Artificial intelligence can predict Alzheimer’s disease two years before doctors can, study shows

Artificial intelligence can predict Alzheimer’s disease two years before doctors can: study

  • A computer-driven algorithm was able to accurately foresee whether or not a person would develop Alzheimer’s disease up to two years before he or she displays symptoms, according to a new study from McGill University.
  • Alzheimer’s is the most common form of dementia and clinical trials for drugs to combat the disease must meet a minimum 18 to 24-month run time.
  • “You want to include people who will be progressing from mild cognitive impairment to dementia in the time of the clinical trial,” Rosa-Neto said.
  • Scientists have a tough time predicting the disease on their own through amyloid (a protein that builds up and leads to cognitive damage) PET scans, especially considering that the existence of amyloids in the brain does not necessarily always lead to a dementia diagnosis.
  • The AI technology was then shown the scans of 270 new individuals who had mild cognitive impairments — 43 of whom eventually developed Alzheimer’s — from before they were diagnosed.

Artificial intelligence can pick up on symptoms of Alzheimer’s disease in brain scans long before doctors or patients.
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Facebook Training AI Bots to Negotiate with Humans – News Center

See how Facebook used #GPUs + #AI to train bots to negotiate and compromise with humans.

  • In a new blog post, Facebook explains how existing chatbots can hold short conversations and perform simple tasks such as booking a restaurant – but building machines that can hold meaningful conversations with people is challenging because it requires a bot to combine its understanding of the conversation with its knowledge of the world, and then produce a new sentence that helps it achieve its goals.
  • To help build their training set, the team created an interface with multi-issue bargaining scenarios and crowdsourced humans on Amazon Mechanical Turk to negotiate in natural language to divide a random set of objects.
  • The models were trained end-to-end from the language and decisions that humans made, meaning that the approach can easily be adapted to other tasks.
  • Reinforcement learning was then used to reward the model when it achieved a good outcome which prevents the AI bot from developing its own language.
  • In their experiments, majority of the people didn’t know they were talking to a bot and FAIR’s best reinforcement learning negotiation agent matched the performance of human negotiators – achieving better deals about as often as worse deals.

Researchers at Facebook Artificial Intelligence Research (FAIR) published a paper introducing AI-based dialog agents that can negotiate and compromise.
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Machine Learning In Laymen’s Terms

#MachineLearning In Laymen's Terms -  #ArtificialIntelligence #AI

  • Industry experts are predicting that the combination of Machine learning and hence AI and Internet of Things (IoT) will be the new technological era setter and the businesses, startups, governments etc. will invest huge numbers in the same.
  • I am addicted to this game, and I realized that the particular game is the best way to narrate “What Machine Learning is” with a Layman’s level of knowledge.
  • Let’s see Paper Toss example in Machine and Non-Machine Learning approach.
  • Now, if you add the wind flow using a fan in the system, the system will continuously miss the target because the force and the angle of projection are pre-set using a formula that works only for a particular condition.
  • The most accepted technical definition of Machine Learning is

    “A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.” — Tom Mitchell, Carnegie Mellon University.

Machine Learning A Laymans Understanding.
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March Madness: A Swarm Intelligence Is Predicting the Future

#MarchMadness: A Swarm Intelligence Is Predicting the Future   #AI #IBMInterConnnect

  • When Unanimous AI developed UNU in 2015, the goal was to create artificial intelligence (AI) systems that “keep people in the loop,” amplifying human intelligence instead of replacing it.
  • Unanimous AI’s March Madness bracket was able to beat all but three percent of ESPN brackets across the country on the first day of the tournament.
  • The technology makes use of the collective intelligence of people — combining “knowledge, insights, and intuitions,” as Unanimous AI puts it — to develop a kind of artificial intelligence that’s inherently human.
  • As Unanimous AI explains, “We empower people to act as ‘data processors’ that come together online and form an intelligent system, connected by AI algorithms.
  • One day, perhaps we’ll be able to combine the intelligence of Watson with that of a swarm of medical professionals to improve healthcare, or combine the insights of an investment-making AI with a swarm of finance experts.

The collective intelligence is killing it on ESPN.
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New AI Can Write and Rewrite Its Own Code to Increase Its Intelligence

This new #AI can rewrite its own code to get even smarter  via @futurism

  • This form of probabilistic programming — a code that uses probabilities instead of specific variables — requires fewer examples to make a determination, such as, for example, that the sky is blue with patches of white clouds.
  • Gamalon CEO and cofounder Ben Vigoda showed MIT Technology Review a demo drawing app that uses their new method.
  • However, unlike Google’s version, which relied on sketches it had previously seen to make predictions, Gamalon’s app relies on probabilistic programming to identify an object’s key features.
  • One product, the Gamalon Structure, using Bayesian program synthesis to recognize concepts from raw text, and it does so more efficiently than what’s normally possible.
  • For example, if equipped with a Beysian model of machine learning, smartphones or laptops wouldn’t need to share personal data with large companies to determine user interests; the calculations could be done effectively within the device.

Teaching machines could be much easier using this tech.
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