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

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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Artificial Intelligence: A Modern Approach (3rd Edition …

Stuart Russell was born in 1962 in Portsmouth, England. He received his B.A. with first-class honours in physics from Oxford University in 1982, and his Ph.D. in computer science from Stanford in 1986. He then joined the faculty of the University of California at Berkeley, where he is a professor of computer science, director of the Center for Intelligent Systems, and holder of the SmithZadeh Chair in Engineering. In 1990, he received the Presidential Young Investigator Award of the National Science Foundation, and in 1995 he was cowinner of the Computers and Thought Award. He was a 1996 Miller Professor of the University of California and was appointed to a Chancellors Professorship in 2000. In 1998, he gave the Forsythe Memorial Lectures at Stanford University. He is a Fellow and former Executive Council member of the American Association for Artificial Intelligence. He has published over 100 papers on a wide range of topics in artificial intelligence. His other books include The Use of Knowledge in Analogy and Induction and (with Eric Wefald) Do the Right Thing: Studies in Limited Rationality.

Peter Norvig is currently Director of Research at Google, Inc., and was the director responsible for the core Web search algorithms from 2002 to 2005. He is a Fellow of the American Association for Artificial Intelligence and the Association for Computing Machinery. Previously, he was head of the Computational Sciences Division at NASA Ames Research Center, where he oversaw NASAs research and development in artificial intelligence and robotics, and chief scientist at Junglee, where he helped develop one of the first Internet information extraction services. He received a B.S. in applied mathematics from Brown University and a Ph.D. in computer science from the University of California at Berkeley. He received the Distinguished Alumni and Engineering Innovation awards from Berkeley and the Exceptional Achievement Medal from NASA. He has been a professor at the University of Southern California and a research faculty member at Berkeley. His other books are Paradigms of AI Programming: Case Studies in Common Lisp and Verbmobil: A Translation System for Faceto-Face Dialog and Intelligent Help Systems for UNIX.

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Top 12 Artificial Intelligence Tools & Frameworks | Edureka

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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AI Standards | NIST

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, see

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 Tutorial | Artificial Intelligence Tutorial – Javatpoint

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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Artificial Intelligence in India Opportunities, Risks …

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 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, 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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The Global Artificial Intelligence in Aviation Market is …

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) -- 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" -

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:

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

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 ...

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

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


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.


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.


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

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