there may be other uses for that data as well. So when you get to secondary uses you have to examine a number of challenges, he said at AFCEA's Automation Transformation conference.

Some of the most pressing concerns the VAs AI program hasencountered include questions of how to best apply curated data sets to newfound use cases, as well as how to properly navigate consent of use for proprietary medical data.

Considering the specificity of use cases, particularly for advanced medical diagnostics and predictive analytics, Alterovitz has proposed releasing broader ecosystems of data sets that can be chosen and applied depending on the demands of specific AI projects.

Theres a lot to think about data sets and how they work together. Rather than release one data set, consider releasing an ecosystem of data sets that are related," he said."Imagine, for example, someone is searching for a trial you have information about. Consider the patient looking for the trial, the physician, the demographics, pieces of information about the trial itself, where its located. Having all that put together makes for an efficient use case and allows us to better work together."

Alterovitz also discussed the value of combining structured and unstructured data sets in AI projects, a methodology that Veterans Affairs has found to provide stronger results than using structured data alone.

When you look at unstructured data, there have been a number of studies in health care looking at medical records where if you look at only structured data or only unstructured data individually, you dont get as much of a predictive capability whether it be for diagnostics or prognostics as by combining them, he said.

Beyond refining and expanding these data applications methodologies, the VA also appears attentive to how to best leverage proprietary medical data while protecting personally identifying information.

The solution appears to lie in creating synthetic data sets that mimic the statistical parameters and overall metrics of a given data set while obscuring the particularities of the original data set it was sourced from.

How do you make data available considering privacy and other concerns?" Alterovitz said."One area is synthetic data, essentially looking at the statistics of the underlying data and creating a new data set that has the same statistics, but cant be identified because it generates at the individual level a completely different data set that has similar statistics."

Similarly, creating select variation within a given data set can serve to remove the possibility of identifying the patient source, You can take the data, and then vary that information so that its not the exact same information you received, but is maybe 20% different. This makes it so you can show its statistically not possible to identify that given patient with confidence.

Going forward, the VA appears intent on solving these quandaries so as to best inform expanded AI research.

A lot of the data we have wasnt originally designed for AI. How you make it designed and ready for use in AI is a challenge and one that has a number of different potential avenues, Alterovitz concluded

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VA Looking to Expand Usage of Artificial Intelligence Data - GovernmentCIO Media

ARTIFICIAL Intelligence (AI) is the wave of the future. This area of computer science emphasising the creation of intelligent machines that work and react like humans is heavily influencing and taking over the way we get on with daily life.

Artificial Intelligence is revolutionising industries and improving the way business is conducted.

More importantly, it is revolutionising industries and improving the way business is done, being already widely used in applications including automation, data analytics and natural language processing.

On a bigger spectrum, from self-driving cars to voice-initiated mobile phones and computer-controlled robots, the presence of AI is seen and felt almost everywhere.

As more industries shift towards embracing the science of incorporating human intelligence in machines so the latter can function, think and work like humans, the demand for human capital with the relevant skill and expertise correspondingly increases.

As such, the question is, how do engineering students ride this wave and make the most of it?

AI has a high learning curve but the rewards of a career in AI far outweigh the investment of time and energy.

Unlike most conventional careers, AI is still in its infancy stage although several modern nations have fully embraced the Fourth Industrial Revolution.

Taking this into account, UCSI University has taken the initiative to develop the Bachelor of Computer Engineering (Artificial Intelligence) programme.

The nations best private university for two years in a row, according to the two recent QS World University Rankings exercises, proactively defines its own AI curriculum to offer educational content that can help increase the supply of AI engineers with job-ready graduates and real world experiences.

The AI programme at UCSI consists of a number of specialisations and several overlapping disciplines, including mathematical and statistical methods, computer sciences and other AI core subjects to provide a conceptual framework in providing solutions for real-world engineering problems.

The first two years covers core theoretical knowledge such as mathematics and statistics, algorithm design and computer programming, as well as electrical and electronics.

Students will progress to the AI subfields by selecting the specialisation elective tracks covering emerging areas such as machine learning, decision-making and robotics, perception and language and human-AI interaction, among others.

We aim to nurture the new generation workforce with the right skills set and knowledge on smart technologies to accelerate Malaysias transformation into a smart and modern manufacturing system, says Ang.

UCSI Faculty of Engineering, Technology and Built Environment dean Asst Prof Ts Dr Ang Chun Kit pointed out that AI was unavoidably the way forward.

We aim to nurture the new generation workforce with the right skills set and knowledge on smart technologies to accelerate Malaysias transformation into a smart and modern manufacturing system.

This programme was developed with a vision to provide the foundation for future growth in producing more complex and high-value products for industry sectors in Malaysia, he said.

Leading the faculty in which 46 of its members have PhDs, Ang emphasised the university focuses on research attachment abroad and has established partnerships with key industry players.

The faculty also stands out in terms of receiving grants to advance high impact projects.

Students from the faculty are also annually selected for researches at world-renowned universities such as Imperial College London and Tsinghua University.

The faculty also strives to give students field experience through internships at various top companies.

An example would be Harry Hoon Jian Wen, an Electrical and Electronic Engineering student. He was selected to go to the University of Queensland for a research attachment while also successfully completing his internship at Schneider Electric.

For further details, visit http://online.ucsiuniversity.edu.my/ or email info.sec@ucsiuniversity.edu.my

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The future through Artificial Intelligence - The Star Online