Monthly Archives: May 2017

As well as beating us at board games, driving cars, and spotting cancer, artificial intelligence is now generating brand new sounds that have never been heard before, thanks to some advanced maths combined with samples from real instruments.

Before long, you might hear some of these fresh sounds pumping out of your radio, as the researchers responsible say they're hoping to give musicians an almost limitless new range of computer-generated instruments to work with.

The new system is called NSynth, and it's been developed by an engineering team called Google Magenta, a small part of Google's larger push into artificial intelligence.

"Learning directly from data, NSynth provides artists with intuitive control over timbre and dynamics and the ability to explore new sounds that would be difficult or impossible to produce with a hand-tuned synthesizer," explains the team.

You can check out a couple of NSynth samples below, courtesy of Wired:

NSynth takes samples from about a thousand different instruments and blends two together them together, but in a highly sophisticated way. First, the AI program learns to identify the audible characteristics of each instrument so they can be reproduced.

That detailed knowledge is then used to produce a mix of instruments that doesn't sound like a mix of instruments the properties of the audio are adjusted to create something that sounds like a single, new instrument rather than a mash of multiple sounds.

So instead of having a flute and violin play together, you've got a brand new, algorithm-driven digital instrument somewhere between the two. How much of the flute and how much of the violin are in the final sound is up to the musician.

Like many of Google's AI initiatives, NSynth relies on deep learning: a specific approach to AI where vast amounts of data can be processed in a similar way to the human brain, which is why these systems are often described as artificial neural networks.

So not only can deep learning systems use millions of cat pictures to correctly identify a cat, for instance, they can also learn from their mistakes and get better over time, teaching themselves how to improve just like our brains do.

The idea of deep learning has been around for decades, but we're only now seeing the kind of software and computing power appear that can make it a reality.

One consequence of that is that the NSynth demos built by the Google Magenta team all work in real time, allowing new compositions to be created.

Music critic Marc Weidenbaum told Wired that Google's new approach to the traditional trick of combining instruments together shows promise.

"Artistically, it could yield some cool stuff, and because it's Google, people will follow their lead," he said.

Google engineers have just been demoing NSynth at the Moogfest festival, and you can read a paper on their work at arXiv.

Read more:

Now artificial intelligence is inventing sounds that have never been heard before - ScienceAlert

Professional writing isnt easy. As a blogger, journalist or reporter, you have to meet several challenges to stay at the top of your trade. You have to stay up to date with the latest developments and at the same time write timely, compelling and unique content.

The same goes for scientists, researchers and analysts and other professionals whose job involves a lot of writing.

With the deluge of information being published on the web every day, things arent getting easier. You have to juggle speed, style, quality and content simultaneously if you want to succeed in reaching your audience.

Fortunately, Artificial Intelligence, which is fast permeating every aspect of human life, has a few tricks up its sleeve to boost the efforts of professional writers.

In 2014, George R. R. Martin, the acclaimed writer of the Song of Ice and Fire saga, explained in an interview how he avoids modern word processors because of their pesky autocorrect and spell checkers.

Software vendors have always tried to assist writers by adding proofreading features to their tools. But as writers like Martin will attest, those efforts can be a nuisance to anyone with more-than-moderate writing skills.

However, that is changing as AI is gettingbetter at understanding the context and intent of written text. One example is Microsoft Words new Editor feature, a tool that uses AI to provide more than simple proofreading.

Editor can understand different nuances in your prose much better than code-and-logic tools do. It flags not only to grammatical errors and style mistakes, but also the use of unnecessarily complex words and overused terms. For instance, it knows when youre using the word really to emphasize a point or to pose a question.

It also gives eloquent descriptions of its decisions and provides smart suggestions when it deems something as incorrect. For example if it marks a sentence as passive, it will provide a reworded version in active voice.

Editor has been well received by professional writers (passive voice intended), though its still far from perfect.

Nonetheless AI-powered writing assistance is fast becoming a competitive market. Grammarly, a freemium grammar checker that installs as a browser extension, uses AI to help with all writing tasks on the web. Atomic Reach is another player in the space, which uses machine learning to provide feedback on the readability of written content.

Writing good content relies on good reading. I usually like to go through different articles describing conflicting opinions about a topic before I fire up my word processor. The problem is theres so much material and so little time to readall of it. And things tend to get tedious when youre trying to find key highlights and differences between articles written about a similar topic.

Now, Artificial Intelligence is making inroads in the field by providing smart summaries of documents. An AI algorithm developed by researchers at Salesforce generates snippets of text that describe the essence of longtext. Though tools for summarizing texts have existed for a while, Salesforces solution surpasses others by using machine learning. The system uses a combination of supervised and reinforced learning to get help from human trainers and learn to summarize on its own.

Other algorithms such as Algorithmias Summarizer provide developers with libraries that easily integrate text summary capabilities into their software.

These tools can help writers skim througha lot of articles and find relevant topics to write about. It can also help editors to read through tons of emails, pitches and press releases they receive every day. This way theyll be better positioned to decide which emails need further attention. Having hundreds of unread emails in my inbox, I fully appreciate the value this can have.

Advances in Natural Language Processing have contributed widely to this trend. NLP helps machines understand the general meaning of text and relations between different elements and entities.

To be fair, nothing short of human level intelligence can have the commonsense knowledge and mastery of language required to provide flawless summary of all text. The technology still has more than and few kinks to iron out, but it shows a glimpse of what the future of reading might look like.

No matter how high-quality and relevant your content is, itll be of no use if you cant reach out to the right audience. Unfortunately, old keyword-based search algorithms pushed online writers toward stuffing their content with keywords in order to increase their relevance for search engine crawlers.

Although with PageRank, Google did a great job in organizing the web, it also created a web where keywords ruled over content, says Gennaro Cuofano, growth hacker at WordLift, a company that develops tools for semantic web. Eventually, web writers ended up spending a significant amount of time improving the findability. The trend resulted in poor quality writing getting higher search ranking.

But thanks to Artificial Intelligence, search engines are able to parse and understand content, and the rules of search engine optimization have changed immensely in past years.

Since new semantic technologies are now mature enough to read human language, journalists and professional writers can finally go back to writing for people, Cuofano says. This means you can expect more quality content to appear both on websites and search engine results.

Where do we go from here? The next revolution (which is already coming) is the leap from NLP to a subset of it called NLU (Natural Language Understanding), Cuofano says. In fact, while NLP is more about giving structure to data, defining it and making it readable by machines; NLU instead is about taking unclear, unstructured and undefined inputs and transforming them to an output that is close to human understanding.

Were already seeing glimmers of this next generation in AI-powered journalism. The technology is still in its infancy, but will not remain so indefinitely. Writing can someday become a full-time machine occupation, just like many other tasks that were believed to be the exclusive domain of human intelligence the past.

How does this affect writing? Currently, the web is a place where how-to articles, tutorials and guides are dominant, Cuofano says. This makes sense in an era where people are still in charge of most tasks. Yet in a future where AI takes over, wouldnt it make more and more sense to write about why we do things? Thus, instead of focusing on content that has a short shelf life, we can focus again on content that has the capability to outlive us.

Read next: Apple blames exploding Beats headset on AAA batteries

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How Artificial Intelligence will impact professional writing - TNW

Forbes

Techstars: How Artificial Intelligence Can Make The Music Industry ... Forbes New darling tech startups are transforming the music value chain to the industry's benefit. Can Artificial Intelligence Save The Music Industry? - UproxxUPROXX all 2 news articles »

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Techstars: How Artificial Intelligence Can Make The Music Industry ... - Forbes

Sid Dixit of Planet presents at Train AI in San Francisco. Photo by: Catherine Cheney / Devex

Machine learning is the ultimate way to go faster, said Peter Norvig, director of research at Google, as he showed a slide image of a race car to a crowd of professionals gathered to learn more about artificial intelligence.

But speed can also lead to accidents, Norvig warned, clicking to another slide with an image of a dramatic crash. Norvig, who is also the author of a leading textbook on artificial intelligence, or AI, was speaking at Train AI, a conference hosted by CrowdFlowerin San Francisco, California, this week.

In every industry, theres a place where AI can make things better, Norvig told Devex. Look at all of the AI technologies, and all problems, and its just a question of fitting them together and figuring out, whats the right technological match and whats the right policy match?

Machine intelligence will have profound implications for the development sector. AI is a way to understand data, Norvig continued, and the global development community will be unable to understand and act on information coming in from cell phones to satellites without both human and machine intelligence.

Norvig will also speak at the upcoming AI for Global Goodsummit hosted at the International Telecommunications Unitin Geneva, Switzerland. Gatherings such as these help technologists connect solutions to problems, he said. From Silicon Valley to Hyderabad, India, where the ninth annual Information and Communications Technology for Development, or ICT4D, conferencehas just taken place, there is growing interest in bringing the technology community together with the global development community, in order to leverage AI to achieve the Sustainable Development Goals.

Every day we see a new news report on how AI is changing the future of every part of society, said Robin Bordoli, chief executive officer of CrowdFlower, which describes itself as an essential platform for data science. Despite some of the concerns around job loss, we believe in the power of AI to create positive change at all levels of society.

That is the thinking behind the launch of CrowdFlowers AI for Everyonechallenge to put the power of AI into the hands of people who want to use machine intelligence to solve social problems. The company expects to see applications addressing global challenges in areas such as health care, food and nutrition, and climate change, and it is just the latest in a number of similar such competitions.

Earlier this month, XPRIZE, which puts on competitions together with partners, announced the 147 teamsfrom 22 countries that would advance in the $5 million IBM Watson AI XPRIZE. The teams entering this global competition are working to develop AI applications that demonstrate how humans, together with AI, can tackle global challenges. Examples include Harvesting, a global intelligence platform for agriculture.

With any automated and digital system you have to make sure you are not shutting out or creating unintended problems for people who can't read, don't have devices, or otherwise are not able to access the new system.

And the Digital Financial Services Innovation Lab, an early stage incubator for entrepreneurs building financial technology companies in developing countries, has open challenges for biometrics and chatbots, with a deadline of May 30. DFS Lab is housed within Caribou Digital, a research and delivery consultancy in Seattle, Washington. TheBill & Melinda Gates Foundationfunded the incubator to engage top scientists and engineers in challenges such as these around boosting financial inclusion, said Jake Kendall, director of the DFS Lab, who was formerly on the Financial Services for the Poor team at the Gates Foundation.

Human interactions are always going to be necessary, but any time you can remove the need for them from a process through automation or NLP [natural language processing] conversational interfaces, that can be a game changer in terms of scalability and efficiency, he told Devex via email. NLP and bots give people tools to help themselves in the digital realm which can be really empowering. But there are downsides. With any automated and digital system you really have to make sure you are not shutting out certain people or creating unintended problems for people who can't read, don't have devices, or otherwise are not able to access the new system.

In the popular imagination, AI can feel like something that solves the problems of the affluent with products such as Alexa, the Amazon device that allow users to get information, play music, or control their smart homes using their voices. But many experts in the field believe there is a major role for AI in helping achieve the SDGs, and the founder of Arifu is making the casefor the role of chatbots and AI in achieving the SDGs at the ICT4D conference. His education technology company, which launched in Kenya in 2015, points to how a chatbot leveraging AI can deliver personalized learning on mobile devices to provide access to information on topics such as farming, entrepreneurship or financial literacy to the worlds least served.

Were moving from the enterprise and the abstract to the consumer and the personal, said Robert Munro, principal product manager at Amazon AI, at Train AI.

What that means for global health, for example, is a shift toward point of care tests, even in resource limited settings, he said.

Munros talk in San Francisco revisited AIs progress after a series of talks he gave five years ago called Wheres My Talking Robot. AI is now making more decisions in our lives than most people realize, he said. Its making us smarter, choosing our friends, selecting our news, aiding our health, moving us around, and protecting our security.

For example, he mentioned how the first alert for the swine flu outbreak in Mexico came from reading AI reports about potential disease outbreaks.

However, at a conference covering high-definition mapping, AI and medicine, and deep learning, examples of applications of machine learning in developing countries were few and far between.

Lukas Biewald, founder & executive chairman of CrowdFlower, did talk about how one of his clients is using drones for conservation.

And in a series of presentations from CrowdFlower customers, Sid Dixit, director of product program management at Planet, talked about how AI combined with millions of images from its small satellites can determine the health of forests and water resources, and monitor harvests and agriculture everywhere.

Anthony Goldbloom, CEO of Kaggle, talked about how Genentech, a biotechnology corporation,opened a challengeon his platform for machine learning competitions to predict which women would not be screened on schedule for cervical cancer, a largely preventable disease that several leaders in the global health community, including PATHin Seattle, Washington,are saying needs more attention in developing countries.

But the list of examples of applications of AI to the SDGs continues to grow. This week, at ICT4D, the international agriculture research consortium known as CGIAR launched a platform forbig data in agriculture. It unites agricultural research institutes and companies with the goal of closing the digital divide between farmers in developed and developing countries. Amazon will bring its cloud computing and data processing capabilities, IBM, creator of the Watson artificial intelligence system, will bring its data analysis, and PepsiCo will bring its use of big data to manage supply chains.

One of the lines that came up at the AI conference in San Francisco was recent comments by physicist Stephen Hawking, who said thatthis technology will be either the best, or the worst thing, ever to happen to humanity.

Silicon Valley is behind a number of initiatives working to ensure that AI benefits humanity, including OpenAI, a nonprofit AI research company, to ensure that the benefits of machine learning are as widely and evenly distributed as possible.

And increasingly, forward looking thinkers in the global development community are presenting themselves as natural partners in these efforts, as the ITU has done in organizing the AI for Global Good Summit together with XPRIZE and other United Nations agencies.

As the U.N. specialized agency for information and communication technologies, ITU aims to guide AI innovation towards the achievement of the U.N. SDGs, ITU Secretary-General Houlin Zhao said of the event, which kicks off June 07. We are providing a neutral platform for international dialogue to build a common understanding of the capabilities of emerging AI technologies.

The ITUs most recent magazineis entirely focused on how AI can boost sustainable global development.

The biggest risks posed by the rise of AI is not so much the singularity, in which machine intelligence matches then surpasses human intelligence, but wasted projects and dollars, said two venture capitalists from Bloomberg Beta, which makes early stage investments in artificial intelligence startups. Echoing some of the points made by Norvig of Google, they said the key is to use AI to solve real problems. Of course, global development professionals are working on complex problems that might appeal to machine learning experts looking to use their skills for good, which is why any effort to ensure AI benefits humanity might consider bringing these communities together.

Read more international development newsonline, and subscribe to The Development Newswireto receive the latest from the worlds leading donors and decision-makers emailed to you free every business day.

Link:

How artificial intelligence might help achieve the SDGs - Devex

There wasnt any one big product announcement at Google I/O keynote on Wednesday, the annual event when thousands of programmers meet to learn about Googles software platforms. Instead, it was a steady trickle of incremental improvements across Googles product portfolio. And almost all of the improvements were driven by breakthroughs in artificial intelligence the softwares growing ability to understand complex nuances of the world around it.

Companies have been hyping artificial intelligence for so long and often delivering such mediocre results that its easy to tune it out. AI is also easy to underestimate because its often used to add value to existing products rather than creating new ones.

But even if youve dismissed AI technology in the past, there are two big reasons to start taking it seriously. First, the software really is getting better at a remarkable pace. Problems that artificial intelligence researchers struggled with for decades are suddenly getting solved

Our software is going to get superpowers thanks to AI, says Frank Chen, a partner at the venture capital firm Andreessen Horowitz. Computer programs will be able to do things that we thought were human-only activities: recognizing what's in a picture, telling when someone's going to get mad, summarizing documents.

But more importantly, Chen says, AI capabilities are about to be everywhere. Until recently, big companies focused on adding AI capabilities to their own products think about your smartphone transcribing your voice and Facebook identifying the faces in your photos. But now big companies are starting to open up their powerful AI capabilities to third-party developers.

And often, this is the moment when a new technology has a really big impact. The iPhone didnt really become truly revolutionary until Apple created the app store, allowing third parties to create apps like Uber and Instagram. Soon every company and every ambitious kid in a dorm room is going to have access to the same powerful AI tools as the worlds leading technology companies.

Primitive forms of AI have been around for a long time. Back in the 1990s, for example, you could get voice-to-text software that would transcribe your words into a word processor.

But these products used to be terrible. Speech-to-text software would make so many errors that wasnt much faster than typing a document on a keyboard. The handwriting recognition feature on Apples 1990s tablet computer, the Newton, was so bad it became a punchline. As recently as the early 2010s, I remember the voice-to-text feature of my smartphone making a lot of mistakes.

Then AI technology suddenly started working better. A couple of years ago, I noticed that my smartphone hardly ever made mistakes. Photo apps from Apple, Google, and Facebook got good at recognizing faces. In his Wednesday keynote, Google CEO Sundar Pichai offered some data on just how rapid this progress has been:

This data illustrates how good Googles smart speaker, Google Home, is at understanding user speech in a noisy room. In less than a year, the error rate has fallen by almost half.

Touting this rapid progress in voice recognition, Pichai told an audience of hundreds of developers that the pace even since last year has been pretty amazing to see.

And there are more impressive breakthroughs coming up. For example, Pichai said, suppose you took this photo of your daughter playing baseball:

Pichai says that youll soon be able to use Google technology to remove the chain-link fence, producing a photo that looks like this:

The two-hour keynote featured demonstrations from across Googles product portfolio, from Android to YouTube. And seemingly every product had significant AI-based improvement.

Googles photo app will soon be able to recognize your best photos, figure out who is in them, and then offer to send copies to the people in the photos with one click.

Google Home is getting smart enough to distinguish between different users in a household. If you say, Call Mom, Googles software will be smart enough to know just based on your voice to call your own mother and not your spouses mother.

The machine learning algorithms that underpin the AI revolution place extreme demands on conventional computing hardware. At last years Google I/O, the search giant announced that it had designed a custom chip called a tensor processing unit for machine learning applications. Tests show that these chips can execute machine learning code up to 30 times faster than conventional computer chips.

Over the past year, Google has installed racks and racks of these chips in its vaunted data centers to support the growing AI capabilities of various Google products. On Wednesday, Google announced that it will soon be opening up these chips for anyone to use as part of Googles cloud computing platform. Google has already released its powerful machine learning software, called TensorFlow, as an open source project so that anyone could use it.

Google isnt just being nice, of course. The larger goal is to establish Googles AI platform as the industry standard thousands of other companies rely on for their own AI software. Once you build software on top of one platform, its very expensive to switch, so becoming an industry standard could make Google billions in the coming years.

Of course, Googles rivals arent going to accept this without a fight. Amazon currently leads the cloud computing market with its Amazon Web Services, and it is offering developers a rival suite of machine learning tools. Microsoft offers machine learning tools on its own Azure cloud computing platform.

Consumers dont care which tech giants cloud computing platform powers their favorite app or website. But this platform war will have big indirect benefits for consumers. Because in their rush to win the cloud computing war, these technology giants are making more and more powerful AI capabilities available to anyone who wants to use them. Which means were about to see an explosion of experimentation with AI capabilities.

Google showed off a small example of what this might look like with Googles voice-based assistant. On the I/O stage a Google executive said, I'd like delivery from Panera, and this started a conversation with the app that worked a lot like a conversation youd have with a human Panera cashier. The executive said she wanted to order a sandwich. The virtual assistant asked if she wanted to add a drink. After she chose a drink, the assistant told her the total price and asked if she wanted to place the order.

The remarkable thing about this exchange wasnt so much the ability to carry out a simple conversation something virtual assistants like Apples Siri have been able to do for a few years. Its the promise that every retail establishment in America could build a similar capability without having to hire a bunch of computer science PhDs.

Googles promise is that creating this kind of sophisticated AI experience will soon be as simple as building a website or a smartphone app is today. Googles own engineers will do most of the hard work, creating powerful tools that allow non-software companies to build services that would have been beyond the reach of even the most sophisticated technology companies a decade ago. It might take a few years for this vision to be realized the first websites and smartphone apps were often terrible but eventually customers will expect every app to offer these kinds of capabilities.

At the same time, more sophisticated developers will be able to use the tools provided by Google, Amazon, Microsoft, and their competitors to push the envelope even further. Chen believes that machine learning techniques will lead to improvements in medical care for example, helping radiologists identify cells with cancer. In the past, you needed a huge team of AI experts to even attempt to build something like this. Today, the basic tools are within reach of high school kids. Its a safe bet that this will spawn totally new kinds of apps, just as the invention of the smartphone made Uber possible.

Disclosure: My brother works at Google.

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Artificial intelligence is getting more powerful, and it's about to be everywhere - Vox

Saying AI will take over the creativity of humans is not right: Adobe

IANS | New Delhi May 20, 2017 Last Updated at 14:09 IST

As artificial intelligence (AI)-powered smart devices and solutions gather momentum globally amid fears of "bots" taking over jobs soon, a top Adobe executive has allayed such fears, saying AI will actually assist people intelligently.

"Saying AI will take over the creativity of humans is not right. It will take away a lot of stuff that you have to do in a mundane way. A human mind is a lot more creative than a machine," Shanmugh Natarajan, Executive Director and Vice President (Products) at Adobe, told IANS in an interview.

"With AI, we are trying to make the work easier. It is not like self-driving cars where your driver is getting replaced. I think creativity is going to stay for a long time," Natarajan added.

Market research firm Gartner recently said that CIOs will have a major role to play in preparing businesses for the impact that AI will have on business strategy and human employment.

Global enterprises like Adobe are now betting on India to boost AI in diverse sectors across the country.

The company has a massive set-up in India, with over 5,200 employees spread across four campuses in Noida and Bengaluru and its R&D labs claim a significant share of global innovations.

According to Natarajan, a lot of work related to AI, machine learning and Internet of Things (IoT) is being done in Adobe's India R&D Labs.

"The way we have structured our India labs is very similar to how larger companies have structured it. There are separate lab initiatives and areas, including digital media, creative lab, Big Data and marketing-related labs and, obviously, document is a big part and we have labs associated with it as well," the executive said.

"With the Cloud platform, we are trying to provide a framework where people with the domain expertise can come and set their data and machine learning algorithms in play and then train the systems and let the systems learn," Natarajan explained.

Speaking on the significance of India R&D labs, Natarajan said earlier the R&D labs were focused on North America where scientists used to come in from esteemed universities.

With India becoming a crucial market for research and development, Adobe started its data labs in Bengaluru under the leadership of Shriram Revankar.

"Nearly 30 per cent of our total R&D staff is here. Apart from other works, we file patents. Every year, Adobe India has been filing nearly 100 patents from a global perspective. We have eight patents coming in soon," Natarajan told IANS.

Interestingly, a big part of "Adobe Sensei" -- a new framework and set of intelligent services that use deep learning and AI to tackle complex experience challenges -- was developed in India.

On why there is a technology gap between India and other developed economies in terms of use of concepts like AI, machine learning and IoT, Natarajan said that people underestimate the country.

"The transitions and generational things might not be at the same level and sophistication, or the pace as compared to other countries, but here, the changes are dramatic," Natarajan told IANS.

"Everyone has a smartphone now and people have figured out that they can speak to their smartphones and retrive data. The data may be small as compared to 100 trillion that Adobe gets, but it is a Cloud and IoT device. People are interacting with them and machine is learning from this," the executive noted.

As artificial intelligence (AI)-powered smart devices and solutions gather momentum globally amid fears of "bots" taking over jobs soon, a top Adobe executive has allayed such fears, saying AI will actually assist people intelligently.

"Saying AI will take over the creativity of humans is not right. It will take away a lot of stuff that you have to do in a mundane way. A human mind is a lot more creative than a machine," Shanmugh Natarajan, Executive Director and Vice President (Products) at Adobe, told IANS in an interview.

"With AI, we are trying to make the work easier. It is not like self-driving cars where your driver is getting replaced. I think creativity is going to stay for a long time," Natarajan added.

Market research firm Gartner recently said that CIOs will have a major role to play in preparing businesses for the impact that AI will have on business strategy and human employment.

Global enterprises like Adobe are now betting on India to boost AI in diverse sectors across the country.

The company has a massive set-up in India, with over 5,200 employees spread across four campuses in Noida and Bengaluru and its R&D labs claim a significant share of global innovations.

According to Natarajan, a lot of work related to AI, machine learning and Internet of Things (IoT) is being done in Adobe's India R&D Labs.

"The way we have structured our India labs is very similar to how larger companies have structured it. There are separate lab initiatives and areas, including digital media, creative lab, Big Data and marketing-related labs and, obviously, document is a big part and we have labs associated with it as well," the executive said.

"With the Cloud platform, we are trying to provide a framework where people with the domain expertise can come and set their data and machine learning algorithms in play and then train the systems and let the systems learn," Natarajan explained.

Speaking on the significance of India R&D labs, Natarajan said earlier the R&D labs were focused on North America where scientists used to come in from esteemed universities.

With India becoming a crucial market for research and development, Adobe started its data labs in Bengaluru under the leadership of Shriram Revankar.

"Nearly 30 per cent of our total R&D staff is here. Apart from other works, we file patents. Every year, Adobe India has been filing nearly 100 patents from a global perspective. We have eight patents coming in soon," Natarajan told IANS.

Interestingly, a big part of "Adobe Sensei" -- a new framework and set of intelligent services that use deep learning and AI to tackle complex experience challenges -- was developed in India.

On why there is a technology gap between India and other developed economies in terms of use of concepts like AI, machine learning and IoT, Natarajan said that people underestimate the country.

"The transitions and generational things might not be at the same level and sophistication, or the pace as compared to other countries, but here, the changes are dramatic," Natarajan told IANS.

"Everyone has a smartphone now and people have figured out that they can speak to their smartphones and retrive data. The data may be small as compared to 100 trillion that Adobe gets, but it is a Cloud and IoT device. People are interacting with them and machine is learning from this," the executive noted.

IANS

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Qualified humans must not fear bots: Adobe on Artificial Intelligence - Business Standard