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London’s PQShield raises 5.5 million seed to develop security solutions that match the power of quantum computing – Tech.eu

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PQShield, a London-based cybersecurity startup that specialises in post-quantum cryptography, has come out of stealth mode with a 5.5 million seed investment from Kindred Capital, Crane Venture Partners, Oxford Sciences Innovation and angel investors including Andre Crawford-Brunt, Deutsche Banks former global head of equities.

According to the startup, quantum computers promise an unprecedented problem for security, since they will be able to smash through traditional public-key encryption and threaten the security of all sensitive information, past and present. For that reason, the company is developing quantum-secure cryptography, advanced solutions for hardware, software and communications that resist quantum threat yet still work with todays technology.

Whether cars, planes or other connected devices, many of the products designed and sold today are going to be used for decades. Their hardware may be built to last, but right now, their security certainly isnt. Future-proofing is an imperative, just as it is for the banks and agencies that hold so much of our sensitive data, explains founder and CEO Dr. El Kaafarani,

The team, a spin out from Oxford University, is already working on commercialisation and roll-out as well. Its System on Chip (SoC) solution, built fully in-house, will be licensed to hardware manufacturers, while a software development kit will enable the creation of secure messaging apps protected by post-quantum algorithms. Bosch is already a customer.

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VFX Supervisor Andrew Whitehurst Grapples With The Intricacies Of Quantum Physics On Sci-Fi Thriller Devs – Deadline

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On sci-fi thriller Devs, VFX supervisor Andrew Whitehurst reteamed with director Alex Garland for an exploration of the multiverse, digging into scientific literature to depict a world of the near future, and the technology that accompanied it.

Starring Sonoya Mizuno, the series centers on Lily, a software engineer for a quantum computing company in the Bay Area, who investigates a secretive development division within her company, following the mysterious disappearance of her boyfriend.

An Oscar winner known for films including Ex Machina and Annihilation, Whitehurst began conversations on Devs while the latter film was being finished. [Alex and I] were talking a lot during the period of him writing it, because we both have a shared interest in quantum physics, and the idea of multiverses. I was being sent episodes as they were being written, and discussing what he was about to go and write before he was writing it, Whitehurst says. So, it was probably the most involved Ive ever been in that part of a production, which is lovely.

In early conversations with Garland, Whitehurst understood that visual effects would play out in two branches throughout the show. What art departments cant build, we would have to augment or extend, or in some cases, replace. So, theres that sort of invisible worldbuilding aspect to it, which we knew we were going to have to do, because the scope of the vision was so big, he explains. We knew our art department would do something amazing, but we were going to be in the business of making the world complete.

From Whitehursts perspective, the other of the two aforementioned branches was much more creatively driven, representing a singular kind of challenge. Essentially, in his work on Devs, Whitehurst would have to visualize life inside a multiverse. Secondly, he would have to craft outputs, or visualizations, emerging from a quantum computer at Devsthe development division that gives the series its name. Created by obsessive scientists Forest (Nick Offerman) and Katie (Alison Pill), this machine has the ability to predict the future, and visually project into the past, presenting grainy depictions of such figures as Jesus Christ and Joan of Arc.

Prior to production, Whitehurst turned to the writing of physicist David Deutschas he often has throughout his careerfor insights that might inform the visual effects at hand. He wrote an amazing book more than 20 years ago called The Fabric of Reality, which is something that I reread semi-regularly, he says. His notion of trying to come up with this theory of everything that can describe, using scientific ideas, this whole universe, was something that was very appealing, as a philosophical basis to build off.

On a practical level, the VFX supervisor experimented early on with the way he would manifest a multiverse, and the quantum computers visualizations, recognizing that the choices he made would have a direct impact on the way the show was shot. For the multiverse stuff, we needed to know what we were aiming for the finished effect to look like, so we knew what to shoot on set to be able to do that. Then, with the visualizations that you see on the screens inside the [Devs] cube, we were hoping to be able to, and ultimately were able to, project most of that footage live on set, when you were actually shooting those scenes, so that it could act as a light source, Whitehurst explains. It gave the actors something to react to; it gave [DP] Rob [Hardy] something to frame up on.

When it came to multiverse footagewhich featured multiple versions of an actor on screenWhitehurst engaged in a series of tests, shooting various versions of people doing very similar actions, before blurring them, and layering them together. That had this very Francis Bacon look to it, which was kind of cool. But it didnt describe the idea of many different worlds clearly enough. So, that was an iterative process, the artist reflects. We ended up going, Look. The way that we should do this, that we should represent the many worlds, is by being able to see each distinct person in their own world of the multiverse. And were just going to layer that together.

In the design process for the visualizations, Whitehurst asked himself, how would the quantum computer visually generate a world for people to look at? Again, we went through a lot of different ideas of building it up in blocks, or building it up as clouds. And ultimately, the way that modern computer renderers work, which is the piece of software that generates our CG pictures, is that it works by doing continually refining passes, he explains. So, when you say, Render me this scene, the first thing youre presented with is this very sandy, rough version of the image, and then it gets slightly less rough, and slightly less rough, and the sandiness goes away, and it becomes clearer, and clearer, and clearer.

For Garland and his VFX supervisor, this understanding of real-world rendering lent itself to an interesting visual ideaand so over the course of Devs, we see that the computer is getting better at creating its images over time. We took that idea, and we actually ended up coming up with this sort of 3D volume of these points drifting around, as if they were little motes of dust suspended in water. The computer is generally coaxing these points to be specific objects in a certain space, and as they get better and better at it, the points become denser, and the object becomes clearer and clearer, Whitehurst says. That ended up being a narratively satisfying approach to designing that visual effect, but also it had a real aesthetic quality to it, as well. So, that was kind of a double win for us, really.

The visuals that appear on the massive Devs screen were all first photographed as plates, which would serve as a base for Whitehursts creations. We had a performer to be Joan of Arc, and we had a series of actors to be Lincoln, and the other people at the Gettysburg Address. Those were filmed in a car park at Pinewood [Studios], and then we would track those, and isolate them, so that we could put them into three-dimensional space, the VFX supervisor says. Then, we would create digital matte painting environments, and we were able to build up this scene, which had depth, which we could then, using the simulation software that wed developed, push these points around, so that they could attempt to try and stick themselves to the forms of these people. And the amount that they stuck to that form determined how clear they were.

In terms of the invisible worldbuilding Whitehurst tackled for the series, one of the biggest challenges, and most distinct examples, was the Devs cubethe beautifully futuristic center of the development divisions operations. Encased in reflective golden walls, the cube was an office, which workers entered into, by way of a floating capsule on a horizontal path.

Art departments were constrained by the size of the biggest soundstage that we could find, which happened to be in Manchester. What they were able to build was the office level of the floating cube, the gold walls that surround it, the gap in between, and a glass capsule, which was mounted on a massive steel trolley that could be pushed backwards and forwards by grips, Whitehurst shares. But everything thats above and below that had to be a visual effect. Then, any angles where you were particularly low, looking up, or particularly high, looking down, also had to be full visual effect shots, because you couldnt get the camera that high or that low, because of the constraints of the space.

Most dialogue scenes within the Devs cube were realized in-camera, given that the camera department was following people on the office floor, with a level lens. But basically, anything thats above or below the office floor in that environment is digital, the VFX supervisor notes. And obviously, you had to paint out the trolley that the capsule was on, and replace that section of the environment with a digital version.

Another impressive example of the series VFX worldbuilding was the massive statue of Amaya, which towered over the redwood trees on the Devs campus. Present very little on screen, this little girl is more of a specteran absence that permeates and haunts the world of Devs. That [statue] was fully CG, Whitehurst says. The location that its sat in is the amphitheater at the University of California, Santa Cruz. So, they had a stage area, and its like, Well, the statue will be standing on that.

Taking into consideration the environment in which the statue would stand, Whitehurst then had to consider in depth how it would look. We did a photogrammetry session, which is where you are able to take multiple photographs instantaneously of a subjectin this case, the little girl. From that, you can build a 3D model. So, its a sort of snapshot in time that you can then create into something 3D, the VFX supervisor says. We used that as the basis of our digital sculpt then to make the statue, and then we went through a long process of, Well, should this be a piece of pop art? Should it have a sort of Jeff Koons quality to it? Or should we go for something that feels like its made out of concrete?

We tried a whole bunch of different surfacing approaches, and how would it catch the light if it was made of concrete, or if it was enamel paint, and eventually, the pop art approach felt narratively the most appropriate, he adds. So, thats what we ended up going with.

For Whitehurst, there were a great number of creative challenges in designing visual effects for Devs. Certainly, I think the complexity of some of the environmentsso, the cube with the permanently shifting lighting on it, where were having to match all of those lighting changeswas very tricky. Getting this sort of aesthetic balance in things like the visualizations, making it feel something that felt scientifically plausible, but also had a sense of beauty. And how much should we allow the audience to see, and how mysterious should it be? he says. That sort of thing was complex.

The series was also notable for Whitehurst, given that it was the first he had ever taken on. Most of us working on the series come from a film background. But I think the key thing that is most exciting about it, and particularly for someone like Alex, who is so big-ideas-driven, and writes characters so well, is having something where you get to spend more time with those characters, he says. You really get to flesh out and develop those big ideas, which is something that all of the rest of us working on it can help with.

The other highlight is, I got to work with some of my favorite people, again, for the third time, Whitehurst adds. So, it was an exciting mixture of very familiar, in terms of most of the people I was working with, and something excitingly new at the same time.

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VFX Supervisor Andrew Whitehurst Grapples With The Intricacies Of Quantum Physics On Sci-Fi Thriller Devs - Deadline

QCI Hosts Webinar Series Featuring Optimizations that Deliver Quantum-Ready Solutions at Breakthrough Speed – Stockhouse

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LEESBURG, Va., July 08, 2020 (GLOBE NEWSWIRE) -- Quantum Computing Inc. (OTCQB: QUBT) (QCI), a technology leader in quantum-ready applications and tools, has introduced a new series of free webinars featuring the company’s Mukai quantum computing software execution platform and how it can solve real-world, constrained-optimization problems at breakthrough speed.

Session 1: The Value of QuOIR Running on the Mukai Platform; Use Cases and Examples Date: Tuesday, July 14 Time: 12 noon Eastern time (9:00 a.m. Pacific) Topics: This session will focus on the different ways Mukai can solve a variety of complex, real-world optimization problems faced by nearly every major company and government agency worldwide, including those involving logistics routing, drug design, and manufacturing scheduling.

The presenters will also review a recently published benchmark study showing how Mukai delivers superior performance for an important constrained-optimization problem compared to other solvers, producing best-in-class quality of results, time-to-solution and diversity of solutions running quantum computing software tools on classical computers (Intel® and AMD processor-based) .

Participants will learn about how the QuOIR constrained-optimization layer of the Mukai platform makes it easier to achieve this superior performance by automatically creating a QUBO that meets constraints as well as finds an optimal solution.

Sign up today to attend this event and discover how Mukai has brought us to the day when quantum-ready methods on classical systems can achieve greater performance compared to traditional classical methods.

Register today for Session 1 by clicking here.

Session 2: The Mukai How To’ Webinar Date: Tuesday, July 21 Time: 12 noon Eastern time (9:00 a.m. Pacific) Topics: This session will dive deeper into the functions and features of the Mukai quantum computing software execution platform, focusing on how developers and organizations can migrate their existing applications to quantum-ready solutions today and realize superior performance even when running their solutions on classical computers.

Participants will learn how they can get started with their free trial of Mukai, which the company officially launched last week. Learn how to use the Mukai API for calling a proprietary set of highly optimized and parallelized quantum-ready solvers that can execute on a cloud-based classical computer infrastructure and deliver differentiated performancefor many quantum-ready algorithms.

Mukai’s comprehensive software suite enables developers to create applications that can benefit from quantum advantage without needing to learn how to create quantum gate circuitsor create and embed a QUBO.

While quantum computing is typically a high-dollar investment given the sophisticated and costly hardware requirements, Mukai makes quantum application development affordable and scalable compared to running solutions on intermediate quantum computers, like those offered by D-Wave, Fujitsu, IBM and Rigetti.

Sign up today to attend this event and learning how Mukai’s unique functionality and breakthrough in performance has eliminated one of the greatest obstacles to the development and adoption of quantum-ready applications.

Register today for Session 2 by clicking here.

Your Webinar Host Steve Reinhardt, QCI’s VP of product development, will host the webinars. Recognized for being among the handful of top quantum software experts in the world, Reinhardt has built hardware and software systems that have delivered new levels of performance and analytic capability using conceptually simple interfaces. This includes Cray Research T3E distributed-memory systems, ISC Star-P parallel-MATLAB software, YarcData/Cray Urika graph-analytic systems, and apps and tools for D-Wave Systems’ annealing-based quantum computers.

Reinhardt has focused on graph analytics since 2003, developing graph-analytic core software and using it to solve end-user problems, particularly in cybersecurity. He currently leads the QCI product development team which is delivering today on the value proposition of quantum-ready applications and tools.

To learn more about the trial or webinars, please feel free to contact John Dawson at trial@QuantumComputingInc.com. You can also submit your inquiry here.

About Quantum Computing Inc. Quantum Computing Inc. (QCI) is focused on developing novel applications and solutions utilizing quantum and quantum-ready computing techniques to solve difficult problems in various industries. The company is leveraging its team of experts in finance, computing, security, mathematics and physics to develop commercial applications for industries and government agencies that will need quantum computing power to solve their most challenging problems. For more information about QCI, visit http://www.quantumcomputinginc.com.

Important Cautions Regarding Forward-Looking Statements This press release contains forward-looking statements as defined within Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934, as amended. By their nature, forward-looking statements and forecasts involve risks and uncertainties because they relate to events and depend on circumstances that will occur in the near future. Those statements include statements regarding the intent, belief or current expectations of Quantum Computing (Company”), and members of its management as well as the assumptions on which such statements are based. Prospective investors are cautioned that any such forward-looking statements are not guarantees of future performance and involve risks and uncertainties, and that actual results may differ materially from those contemplated by such forward-looking statements.

The Company undertakes no obligation to update or revise forward-looking statements to reflect changed conditions. Statements in this press release that are not descriptions of historical facts are forward-looking statements relating to future events, and as such all forward-looking statements are made pursuant to the Securities Litigation Reform Act of 1995. Statements may contain certain forward-looking statements pertaining to future anticipated or projected plans, performance and developments, as well as other statements relating to future operations and results. Any statements in this press release that are not statements of historical fact may be considered to be forward-looking statements. Words such as may,” will,” expect,” believe,” anticipate,” estimate,” intends,” goal,” objective,” seek,” attempt,” aim to”, or variations of these or similar words, identify forward-looking statements. These risks and uncertainties include, but are not limited to, those described in Item 1A in the Company’s Annual Report on Form 10-K, which is expressly incorporated herein by reference, and other factors as may periodically be described in the Company’s filings with the SEC.

Mukai and QuOIR are trademarks of Quantum Computing Inc. Intel® is a trademark of Intel Corporation.

Company Contact Robert Liscouski, CEO Tel (703) 436-2161 info@quantumcomputinginc.com

Investor & Media Relations Contact Ron Both or Grant Stude CMA Investor Relations Tel (949) 432-7566 Email Contact

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QCI Hosts Webinar Series Featuring Optimizations that Deliver Quantum-Ready Solutions at Breakthrough Speed - Stockhouse

Quantum Computing Technologies Market to Witness a Pronounce Growth During 2025 – News by aeresearch

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The research report on Quantum Computing Technologies market report consists of a thorough assessment of this industry domain. As per the report, the market is expected to generate notable revenue and display a remunerative growth rate during the analysis timeframe.

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Also, the research report summarizes the impact of COVID-19 pandemic on the growth the Quantum Computing Technologies market.

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The report comprises of a thorough analysis of the geographical landscape of the Quantum Computing Technologies market and segments it into North America, Europe, Asia-Pacific, South America, Middle East & Africa, South East Asia.

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The research process begins with internal and external sources to obtain qualitative and quantitative information related to the Quantum Computing Technologies market. It also provides an overview and forecast for the Quantum Computing Technologies market based on all the segmentation provided for the global region. The predictions highlighted in the Quantum Computing Technologies market share report have been derived using verified research procedures and assumptions. By doing so, the research report serves as a repository of analysis and information for every component of the Quantum Computing Technologies market.

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Quantum Computing Technologies Market to Witness a Pronounce Growth During 2025 - News by aeresearch

Cyber Reliant announces another ground- breaking innovation with their development of their Mobile Data Defender Quantum Resistant Secure Voice and…

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Quantum Resistant Secure Voice and Chat

ANNAPOLIS, Md. (PRWEB) July 09, 2020

Traditional secure voice and chat products rely heavily on a combination of limited-use specialized devices and complex to manage network and cloud security. These complexities are not cost effective, deliver a non-commercial user experience and increase data breach exposure risk.

With Cyber Reliants breakthrough secure voice and chat innovation, sensitive and confidential communication can be conducted on any commercially available Android and iOS device thus eliminating the need for a limited-use specialized device with full privacy and security on any network while removing the risk of electronic eavesdropping and exploitation.

Cyber Reliant has released its early access program to specific security and privacy focused government and commercial industry test users for quantum resistant voice and chat. Adopting quantum resistance methods is essential for ensuring full spectrum data security and privacy. Cyber Reliants quantum resistant secure voice and chat provides a level of security such that if an attacker had unlimited computing power, as in a weaponized quantum computer, they still could not compromise the data protection methods employed by Cyber Reliant. These quantum resistant techniques are incorporated directly into the data, thus eliminating the risk of data breaches based on network intrusions, malware, or electronic eavesdropping.

The Cyber Reliant Core transforms voice/chat packets at creation time into randomized, quantum resistant data shreds across any network protocol including cellular, Wi-Fi, or even satellite networks. The voice and chat solutions are implemented as an application on Android or iOS platforms and does not require a specialized mobile device or network enabled router to operate fully secure. Even on a heavily surveilled or compromised network, this technique eliminates the threat of encryption key theft and other exploitation methods.

In an upcoming release, Cyber Reliant will incorporate its ground-breaking advancement in True Random Number Generation. This novel approach uses a patent-pending process without the need for a specialized chipset, and utilizes sensors on standard commercial devices to create provable true random encryption algorithms that strengthen key generation methods, ensuring complete quantum resistant end to end data security. This advancement overlays on legacy mobile devices that have chip-based True Random Number Generators already built-in to the device, and makes it possible for future device optionality and diversity, reducing expense and build complexity while increasing security to combat today and tomorrows threats.

Cyber Reliant initially created the Secure Voice and Chat solution to serve the tactical soldier in mission critical environments, but the need for future-proof data protection in commercial and enterprise markets quickly became clear.

Today, Cyber Reliant offers NSA Commercial Solutions for Classified (CSfC) up to Top Secret file-level data protection software for federal and commercial data privacy solutions and is natively compliant with major regulatory requirements including HIPAA, GDPR, FINRA, PCI DSS, PII, PHI, PAN, NPI, GLBA, FERPA, CCPA and more.

"Cyber Reliants mission has always been to stay one step ahead of the ever-changing security threat landscape through secure-by-design principles that eliminate the attack surface. Our latest quantum resistant innovation delivers the most advanced level of protection available to all sensitive data assets and device types across customer verticals in healthcare, financial services, defense and aerospace, said Rick Bueno, founder and CEO.

Cyber Reliant:Battle Hardened, Business Ready

About Cyber Reliant: Founded in 2010 with a mission to provide critical data protection to the DoD and Intelligence community, Cyber Reliant has become a leading quantum resistant solution provider in the data privacy market for both Government and Commercial clients. Cyber Reliants premier data protection is a quantum resistant software solution authorized by NSA Commercial Solutions for Classified (CSfC) to securely store the Nations most sensitive classified information up to Top Secret on virtually any platform, to include commercially available mobile devices and IoT. Our cutting-edge and patented process of encryption, data shredding and file reconstitution integrates seamlessly with existing technology, provides a commercial user experience with no perceptible latency and demonstrates significant value across both the Federal government and commercial markets.

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Cyber Reliant announces another ground- breaking innovation with their development of their Mobile Data Defender Quantum Resistant Secure Voice and...

Artificial Intelligence Tutorial Learn AI from Experts …

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Overview of Artificial Intelligence

Today, a few applications of artificial intelligence seem to bring us closer to the future. The most convincing pieces of evidence are self-driving cars, Google Translate, and Sophia (humanoid robots). Moreover, have you ever wondered how Cyborg technology (a technique for creating artificial body parts for people with disabilities that enables them to function normally) works? If yes, this Artificial Intelligence tutorial will give you an introduction to AI right from the basics.

In this Artificial Intelligence tutorial, we shall be coveringMachine Learning, Deep Learning, neural networks, real-life applications of Artificial Intelligence, Python and various packages available in it, TensorFlow, Keras, multilayer perceptron, convolution neural networks, recurrent neural networks, long short-term memory, OpenCV, and much more.

Learn for free ! Subscribe to our youtube Channel.

Creativity and ideas never end as they are limitless. Likewise, there are a lot more things to create, improve, implement, and invent in the field of Artificial Intelligence. Evidently, AI is far from reaching its saturation level of creating new things.

In short, here are the goals of Artificial Intelligence:

Become a master of Artificial Intelligence by going through this onlineArtificial Intelligence Course in London!

Artificial Intelligence is the intelligence that machines demonstrate. It allows us to create machines that can perform multiple tasks and solve real problems without error. As a matter of fact, AI can improve efficiency and productivity by automating repetitive tasks. Additionally, it can create an immersive, responsive experience and understand human emotions.

This Artificial Intelligence tutorial will teach you all the techniques that you will have to implement in real life.

Artificial intelligence machines have the ability to make decisions and when exposed to large amounts of real-world data, they try to learn and improve themselves. To illustrate this, here are some practical applications of artificial intelligence:

Now, in this Artificial Intelligence tutorial, we will head on to the subsets of Artificial Intelligence.

Artificial Intelligence is an umbrella term. There are two subsets of Artificial Intelligence: Machine Learning and Deep Learning.

Machine learning is a branch of artificial intelligence in which a program or machine uses a set of algorithms to find patterns in the dataset(s). Above all, we dont have to write individual instruction for every action. As machine learning models capture more and more data, they become smarter and self-improving.

Further, Machine Learning can be sub-categorized into three subsets:

Now, moving ahead with this Artificial Intelligence tutorial, we will look at some applications of Machine Learning.

Since Artificial Intelligence and Machine Learning make our lives better, it is very satisfying to learn these. This Artificial Intelligence tutorial will teach you everything you need to know about the basics of Artificial Intelligence.

Further development of machine learning has led to a different sub-category, i.e., Deep Learning. Deep Learning makes use of artificialneural networks that consist of layers of networks working on different parameters to give the desired output.

If you are preparing for an Artificial Intelligence job interview, go through thistop Artificial Intelligence Interview Questions and Answers!

Artificial Intelligence is about incorporating the human abilities to a machine by designing algorithms such that these algorithms involve self-learning and provide the machine with the ability to think like the way humans do, by which the machine would be able to solve problems without explicit human inputs.However, a lot of creativity and computation is required to make it a successful creation.

Artificial Intelligence is gaining immense popularity because it has a lot of things to explore and create around the world. The applications of Artificial Intelligence that we come across in our day-to-day life are just a small demonstration of AIs wonder-struck start.

Interested in learning Artificial Intelligence? Learn more from thisArtificial Intelligence Course in New York!

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This Artificial Intelligence tutorial has been prepared to help you learn Artificial Intelligence the right way, and it is meant for the beginners and for the professionals to help them understand basic-to-advanced concepts related to AI. This Artificial Intelligence tutorial will help you master AI with which you will be able to take yourself to a higher level of expertise for implementing Artificial Intelligence concepts in real life.

Before going through this Artificial Intelligence tutorial, you should have a fundamental knowledge of the field of Information Technology, along with being familiar with computers, the Internet, and basic working knowledge on data. Such basics will help you understand the AI concepts better and will move you faster on the learning path.

This AI tutorial, further it its pages, covers the introduction to AI, the history, goals, and the applications of AI, AI vs ML vs DL, various data science packages, artificial neural networks, back-propagation algorithm, multilayer perceptron, the problems of overfitting and underfitting, convolution neural networks, recurrent neural networks, long short-term memory, various Machine Learning concepts, and OpenCV.

If you have any technical doubts or queries related to AI, post the same on IntellipaatsAICommunity!

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Artificial Intelligence Tutorial Learn AI from Experts ...

Artificial Intelligence (M.A.S.) | Illinois Institute of …

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Whether you are a recent graduate or career professional, keep up to date, deepen, and extend your knowledge of artificial intelligence while building your competitive edge in business, industry, or government. A Master of Artificial Intelligence from Illinois Tech will give you this rigorous and practical education in artificial Intelligence and its subfields of machine learning, deep learning, computer vision, natural language processing, probabilistic reasoning, and data analytics.

At Illinois Tech, master the theoretical concepts of AI and gain experience applying them through course and research projects analyzing financial data, medical images, social media, and more.

At the forefront of technology and just minutes from downtown Chicago, Illinois Tech is ideally positioned to launch you into an exciting career in artificial intelligence. Join us!

Please note that some programs designated as online require in-person coursework such as lab, classroom participation, or a thesis. As such, all courses will be offered fully online for the fall 2020 semester, however, some courses in subsequent semesters may require in-person attendance.

Master the theoretical concepts and practical applications of artificial intelligence, machine learning, and data analytics. In Illinois Techs Master of Artificial Intelligence program, learn how you can make a positive impact with the knowledge of AI.

Artificial intelligence is being implemented in almost all fields of industry into everyday operations. Whether it is analyzing risk management in corporate finances, finding tumors in medical images, building an autonomous vehicle to explore other worlds, or designing a horrible supervillain for the next hot video game, AI is being used to solve problems in all corners of society.

A full-time student whose bachelors degree is in computer science can complete the MAS-AI program in three semesters plus a summer course. A student without a bachelor's degree in computer science may require extra time inprerequisite undergraduate coursework.

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Applicants must have a bachelor's degree, although not necessarily in computer science or related field of study, with an overall GPA of 3.0/4.0.

GRE scores of a minimum of 305 are required.

For those without a bachelor's degree in computer science, prerequisite undergraduate coursework with gradesof Bor better in Accelerated Computer Science or Object-Oriented Programming I and II, and Introduction into Advanced Studies I and II, is required.

TOEFL/PTE/IELTS scores may be required for some applicants.

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10 Best Artificial Intelligence Courses Online in 2020 …

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Artificial Intelligence is the big thing in the world of technology. From self-driving cars to automated robots to NPCs in our games to simple things like AI processing of images in our smartphones, Artificial Intelligence is now entrenched in our daily lives. And if you are looking to have a long future as a developer and get the highest paying jobs, you need to have the required skills to work in the AI field. Thankfully, there are tons of online courses that can not only get you started on AI but also help you become a professional AI developer. To make your life easier, we have listed the 10 best Artificial Intelligence courses online that can help you in your learning journey.

In this article, we have mentioned beginners, intermediate, and advanced Artificial Intelligence courses. So no matter where you are in your Artificial Intelligence learning journey, you will find a course on AI that will help you improve your skills. With that said, lets dive into our best AI learning courses list, shall we?

This course on Artificial Intelligence will teach you to combine the power of Data Science, Machine Learning, and Deep Learning to create powerful AI for real-world applications. This is a big course with 16.5 hours of video lectures, 17 articles, and 1 downloadable resource. Once you complete this course you will also get a Certificate of Completion which you can use in your next job hunting endeavor. Before you start this course, you should know that it requires you to have a basic knowledge of High School Mathematics and the Python language. If you dont know Python, there are several courses on Python that can get you started.

What I love about this course is that it focuses on teaching you real-world application of AI. Going through this course you will build an AI, create a virtual self-driving car, understand the theory behind AI, make an AI to beat games, and more. Also, since this course is on Udemy, you can buy it once and have lifetime access for it. Theres no subscription pricing involved so it is quite light on your wallet. If you are thinking of starting the AI journey, this is the course you should check out. After all, it is one of the best if not the best Artificial Intelligence courses online.

Course Rating: 4.4 (Rated by 11,783 students)

Difficulty level: Beginner to Advanced (requires basic knowledge of Python and high school mathematics)

Buy Course on Udemy: Starting at $13.99

Another great course to help you kick-start your AI learning journey is the Artificial Intelligence Masterclass on Udemy. In this course, you will learn to develop a powerful Artificial intelligence model based on a robust Hybrid Intelligence System. The course cover topics on fully-connected neural networks, recurrent neural networks, AutoEncoders and Variational AutoEncoders, evolution strategies, and more. To help you learn all these concepts, you get 12 hours of step-by-step video guide and the full roadmap which will help you build your own Hybrid AI Model from scratch.

The course also provides a toolkit and that you can use to build hybrid intelligent systems. You also get a Certificate of Completion which comes handy when hunting for a job. The course is great but before you take it you should know its basic requirements. You need to have a working knowledge of high school mathematics and previous coding experience. You dont need to be an expert coder to start this course but this is also not meant for someone who is just starting to code. If you want to build hybrid intelligent systems, then this is one of the best online Artificial Intelligence courses that you can find right now.

Course Rating: 4.5 (Rated by 478 students)

Difficulty level: Intermediate to Advanced (requires basic knowledge of coding and high school mathematics)

Buy Course on Udemy: Starting at $13.99

One of the biggest applications of AI is in games. From generating random maps and levels to creating interactive NPCs (Non-Playable Characters / Non-Player Characters), AI handles a large portion of our games today. If the gaming industry is where you want to apply your Artificial Intelligence skills then this is the course for you. The main purpose of this course is to provide you with a practical guide to program non-player characters for games. The course focuses on developing NPCs in the Unity engine which is a popular gaming engine used for developing games across multiple operating systems.

In this course, you will learn to design and code NPCs in C#. The course will give you a detailed explanation on the use of AI in games and teach you to implement AI-related Unity Asset plugins into existing projects. Finally, you will also learn to work with a variety of AI techniques for developing navigation and decision making abilities in NPCs. The course has 9 hours of video content with 12 articles and 62 downloadable materials. Like other Udemy courses, you can buy it once for lifetime access and will receive a Certificate of Completion after you are done with the course.

Course Rating: 4.2 (Rated by 1242 students)

Difficulty level: Intermediate to Advanced (Requires familiarity with C# and the Unity Game Development Engine)

Buy Course on Udemy: Starting at $13.99

Stanford University is one of the top-most universities in the world and was ranked third in Times Higher Education ranking of universities across the globe. If thats not enough, you should know that this course is taught by Andrew Ng who was the founder of Googles deep learning research unit, Google Brain, Head of AI at Baidu, and is currently the CEO/Founder of Landing AI. The course on AI and ML that we are featuring is available completely online that means you dont have to be physically present at the university to take classes. That also means that it is way cheaper than attending school. The course covers AI and ML from the basics to advanced level so you can get started with your Artificial Intelligence journey. It provides a broad introduction to machine learning, data mining, and statistical pattern recognition.

Its a vast course with 56 hours of content that is spaced over 10 weeks. It starts with basic topics such as Linear Algebra, Linear Regression with Multiple Variables, Logistic Regression, and more and then move onto advanced lectures on Neural Networks, application of Machine Learning, Unsupervised Machine Learning, and more. Not only that, you also get graded assignments and quizzes that will help you go through this course and solve your doubts. Finally, you will also get an electronic certificate of completion from Stanford University which has its value. This is one of the best developed online courses on Artificial Intelligence and you should not ignore it.

Course Rating: 4.9 (Rated by more than 118,348 students)

Difficulty level: Advanced

Subscribe on Coursera: $49/month (financial aid available)

If you want to take a university course like the one featured above but cannot afford to pay for it then you should take this Machine Learning course provide by the Artificial Intelligence faculty of Columbia University. The course is available on edX which provides university-level courses for free. You only have to pay if you want the certificate from University. If you just want to learn then the course is free which is great for users who dont have that kind of cash. Talking about the course, it focuses on AI and ML models and methods and teaches you to apply them to real-world situations ranging from identifying trending news topics to building recommendation engines, ranking sports teams, and plotting the path of movie zombies.

The course takes 12 weeks to complete with 8-10 hours of work required every week. The topics include classification and regression, clustering methods, sequential models, matrix factorization, topic modeling and model selection, and more. The course focuses on both supervised and unsupervised learning techniques so you develop an all-around knowledge or Artificial Intelligence and Machine Learning. Its one of the best courses on Artificial Intelligence online right now.

Course Rating: No course ratings available at edX

Difficulty level: Advanced

Enroll on edX: Free, $375 for Electronic Certificate from Columbia University

Another good course on Artificial Intelligence comes from IBM, a company that has been at the forefront of AI innovation. After you have completed this course you will have a complete understating of Machine Learning and Deep Learning and you will be able to apply them in your professional projects. The course covers the fundamental concepts of Machine Learning and Deep Learning, including supervised and unsupervised learning. You will learn to use multiple ML and DL libraries such as SciPy, ScikitLearn, Keras, PyTorch, and Tensorflow and apply them to solve problems involving object recognition and Computer Vision, image and video processing, text analytics, and more.

One of my favorite things about this course is that it not only teaches you but also makes you apply the knowledge through a system of hands-on projects. You can use these projects to both build your skills and develop your portfolio which will help you during job hunting. You will also earn a certificate that will be recognized by various professional hiring partners that can help you land good jobs. If you want to work in the AI industry this is one of the best Artificial Intelligence courses online that you can take.

Course Rating: No Ratings Available

Difficulty level: Advanced

Subscribe on Coursera: $49/month (financial aid available)

One of the lesser-known applications of Artificial Intelligence is in the field of business where it is used to optimize workflow and processes to enhance business operations and solve real-world problems. If that is what you are interested in then this is the online Artificial Intelligence course for you. Available on Udemy, the course comes with 15 hours of on-demand video lectures, 16 articles, and 1 downloadable resource to help you learn AI for Business. Once you buy it, you can access life allowing you to learn the course at your pace.

Talking about the course structure it starts with the basics of business optimization AI by teaching you how to build an optimization model powered by AI. Then you will learn to use Artificial Intelligence to improve various business processes such as maximizing efficiency, minimizing costs, maximizing revenue, implementing Deep-Q learning, and more. In the process, you will understand how to build an artificial brain and master the general AI framework. This is a great course on AI for anyone who is looking to introduce AI enhancements in their business.

Course Rating: 4.6 (Rated by 1,145 students)

Difficulty level: Beginner (requires basic knowledge of Python and high school mathematics)

Buy Course on Udemy: Starting at $11.39

Microsoft is one of the leading companies that is pushing AI forward. Its doing that by not only using and evolving AI in multiple fronts of its business but also by providing a complete course on AI which anyone can take to start their AI journey. This is a vast course that requires 2-4 months and in total 224-356 hours of effort. It is so vast because its not a single course but consists of 11 courses all of which combine will take you from AI novice to a professional AI developer.

The course starts by introducing you to Python for Data Sciences and then move onto topics such as Deep Learning, Natural Language Processing, Reinforcement Learning, Computer Vision and Image Analysis, Principles of Machine Learning and more. I love that theres even a section on ethics of AI which is something many courses forget to include. After completion of the course, you will receive a certification course from Microsoft which you can use when applying for AI jobs. Overall, I find this to be one of the most complete online course on Artificial Intelligence.

Course Rating: No course ratings available at edX

Difficulty level: Advanced

Enroll on edX: Free (limited access) $980.10

This course provides a complete guide to Artificial Intelligence and prepares you for Deep Reinforcement Learning with Stock Trading Applications. This is an advanced course and requires prior knowledge of Calculus, Probability, Markov Models, Gradient descent, Python, and more subjects. If you are a beginner do not buy this course. With that out of the way, lets take a look at what this course has to offer, shall we?

Well, the course is quite short with only 9.5 half hours of video lectures. However, while the run-time is short, unpacking everything in the classes will take you a ton of time. In this course, you will learn to apply gradient-based supervised machine learning methods to reinforcement learning, understand reinforcement learning on a technical level, and implement 17 different reinforcement learning algorithms. if you want to get into reinforcement learning of AI, this is the course for you.

Course Rating: 4.6 (Rated by 5,528 students)

Difficulty level: Advanced

Buy Course on Udemy: Starting at $11.39

Another premier institute that is offering its Artificial Intelligence online course is MIT. Its AI Implications for Business Strategy course focuses on organizational and managerial implications of these technologies, rather than on their technical aspects. That means its not meant for developers rather for managers who want to pioneer the successful integration of AI in business. It is a 6-week online program (6-8 hours of work every week) that presents you with a foundational understanding of where we are today with AI and how we got here.

The course focuses on three prime AI technologies which include Machine Learning, Natural Language Processing, and Robotics. As I mentioned before the program is meant for managers and high-level executives and aims to help them effectively analyze, articulate, and apply key AI management and leadership insights in their work. So, if you find yourself in a position of instituting AI in your business this is the course you should take. The only drawback of this course is that its quite costly at $3,200. If you can afford to buy it, you should check it out.

Course Rating: 4.8

Difficulty level: Beginner to Advanced

Enroll on MIT Website: $3200

That ends our article on the best online courses on Artificial Intelligence. While I have tried my best to make this as beginner-friendly as possible, you will require a basic knowledge of coding (preferably Python) if you want to get started with AI. Artificial Intelligence is already dominating the core technology and its importance is only going to increase over time. So, check out these courses and select one to start your AI learning journey.

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10 Best Artificial Intelligence Courses Online in 2020 ...

Artificial Intelligence and Ethics – Markkula Center for …

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Brian Green is the assistant director of Campus Ethics at the Markkula Center for Applied Ethics. Views are his own.

Artificial intelligence and machine learning technologies are rapidly transforming society and will almost certainly continue to do so in the coming decades. This social transformation will have deep ethical impact, with these powerful new technologies both improving and disrupting human lives. AI, as the externalization of human intelligence, offers us in amplified form everything that humanity already is, both good and evil. Much is at stake. At this crossroads in history we should think very carefully about how to make this transition, or we risk empowering the grimmer side of our nature, rather than the brighter.

The Markkula Center for Applied Ethics recently joined the Partnership on AI to Benefit People and Society, and as an institution we have been thinking deeply about the ethics of AI for several years. In that spirit, we offer a preliminary list of issues with ethical relevance in AI and machine learning.

The first question for any technology is whether it works as intended. Will AI systems work as they are promised or will they fail? If and when they fail, what will be the results of those failures? And if we are dependent upon them, will we be able to survive without them?

For example, at least one person has already died in a semi-autonomous car accident because the vehicle encountered a situation in which the manufacturer anticipated it would fail and expected the human driver to take over, but the human driver didnt correct the situation.

The question of technical safety and failure is separate from the question of how a properly-functioning technology might be used for good or for evil (questions 3 and 4, below). This question is merely one of function, yet it is the foundation upon which all the rest of the analysis must build.

Once we have determined that the technology functions adequately, can we actually understand how it works and properly gather data on its functioning? Ethical analysis always depends on getting the facts first only then can evaluation begin.

It turns out that with some machine learning techniques such as deep learning in neural networks it can be difficult or impossible to really understand why the machine is making the choices that it makes. In other cases, it might be that the machine can explain something, but the explanation is too complex for humans to understand.

For example, in 2014 a computer proved a mathematical theorem called the Erdos discrepancy problem, using a proof that was, at the time at least, longer than the entire Wikipedia encyclopedia. Explanations of this sort might be true explanations, but humans will never know for sure.

As an additional point, in general, the more powerful someone or something is, the more transparent it ought to be, while the weaker someone is, the more right to privacy he or she should have. Therefore the idea that powerful AIs might be intrinsically opaque is disconcerting.

A perfectly well functioning technology, such as a nuclear weapon, can, when put to its intended use, cause immense evil. Artificial intelligence, like human intelligence, will be used maliciously, there is no doubt.

For example, AI-powered surveillance is already widespread, in both appropriate contexts (e.g., airport-security cameras) and perhaps inappropriate ones (e.g., products with always-on microphones in our homes). More obviously nefarious examples might include AI-assisted computer-hacking or lethal autonomous weapons systems (LAWS), a.k.a. killer robots. Additional fears, of varying degrees of plausibility, include scenarios like those in the movies 2001: A Space Odyssey, Wargames, and Terminator.

While movies and weapons technologies might seem to be extreme examples of how AI might empower evil, we should remember that competition and war are always primary drivers of technological advance, and that militaries and corporations are working on these technologies right now. History also shows that great evils are not always completely intended (e.g., stumbling into World War I and various nuclear close-calls in the Cold War), and so having destructive power, even if not intending to use it, still risks catastrophe. Because of this, forbidding, banning, and relinquishing certain types of technology would be the most prudent solution.

The main purpose of AI is, like every other technology, to help people lead longer, more flourishing, more fulfilling lives. This is good, and therefore insofar as AI helps people in these ways, we can be glad and appreciate the benefits it gives to us.

Additional intelligence will likely provide improvements in nearly every field of human endeavor, including, for example, archaeology, biomedical research, communication, data analytics, education, energy efficiency, environmental protection, farming, finance, legal services, medical diagnostics, resource management, space exploration, transportation, waste management, and so on.

As just one concrete example of a benefit from AI, some farm equipment now has computer systems capable of visually identifying weeds and spraying them with tiny targeted doses of herbicide. This not only protects the environment by reducing the use of chemicals on crops, but it also protects human health by reducing exposure to these chemicals.

One of the interesting things about neural networks, the current workhorses of artificial intelligence, is that they effectively merge a computer program with the data that is given to it. This has many benefits, but it also risks biasing the entire system in unexpected and potentially detrimental ways.

Already algorithmic bias has been discovered, for example, in areas ranging from criminal sentencing to photograph captioning. These biases are more than just embarrassing to the corporations which produce these defective products; they have concrete negative and harmful effects on the people who are victims of these biases, as well as reducing trust in corporations, government, and other institutions which might be using these biased products. Algorithmic bias is one of the major concerns in AI right now and will remain so in the future unless we endeavor to make our technological products better than we are. As one person said at a recent meeting of the Partnership on AI, We will reproduce all of our human faults in artificial form unless we strive right now to make sure that we dont.

Many people have already perceived that AI will be a threat to certain categories of jobs. Indeed, automation of industry has been a major contributing factor in job losses since the beginning of the industrial revolution. AI will simply extend this trend to more fields, including fields that have been traditionally thought of as being safer from automation, for example law, medicine, and education. Other than the job of AI developer, it is not clear what new careers these unemployed people will be able to transition into, and even AI programming may become at least partially automated in the future.

Attached to the concern for employment is the concern for how humanity spends its time and what makes a life well-spent. What will millions of unemployed people do? What good purposes can they have? What can they contribute to the well-being of society? How will society prevent them from becoming disillusioned, bitter, and swept up in evil movements such as white supremacy and terrorism?

Related to the unemployment problem is the question of how people will survive if unemployment rises to very high levels. Where will they get money to maintain themselves and their families? While prices may decrease due to lowered cost of production, those who control AI will also likely rake in much of the money that would have otherwise gone into the wages of the now-unemployed, and therefore economic inequality will increase.

Some people, including some billionaires like Mark Zuckerberg, have suggested a universal basic income (UBI) to address the problem, but this will require a major reconstruction of national economies. Various other solutions to this problem may be possible, but they all involve potentially major changes to human society and government. Ultimately this is a political problem, not a technical one, so this solution, like those to many of the problems described here, needs to be addressed at the political level.

If we turn over our decision-making capacities to machines, we will become less experienced at making decisions. For example, this is a well-known phenomenon among airline pilots: the autopilot can do everything about flying an airplane, from take-off to landing, but pilots intentionally choose to manually control the aircraft at crucial times in order to maintain their piloting skills.

Because one of the uses of AI will be to either assist or replace humans at making certain types of decisions (e.g. spelling, driving, stock-trading, etc.), we should be aware that humans may become worse at these skills. In its most extreme form, if AI starts to make ethical and political decisions for us, we will become worse at ethics and politics. We may reduce or stunt our moral development precisely at the time when our power has become greatest and our decisions the most important.

This means that the study of ethics and ethics training are now more important than ever. We should determine ways in which AI can actually enhance our ethical learning and training. We should never allow ourselves to become de-skilled and debilitated at ethics, or when our technology finally does present us with a problem we must solve we may be like frightened and confused children before a creation we do not understand.

Some thinkers have wondered whether AIs might eventually become self-conscious, attain their own volition, or otherwise deserve recognition as persons like ourselves. Legally speaking, personhood has been given to corporations and (in other countries) rivers, so there is certainly no need for consciousness even before legal questions may arise.

Morally speaking, we can anticipate that technologists will attempt to make the most human-like AIs and robots possible, and perhaps someday they will be such good imitations that we will wonder if they might be conscious and deserve rights and we might not be able to determine this conclusively. If future humans do conclude AIs and robots might be worthy of moral status, then we ought to err on the side of caution and fairness and give it.

In the midst of this uncertainty about the status of our creations, what we will know, though, is that we humans have moral characters and that, to quote Aristotle, we become what we repeatedly do. So we ought not to treat AIs and robots badly, or we might be habituating ourselves towards having flawed characters, regardless of the moral status of the artificial beings we are interacting with. In other words, no matter the status of AIs and robots, for the sake of our own moral characters we ought to treat them well, or at least not abuse them.

All of the above areas of interest will have effects on how humans perceive themselves, relate to each other, and live their lives. But there is a more existential question too. If the purpose and identity of humanity has something to do with our intelligence (as several prominent Greek philosophers believed, for example), then by externalizing our intelligence and improving beyond human intelligence, are we making ourselves second-class beings to our own creations?

This is a deeper question with artificial intelligence which cuts to the core of our humanity, into areas traditionally reserved for philosophy, spirituality, and religion. What will happen to the human spirit if or when we are bested by our own creations in everything that we do? Will human life lose meaning? Will we come to a new discovery of our identity beyond our intelligence? Perhaps intelligence is not as important to our identity as we might think it is, and perhaps turning over intelligence to machines will help us to realize that.

This is just a start at the exploration of the ethics of AI; there is much more to say. New technologies are always created for the sake of something good and AI offers us amazing new powers. Through the concerted effort of many individuals and organizations, we can hope to use AI to make a better world.

This article is an adaptation of a paper presented to the Pacific Coast Theological Society, November 3rd, 2017. A shorter draft was presented on October 24th, 2017, at Santa Clara University at a panel entitled AI: Ethical Challenges and a Fast Approaching Future. In the panel, I presented a list of nine areas of ethical concern; thanks to some helpful feedback I expanded the list to ten.

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Artificial Intelligence in India Opportunities, Risks …

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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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