AI isn’t just learning to play video games, it’s helping us build them

AI isn't just learning to play video games, it's helping us build them

  • The creators of Unity, the most popular game engine in the world, recently launched a set of machine-learning tools that lay the groundwork for actual AI (not scripted computer opponents) in video games.
  • Typically these kits include rendering aids and simple tools for training neural networks, but the beta release sent to developers promises to revolutionize video games, and provides machine-learning researchers with a perfect environment for training robot brains.
  • Unity provides developers with the tools to create machine-learning agents capable of learning and interacting with each other in a virtual world, which makes it possible to create games inhabited by AI that actually learns, instead of forcing developers into painstakingly scripting behavior by hand.
  • Video game developers have been using the term “artificial intelligence” (AI) since the 1950s to describe a computer opponent designed to challenge humans.
  • This use of the term has no relation to machine-learning; the AI in a video game doesn’t learn anything, it simply executes algorithms.

Unity developers recently got an AI upgrade in the form of machine-learning tools that provide game and AI programmers with next generation capabilities.
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Medicine in the Age of AI

Medicine in the Age of AI
#AI #MachineLearning #DeepLearning #ML #DL #HealthTech #tech

  • I have not seen any significant use of AI in my own clinical practice during my time in Boston, but exciting developments in the last two years hold much promise.
  • One of the potential benefits when integrating AI into medical practice is improvement of clinical decision making and diagnosis.
  • The concept of using AI to provide clinical decision support systems for physicians has been studied in some medical specialties with varying degrees of effectiveness.
  • Comparisons between the paradigms of machine learning based, knowledge based, and hybrid methods have not yielded a clear model on how AI best uses clinical data to arrive at a diagnosis.
  • There is promise that Deep Learning methodology will allow for AI to train much like a medical resident does through a large data set of disease presentations.

A discussion on Dr. Michael Forsting’s article “Machine Learning Will Change Medicine” in the Journal of Nuclear Medicine.
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Google’s AI Guru Says That Great Artificial Intelligence Must Build on Neuroscience

Google’s #AI guru says that great artificial intelligence must build on neuro science

  • Demis Hassabis knows a thing or two about artificial intelligence: he founded the London-based AI startup DeepMind, which was purchased by Google for $650 million back in 2014.
  • In a paper published today in the journal Neuron, Hassabis and three coauthors argue that only by better understanding human intelligence can we hope to push the boundaries of what artificial intellects can achieve.
  • But it also points out that more recent advances haven’t leaned on biology as effectively, and that a general intelligence will need more human-like characteristics—such as an intuitive understanding of the real world and more efficient ways of learning.
  • As Hassabis explains in an interview with the Verge, artificial intelligence and neuroscience have become “two very, very large fields that are steeped in their own traditions,” which makes it “quite difficult to be expert in even one of those fields, let alone expert enough in both that you can translate and find connections between them.”
  • (Read more: Neuron, The Verge, “Google’s Intelligence Designer,” “Can This Man Make AI More Human?”)

Inquisitiveness and imagination will be hard to create any other way.
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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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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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