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Curiosity is Vital: How Machine Inquisitiveness Improves the Ability to Perform Smartly

Posted on June 2, 2017May 23, 2020 by Dexlab

What happens when a computer algorithm merges with a form of artificial curiosity – to solve precarious problems?

Meticulous researchers at the University of California, Berkeley framed an “intrinsic curiosity model” to make their learning algorithm function even when there is a lack of strong feedback signal. The pioneering model developed by this team visions the AI software controlling a virtual agent in video games in pursuit of maximising its understanding of its environment and related aspects affecting that environment. Previously, there have been numerous attempts to render AI agents’ curiosity, but this time the trick is simpler and rewarding.

The shortcomings of robust machine learning techniques can be solved with this mighty trick, and it could help us in making machines better at solving obscure real world problems.

Pulkit Agrawal, a PhD student at UC Berkeley, who pulled off the research with colleagues said, “Rewards in the real world are very sparse. Babies do all these random experiments, and you can think of that as a kind of curiosity. They are learning some sort of skills.”

Also read: Data Science – then and now!

Like several potent machine learning techniques rolled out in the past decade, Reinforcement Learning has brought in a phenomenal change in the way machine accomplish their things. It has been an intrinsic part of AlphaGo, a poster child of DeepMind; it helped playing and winning the complex board game GO with incredible skill and wit. As a result, the technique is now implemented to imbue machines with striking skills that might be impossible to code manually.

However, Reinforcement Learning comes with its own limitations. Agrawal pointed that sometimes it demands a huge amount of training in order to grasp a task, and the procedure can become troublesome, especially when the feedback is not immediately available. To simplify, the process doesn’t work for computer games where the advantages of specified behaviours is not just obvious. Hence, we call for curiosity!

Also read: After Chess, Draughts and Backgammon, How Google’s AlphaGo Win at Go

For quite some time now, a lot of research activity is going around on artificial curiosity. Pierre-Yves Oudeyer, a research director at the French Institute for Research in Computer Science and Automation, said, “What is very exciting right now is that these ideas, which were very much viewed as ‘exotic’ by both mainstream AI and neuroscience researchers, are now becoming a major topic in both AI and neuroscience,”. The best thing to watch now is how the UC Berkeley team is going to run it on robots that implement Reinforcement Learning to learn abstract stuffs. In context to above, Agrawal noted robots waste a nifty amount of time in fulfilling erratic gestures, but when properly equipped with innate curiosity, the same robot would quickly explore its environment and establish relationships with nearby objects.

Also read: CRACKING A WHIP ON BLACK MONEY HOARDERS WITH DATA ANALYTICS

In support of the UC Berkeley team, Brenden Lake, a research scientist at New York University who lives by framing computational models of human cognitive capabilities said the work seemed promising. Developing machines to think like humans is an impressive and important step in the machine-building world. He added, “It’s very impressive that by using only curiosity-driven learning, the agents in a game can now learn to navigate through levels.”

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Exploring New Avenues of Alliance Between Microsoft and Artificial Intelligence

Posted on May 12, 2017October 10, 2018 by Dexlab

Artificial Intelligence is perhaps the ‘trending’ term of the technological paradigm. Microsoft is quite a honcho in the arena of Artificial Intelligence. This statement is further enhanced by Andrew Shuman, the Corporate VP for Microsoft AI and research Group. At the Microsoft’s annual Build Developer’s conference, he regarded “If I think about the kind of AI revolution that’s going on, it’s very much created by new increase in data being available and cloud service being able to run millions of computations”.

Pertaining to the situation, it is true that Microsoft certainly has all the essential data, in comparison to the other IT companies, which has won the company a premier position in the field of AI. The data includes 100 million Office 365 subscribers and, in OneDrive and certainly has the cloud based services.

Also read: Artificial Intelligence: What the Future Holds for India, Next to US

Now the next section would deal with the utilities of AI in certain sectors:

AI for Office uses – Hard to find a single soul unaware of Microsoft’s Office productivity. But on the downside, the users need to deal with certain upheavals, sometimes causing a lot of difficulties. This entire process, now teamed up with AI, ensures a butter smooth flow of the software.

Power Point– The Quick Starter takes the aid of AI to search for the right template based, sometimes, on a single word it is typed into one of the slides. However, behind the scenes, it is actually dependent on the vast well of the structured Bing data.

The Designer Service is also used for image presentations, in the quest for the congruence of faces and even colors that can influence template design choices.
AI also enriches Power Point presentations as a cognitive vision system exploring pictures and auto-generating the ALT-Text for them.

The Focused Inbox Option in Outlook is mainly supported by the cloud- based machine learning, where the system enriches itself through explicit and implicit The recent past has seen this software gaining much eminence in the Android and IOS versions of outlook.

The last utility is the End –user control, a common theme found across all of Microsoft’s AI It refers to the tools to be personalized to the users. This ensures that the changes often rejected in the Word would be no longer flagged in the writing.

The Cortana Complication – In one word, Cortana is the public face for Microsft’s AI work. The voice assistant installed in million of desktop to be an aid to the users, it is mainly regarded as a hazard with the majority of the Windows users opting for the text box , next to the start button, to type in their queries. Even after this, Microsoft is still being enthusiast to project Cortana as the face and of AI efforts. Microsoft is actually working hard to set it as a household name on the AI front, with its latest discovery of Cortana Speakers, coming to the fore front sometimes next week. On being asked, if Cortana is the main obstacle then why Microsoft doesn’t restore all their efforts for the effective building of the software, Shuman answered, “I think we need to be careful about where we make it Cortana and where we don’t. To me it implies a full set of capabilities instead of little nuggets.”

Also read: Learn to Surf on the Three Waves of Artificial Intelligence

Thus to conclude, whether it is with or without Cortana, Microsoft remains the leading brand name in the AI sector. This again has been explained by Shuman as “We are without a doubt infusing intelligence and understanding in all of our products in ways that’s very shared and shareable,”

So, that was all about the utilities of AI. Feel free to share the latest information. Also, enroll for the Artificial Intelligence Certification Courses only at www.www.dexlabanalytics.com

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Artificial Intelligence: What the Future Holds for India, Next to US

Posted on May 2, 2017June 7, 2018 by Dexlab

Is artificial intelligence outgrowing human intelligence? Is AI becoming smarter than we are?

In the United States – the world’s unchallenged superpower, a new strange issue has popped up, which is being discussed on all the major interactive platforms, like books, talk-shows, YouTube, etc. but to no avail. The issue-in-question is addressed in the beginning of the blog.

Also read: Learn to Surf on the Three Waves of Artificial Intelligence

Will computer intelligence exceed human intelligence?

In India, this doesn’t seem to bother us much. For us, computers are electronic devices that we control, that we command. Our smartphones and tablets are treated as our servants and not our masters. But I wonder for how long will this persist? How long will we be able to refrain ourselves from being influenced by the West? Well, that’s another question to answer and let’s keep it for another day!

Also read: What Makes Artificial Intelligence So Incredibly Powerful?

In the US, some of the tech pundits are working tirelessly on the newer realms of AI each day and speculating what will happen when computer programmes finally overhauls human brain in thinking abilities. Intelligence is a set of information, and the potential to know how to use it.

Also read: How Machine Learning Training Course and AI Made Lives Easier

Since 1990, computer technology has evolved substantially and has become nifty in intelligence. Let’s take the example of self driving cars ploughing the American roads. A fully independent, self-driving car is no more a VFX-induced scene from a sci-fi fantasy movie; in two years or more, they will be found dominating the streets of the US and trust me they will be a reality! Sit in your car, read a book or sleep while your car reaches its destination on its own. This car will perform all those functions that you used to do, giving you a hiatus from driving!

Also read: DexLab Analytics’ Take on the scope of Artificial Intelligence: Its Humanity vs. Algorithms

The boons of AI don’t end here, it’s thriving and improving faster. Why? Because, humans need to address a whole lot of problems with the help of technology. From conducting complicated surgeries to developing hi-tech BI tools, the scope of computer intelligence is vast and still increasing.

Another crucial factor is that the human brain is limited and can only contain a fixed amount of cerebral cortex and related substances that helps us think and remember. Beyond that, there is no scope for expansion, but in computer technology, the sky is the limit. It is possible to create a computer as large as a 3-storeyed building and store humongous amount of data in it.

The research says within the next 25 years, computer intelligence will become so efficacious that it will leave behind man’s intelligence in every way. So, what are you waiting for you? Give your career a robust boost with R programming courses. Reach us at DexLab Analytics, a leading R language certification institute for more queries.

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Transformation of Smartphones with AI

Posted on April 18, 2017May 23, 2020 by Dexlab

Once a science fiction fantasy, Artificial Intelligence is today’s resonating reality. People are already relishing myriad advantages through advanced mobile apps and smart-forever smartphones.

Supposedly, smartphones made our lives easier. Not only does it allows us stay in touch with our beloved ones 24/7 but also allow us easy accessibility to a humungous amount of information over the internet, help us reach our designated destinations, play games, watch movies, check mails, and lot more.. And in the thick of all the telltales of new cameras and additional storage, AI is bringing in a poignant change in the realm of smartphone technology, which will impact our lives immensely.

Integrating AI with apps

How would you feel when your phone opens INSTAGRAM, before you tapped on it? You will be elated! Isn’t it? We are already witnessing some basic versions of above-mentioned technology on some phones where the most-used apps pop up on the top of the screen. It’s no more a thing from a science fiction novel; Google Now would know everything about you – the way you use your phone, when you call your home, when you need to tap open a map app, or even the exact moment you feel like taking a photo. No more you have to arrange your home screen icons or click on the apps you need, because whenever you will unlock your phone, the app you want to open will launch automatically.

Mark your steps with AI

On the other hand, if we talk about mapping systems, you must have already come by Apple maps and Google maps that can predict your next whereabouts, based on past searches and destinations put into. In the near future, this technology will get cleverer intellectually. Making decisions based on your preferred routes, the type of public transport you board, how you react when you are stuck in traffic won’t be a tad difficult, provided Google has all the information it needs about you.

Say Hi to a digital assistant

Do you wonder at times, what if your phone becomes your best friend? Though it may sound creepy at first, but this is exactly the way towards which Artificial Intelligence is heading to. Digital assistants will be more like your best buddy who will be beside you on your happiest and worst days. If you feel stressed at work, your digital assistant will know how to uplift your mood or what kind of music to play to make you better..

FELICITATIONS to the Personal assistant app

How about having an inbuilt personal assistant app to do the flight bookings or order some selected items from your shopping cart? Sounds cool! From restaurant bookings and comparing gas-energy prices to sending smart replies, this personal assistant using the bounties of AI excels on a bouquet of jobs.

Fulfilling software programs like Cortana, Siri and Google Now have already started bridging the gap between them and real-life personal assistants. In the future, this gap will further be lessened and these apps will finally be able to do many smart functions.

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Learn to Surf on the Three Waves of Artificial Intelligence

Posted on April 4, 2017September 12, 2020 by Dexlab

How is the future going to be like? Will human workforce be completely redundant? Will machine learning supersede human intelligence? There have been myriad forecasts about the illuminating future of the AI: that it will be capable of analyzing human emotions, evaluating social nuances, heading medical treatments and surgeries, all that shrouds the best of human resources. But what about now? What is the present scenario like? Fortunately, DARPA is here to unleash a stream of answers to various questions, asked as well as unasked.

DARPA is one of the most intriguing agencies in the US. The stalwarts at DARPA succour outrageous projects – concepts that are completely absurd and far from the accepted paradigms. GPS, legged robots, self-assembling work tools, prediction markets and early internet are some of its incredible creations. And now, they are putting their rear into gear to focus on AI.

DARPA segregates between three distinguishing waves of AI; each is boisterous with its own abilities and challenges. Out of the three, if you ask me which one is more galvanizing, I will point my finger to the third one. However, to decode the third wave of AI, which is the most exhilarating out of the three, you need to understand the first two.

First Wave of AI: Logical Rules and Bespoke Knowledge

Here, tech specialists formulated algorithms and software programs based on the knowledge and information they imbibed, and tried to feed them with logical commands that were decrypted throughout the years. Most of the software that we are using today, like Windows Operating System, traffic system and even our smartphone apps emanated from first wave AI.

Clear and logical rules formed the crux of first wave AI structure. They were successful in introducing simple logical rules for intricate problems, but what they lacked was incapability of learning and ways to deal with the uncertainties.

Second wave of AI: Statistical Learning Systems

In 2004, DARPA initiated its first Grand Challenge – where fifteen autonomous vehicles took part in the competition and they had to complete a 150-mile track in the Mojave Desert. The vehicles were formulated on first wave AI and immediately it revealed the limitations of AI.

Not a single vehicle could finish the entire course; it was an absolute failure.

DARPA learnt its lessons, well. Just one year later, DARPA again organized Grand Challenge 2005 and this time, five groups completed the entire track. But, how? A year ago, they couldn’t.

The groups that could complete the race used the second wave of AI: statistical learning. In this wave, the engineers ignored the exact rules of first wave; instead, they focused on developing statistical models, which they trained eventually on numerous samples to make them highly efficient and accurate.

For better understanding and higher adaptability, statistical learning systems are fetching. If they are properly trained, they can work and adapt themselves to different situations. The brainchild of second wave AI is the notion of artificial neural networks. Besides, the second wave AI is dwarfing humans at speech transcription, face recognition, identifying objects and animals in images, controlling autonomous cars and aerial drones. However, the success of these complex programs leaves the AI pundits clueless. Nobody knows how these systems are working so well. But, we are not complaining!

Third Wave of AI: Redefining First Wave Logical Rules

In the last and final wave, AI system will take the charge of constructing models, themselves. Precisely, they will now redefine the logical rules, which will sculpt the entire decision-making process. The third wave is efficient in relying on several different kinds of statistical models to draw a bigger picture of the world. They are capable of training themselves better.

They are able to derive information from various kinds of sources in order to come at a nuanced and logical conclusion. The systems are so effective that they can actually extract data from our smart homes, cars, cities, even from our wearable devices and deduce our health status. Moreover, they will also be able to program themselves and help in developing abstract thinking.

However, the only challenge that is up on the front is, “there’s a whole lot of work to be done to be able to build these systems”, as told by the director of DARPA’s Information Innovation Office.

The implementation of the third wave is surely going to be a major step for the entire mankind. However, all of this will take some time. Probably it will eventually taste success in the next twenty years. But, when we will appraise the potentials of the AI systems, the concept of the third wave won’t sound improbable.

At the end of the discussion, I am pondering would there be any fourth wave of AI and if yes, then what it would be like? These questions should however be left on time to analyse and then to research. Till then, may you be focused on the third wave.

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