Researchers Have Created an AI That Is Naturally Curious

Researchers Have Created an #AI That Is Naturally Curious 

 #fintech @futurism

  • Researchers have successfully given AI a curiosity implant, which motivated it to explore a virtual environment.
  • This could be the bridge between AI and real world application

    Researchers at the University of California (UC), Berkeley, have produced an artificial intelligence (AI) that is naturally curious.

  • While the AI that was not equipped with the curiosity ‘upgrade’ banged into walls repeatedly, the curious AI explored its environment in order to learn more.
  • This is a useful and effective strategy for teaching AI to complete specific tasks — as shown by the AI who beat the AlphaGo world number one — but less useful when you want a machine to be autonomous and operate outside of direct commands.
  • This is crucial step to integrating AI into the real world and having it solve real world problems because, as Agrawal says, “rewards in the real world are very sparse.”

Researchers have successfully given AI a curiosity implant, which motivated it to explore a virtual environment.
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The Science of AI and the Art of Social Responsibility

The @HuffingtonPost take a look at the science of #AI and the art of social responsibility:

  • But the transformational nature of artificial intelligence requires new metrics of success for our profession.
  • This year alone at least 1 billion people will be touched in some way by artificial intelligence, which is transforming everything from financial services to transportation, energy, education and retail.
  • And why IBM is a founding member of the Partnership on AI, a collaboration among Google, Amazon, Facebook, Microsoft, Apple and many scientific and nonprofit organizations charged with guiding the development of artificial intelligence to the benefit of society.
  • Opportunity: Developers of AI applications should accept the responsibility of enabling students, workers and citizens to take advantage of every opportunity in the new economy powered by cognitive systems.
  • They should help them acquire the skills and knowledge to engage safely, securely and effectively in a relationship with cognitive systems, and to perform the new kinds of work and jobs that will emerge in a cognitive economy.

By Guru Banavar, IBM’s Chief Science Officer for Cognitive Computing
I am a computer scientist and engineer, inspired by the art of the possible an…
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Demystifying machine learning part 2: Supervised, unsupervised, and reinforcement learning

#machinelearning use cases:

1. Supervised
2. Unsupervised
3. Reinforcement

  • It is a type of machine learning, where one guides the system by tagging the output.
  • For example, a supervised machine learning system that can learn which emails are ‘spam’ and which are ‘not spam’ will have its input data tagged with this classification to help the machine learning system learn the characteristics or parameters of the  ‘spam’ email and distinguish it from those of ‘not spam’ emails.
  • Just as the three year old learns the difference between a ‘block’ and a ‘soft toy’, the supervised machine learning system learns which email is ‘spam’ and which is ‘not spam’.
  • Now instead of telling the child which toy to put in which box, you reward the child with a ‘big hug’ when it makes the right choice and make a ‘sad face’ when it makes the wrong action (e.g., block in a soft toy box or soft toy in the block box).
  • Based on your problem domain and the availability of data do you know which type of machine learning system you want to build?

Where business and experience meet emerging technology.
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The Science of AI and the Art of Social Responsibility

The science of #AI and the art of social responsibility:  via @HuffingtonPost

  • But the transformational nature of artificial intelligence requires new metrics of success for our profession.
  • This year alone at least 1 billion people will be touched in some way by artificial intelligence, which is transforming everything from financial services to transportation, energy, education and retail.
  • And why IBM is a founding member of the Partnership on AI, a collaboration among Google, Amazon, Facebook, Microsoft, Apple and many scientific and nonprofit organizations charged with guiding the development of artificial intelligence to the benefit of society.
  • Opportunity: Developers of AI applications should accept the responsibility of enabling students, workers and citizens to take advantage of every opportunity in the new economy powered by cognitive systems.
  • They should help them acquire the skills and knowledge to engage safely, securely and effectively in a relationship with cognitive systems, and to perform the new kinds of work and jobs that will emerge in a cognitive economy.

By Guru Banavar, IBM’s Chief Science Officer for Cognitive Computing
I am a computer scientist and engineer, inspired by the art of the possible an…
Continue reading “The Science of AI and the Art of Social Responsibility”

Facebook’s advice to students interested in artificial intelligence – Besim on Data

Facebook’s advice to students interested in artificial intelligence.  #WomenScienceDay

  • That’s the gist of the advice to students interested in AI from Facebook’s Yann LeCun and Joaquin Quiñonero Candela who run the company’s Artificial Intelligence Lab and Applied Machine Learning group respectively.
  • If differential equations represents the electricity that powers machine learning, statistics represents the gears of the machine itself — as the company touches on in a series of AI explainer videos we linked to at the bottom of this post.
  • How else would a fledgling machine learning student learn to leverage neuroeconomics and cognitive bias to target ads?
  • Amidst all the talk of News Feed bias, it’s important to remember that there is a human behind every application of machine learning.
  • Most of these tips are self-explanatory: find a professor to work with, consider working with PhD students who have more time on their hands and try to secure an industry-focused internship regardless of your future aspirations to understand how AI works in the real world.

That’s the gist of the advice to students interested in AI from Facebook’s Yann LeCun and Joaquin Quiñonero Candela

 who run the company’s Artificial Intelligence Lab and Applied Machine Learning group respectively.
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6 areas of AI and Machine Learning to watch closely

#ICYMI 6 areas of #AI and #MachineLearning to watch closely

  • Those who are able to train faster and deploy AI models that are computationally and energy efficient are at a significant advantage.
  • Some have rebranded AI as “cognitive computing” or “machine intelligence”, while others incorrectly interchange AI with “machine learning”.
  • If we want AI systems to solve tasks where training data is particularly challenging, costly, sensitive, or time-consuming to procure, it’s important to develop models that can learn optimal solutions from less examples (i.e. one or zero-shot learning).
  • Deep learning models are notable for requiring enormous amounts of training data to reach state-of-the-art performance.
  • Without large scale training data, deep learning models won’t converge on their optimal settings and won’t perform well on complex tasks such as speech recognition or machine translation.


Artificial Intelligence is a generic term and many fields of science overlaps when comes to make an AI application. Here is an explanation of AI and its 6 major areas to be focused, going forward.

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Google set to bring AI to Raspberry Pi computers

  • Eben Upton, founder of the Raspberry Pi Foundation, told the BBC: “It’s fantastic to see Google getting closer to the maker community.”
  • Google has set up its very own survey and is asking makers what smart tools would be the “most helpful”.
  • Google set to bring AI to Raspberry Pi computers
  • Google is on its way to bringing artificial intelligence and machine learning tools to computer Raspberry Pi.
  • “I’m particularly excited about the prospect of connecting Raspberry Pi to some of the machine learning work coming out of Google DeepMind in London, allowing us to build smart devices that interact in the real world.”

Take a look at this interesting tech…
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