Category Archives: Artificial Intelligence

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Automation, robotics and the use of sophisticated computer software and programs characterize a career in artificial intelligence (AI). Candidates interested in pursuing jobs in this field require specific education based on foundations of math, technology, logic, and engineering perspectives. Written and verbal communication skills are also important to convey how AI tools and services are effectively employed within industry settings. To acquire these skills, those with an interest in an AI career should investigate the various career choices available within the field.

The most successful AI professionals often share common characteristics that enable them to succeed and advance in their careers. Working with artificial intelligence requires an analytical thought process and the ability to solve problems with cost-effective, efficient solutions. It also requires foresight about technological innovations that translate to state-of-the-art programs that allow businesses to remain competitive. Additionally, AI specialists need technical skills to design, maintain and repair technology and software programs. Finally, AI professionals must learn how to translate highly technical information in ways that others can understand in order to carry out their jobs. This requires good communication and the ability to work with colleagues on a team.

Basic computer technology and math backgrounds form the backbone of most artificial intelligence programs. Entry level positions require at least a bachelors degree while positions entailing supervision, leadership or administrative roles frequently require masters or doctoral degrees. Typical coursework involves study of:

Candidates can find degree programs that offer specific majors in AI or pursue an AI specialization from within majors such as computer science, health informatics, graphic design, information technology or engineering.

A career in artificial intelligence can be realized within a variety of settings including private companies, public organizations, education, the arts, healthcare facilities, government agencies and the military. Some positions may require security clearance prior to hiring depending on the sensitivity of information employees may be expected to handle. Examples of specific jobs held by AI professionals include:

From its inception in the 1950s through the present day, artificial intelligence continues to advance and improve the quality of life across multiple industry settings. As a result, those with the skills to translate digital bits of information into meaningful human experiences will find a career in artificial intelligence to be sustaining and rewarding.

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What Skills Do I Need to Get a Job in Artificial Intelligence?

Artificial Intelligence Computers are everywhere today. It would be impossible to go your entire life without using a computer. Cars, ATMs, and TVs we use everyday, and all contain computers. It is for this reason that computers and their software have to become more intelligent to make our lives easier and computers more accessible. Intelligent computer systems can and do benefit us all; however people have constantly warned that making computers too intelligent can be to our disadvantage. Artificial intelligence, or AI, is a field of computer science that attempts to simulate characteristics of human intelligence or senses. These include learning, reasoning, and adapting. This field studies the designs of intelligentshow more content

Expert systems are also known as knowledge based systems. These systems rely on a basic set of rules for solving specific problems and are capable of learning. The laws are defined for the system by experts and then implemented using if-then rules. These systems basically imitate the experts thoughts in solving the problem. An example of this is a system that diagnosis medical conditions. The doctor would input the symptoms to the computer system and it would then ask more questions if need or give diagnoses. Other examples include banking systems for acceptance of loans, advanced calculators, and weather predictions. Natural language systems interact allow computers to interact with the user in their usual language. They accept, interpret, and execute the commands in this language. The attempt is to allow a more natural interaction between the computer and user. Language is sometimes thought to be the foundation of intelligence in humans. Therefore, it is reasonable for intelligent systems to be able to understand language. Some of these systems are advanced enough to hold conversations. A system that emulates human senses uses human sensory simulation. These can include methods of sight, sound, and touch. A very common implementation of this intelligence is in voice recognition software. It listens to what the user says, interprets the sounds, and displays the information on the screen. These are

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Artificial Intelligence Essay - 966 Words | Bartleby

Artificial Intelligence has facilitated the processing of a large amount of data and its use in the industry. The number of tools and frameworks available to data scientists and developers has increased with the growth of AI and ML. This article on Artificial Intelligence Tools & Frameworks will list out some of these in the following sequence:

Development of neural networks is a long process which requires a lot of thought behind the architecture and a whole bunch of nuances which actually make up the system.

These nuances can easily end up getting overwhelming and not everything can be easily tracked. Hence, the need for such tools arises, where humans handle the major architectural decisions leaving other optimization tasks to such tools. Imagine an architecture with just 4 possible booleanhyperparameters, testing all possible combinations would take 4! Runs. Retraining the same architecture 24 times is definitely not the best use of time and energy.

Also, most of the newer algorithms contain a whole bunch of hyperparameters. Heres where new tools come into the picture. These tools not only help develop but also, optimize these networks.

From the dawn of mankind, we as a species have always been trying to make things to assist us in day to day tasks. From stone tools to modern day machinery, to tools for making the development of programs to assist us in day to day life. Some of the most important tools and frameworks are:

Scikit-learn is one of the most well-known ML libraries. It underpins many administered and unsupervised learning calculations. Precedents incorporate direct and calculated relapses, choice trees, bunching, k-implies, etc.

It includes a lot of calculations for regular AI and data mining assignments, including bunching, relapse and order. Indeed, even undertakings like changing information, feature determination and ensemble techniques can be executed in a couple of lines.

For a fledgeling in ML, Scikit-learn is a more-than-adequate instrument to work with, until you begin actualizing progressively complex calculations.

On the off chance that you are in the realm of Artificial Intelligence, you have most likely found out about, attempted or executed some type of profound learning calculation. Is it accurate to say that they are essential? Not constantly. Is it accurate to say that they are cool when done right? Truly!

The fascinating thing about Tensorflow is that when you compose a program in Python, you can arrange and keep running on either your CPU or GPU. So you dont need to compose at the C++ or CUDA level to keep running on GPUs.

It utilizes an arrangement of multi-layered hubs that enables you to rapidly set up, train, and send counterfeit neural systems with huge datasets. This is the thing that enables Google to recognize questions in photographs or comprehend verbally expressed words in its voice-acknowledgment application.

Theano is wonderfully folded over Keras, an abnormal state neural systems library, that runs nearly in parallel with the Theano library. Keras fundamental favorable position is that it is a moderate Python library for profound discovering that can keep running over Theano or TensorFlow.

What sets Theano separated is that it exploits the PCs GPU. This enables it to make information escalated counts up to multiple times quicker than when kept running on the CPU alone. Theanos speed makes it particularly profitable for profound learning and other computationally complex undertakings.

Caffe is a profound learning structure made with articulation, speed, and measured quality as a top priority. It is created by the Berkeley Vision and Learning Center (BVLC) and by network donors. Googles DeepDream depends on Caffe Framework. This structure is a BSD-authorized C++ library with Python Interface.

It allows for trading computation time for memory via forgetful backprop which can be very useful for recurrent nets on very long sequences.

If you like the Python-way of doing things, Keras is for you. It is a high-level library for neural networks, using TensorFlow or Theano as its backend.

The majority of practical problems are more like:

In all of these, Keras is a gem. Also, it offers an abstract structure which can be easily converted to other frameworks, if needed (for compatibility, performance or anything).

PyTorch is an AI system created by Facebook. Its code is accessible on GitHub and at the present time has more than 22k stars. It has been picking up a great deal of energy since 2017 and is in a relentless reception development.

CNTK allows users to easily realize and combine popular model types such as feed-forward DNNs, convolutional nets (CNNs), and recurrent networks (RNNs/LSTMs). It implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers. CNTK is available for anyone to try out, under an open-source license.

Out of all the tools and libraries listed above, Auto ML is probably one of the strongest and a fairly recent addition to the arsenal of tools available at the disposal of a machine learning engineer.

As described in the introduction, optimizations are of the essence in machine learning tasks. While the benefits reaped out of them are lucrative, success in determining optimal hyperparameters is no easy task. This is especially true in the black box like neural networks wherein determining things that matter becomes more and more difficult as the depth of the network increases.

Thus we enter a new realm of meta, wherein software helps up build software. AutoML is a library which is used by many Machine learning engineers to optimize their models.

Apart from the obvious time saved, this can also be extremely useful for someone who doesnt have a lot of experience in the field of machine learning and thus lacks the intuition or past experience to make certain hyperparameter changes by themselves.

Jumping from something that is completely beginner friendly to something meant for experienced developers, OpenNN offers an arsenal of advanced analytics.

It features a tool, Neural Designer for advanced analytics which provides graphs and tables to interpret data entries.

H20 is an open-source deep learning platform. It is an artificial intelligence tool which is business oriented and help them to make a decision from data and enables the user to draw insights. There are two open source versions of it: one is standard H2O and other is paid version Sparkling Water. It can be used for predictive modelling, risk and fraud analysis, insurance analytics, advertising technology, healthcare and customer intelligence.

Google ML Kit, Googles machine learning beta SDK for mobile developers, is designed to enable developers to build personalised features on Android and IOS phones.

The kit allows developers to embed machine learning technologies with app-based APIs running on the device or in the cloud. These include features such as face and text recognition, barcode scanning, image labelling and more.

Developers are also able to build their own TensorFlow Lite models in cases where the built-in APIs may not suit the use case.

With this, we have come to the end of our Artificial Intelligence Tools & Frameworks blog. These were some of the tools that serve as a platform for data scientists and engineers to solve real-life problems which will make the underlying architecture better and more robust.

You can check out theAI and Deep Learning with TensorFlow Course that is curated by industry professionals as per the industry requirements & demands. You will master the concepts such as SoftMax function, Autoencoder Neural Networks, Restricted Boltzmann Machine (RBM) and work with libraries like Keras & TFLearn. The course has been specially curated by industry experts with real-time case studies.

Got a question for us? Please mention it in the comments section of Artificial Intelligence Tools & Frameworks and we will get back to you.

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Top 12 Artificial Intelligence Tools & Frameworks | Edureka

NIST has releaseda plan for prioritizing federal agency engagement in the development of standards for artificial intelligence (AI)per the February 2019 Executive Order on Maintaining American Leadership on Artificial Intelligence (EO 13859). The plan recommends the federal government commit to deeper, consistent, long-term engagement in AI standards development activities to help the United States to speed the pace of reliable, robust, and trustworthy AI technology development.

It calls for federal agencies to bolster AI standards-related knowledge, leadership, and coordination among agencies that develop or use AI; promote focused research on the trustworthiness of AI systems; support and expand public-private partnerships; and engage with international parties.

NIST will participate in developing AI standards, along with the private sector and academia, that address societal and ethical issues, governance, and privacy policies and principles. These AI standards-related efforts include:

While the AI community has agreed that these issues must factor into AI standards, many decisions still need to be made about whether there is yet enough scientific and technical basis to develop those standards provisions.

For news about this plan, seehttps://www.nist.gov/news-events/news/2019/08/plan-outlines-priorities-federal-agency-engagement-ai-standards-development

To provide the technical expertise and help develop and administer many of the future AI standards activities and development, NISTs Information Technology Laboratory recently established an Associate Director for IT Standardization position.

The U.S. Leadership in AI: A Plan for Federal Engagement in Developing Technical Standards and Related Tools report released on August 9, 2019, was prepared with broad public and private sector input. The plan identifies nine areas of focus for AI standards:

Through these focus areas, the Federal government will commit to deeper, consistent, long-term engagement in AI standards development activities to help the United States to speed the pace of reliable, robust, and trustworthy AI technology development. Specifically, the government will:

NIST will play an active role in advancing the AI standards strategies. NISTs Information Technology Laboratory has recently established an Associate Director for IT Standardization position, which will help administer many of NISTs future AI standards activities and development.

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AI Standards | NIST

The Artificial Intelligence tutorial provides an introduction to AI which will help you to understand the concepts behind Artificial Intelligence. In this tutorial, we have also discussed various popular topics such as History of AI, applications of AI, deep learning, machine learning, natural language processing, Reinforcement learning, Q-learning, Intelligent agents, Various search algorithms, etc.

Our AI tutorial is prepared from an elementary level so you can easily understand the complete tutorial from basic concepts to the high-level concepts.

In today's world, technology is growing very fast, and we are getting in touch with different new technologies day by day.

Here, one of the booming technologies of computer science is Artificial Intelligence which is ready to create a new revolution in the world by making intelligent machines.The Artificial Intelligence is now all around us. It is currently working with a variety of subfields, ranging from general to specific, such as self-driving cars, playing chess, proving theorems, playing music, Painting, etc.

AI is one of the fascinating and universal fields of Computer science which has a great scope in future. AI holds a tendency to cause a machine to work as a human.

Artificial Intelligence is composed of two words Artificial and Intelligence, where Artificial defines "man-made," and intelligence defines "thinking power", hence AI means "a man-made thinking power."

So, we can define AI as:

Artificial Intelligence exists when a machine can have human based skills such as learning, reasoning, and solving problems

With Artificial Intelligence you do not need to preprogram a machine to do some work, despite that you can create a machine with programmed algorithms which can work with own intelligence, and that is the awesomeness of AI.

It is believed that AI is not a new technology, and some people says that as per Greek myth, there were Mechanical men in early days which can work and behave like humans.

Before Learning about Artificial Intelligence, we should know that what is the importance of AI and why should we learn it. Following are some main reasons to learn about AI:

Following are the main goals of Artificial Intelligence:

Artificial Intelligence is not just a part of computer science even it's so vast and requires lots of other factors which can contribute to it. To create the AI first we should know that how intelligence is composed, so the Intelligence is an intangible part of our brain which is a combination of Reasoning, learning, problem-solving perception, language understanding, etc.

To achieve the above factors for a machine or software Artificial Intelligence requires the following discipline:

Following are some main advantages of Artificial Intelligence:

Every technology has some disadvantages, and thesame goes for Artificial intelligence. Being so advantageous technology still, it has some disadvantages which we need to keep in our mind while creating an AI system. Following are the disadvantages of AI:

Before learning about Artificial Intelligence, you must have the fundamental knowledge of following so that you can understand the concepts easily:

Our AI tutorial is designed specifically for beginners and also included some high-level concepts for professionals.

We assure you that you will not find any difficulty while learning our AI tutorial. But if there any mistake, kindly post the problem in the contact form.

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AI Tutorial | Artificial Intelligence Tutorial - Javatpoint

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.

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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 ...

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