Artificial Intelligence

It is my suspicion though that this meta meta-model approach if pursued in
greater detail may the key in locking “Unsupervised learning” or
alternatively “Predictive learning” . Perhaps our puny human brains cannot
figure this out. However armed with meta-learning capabilities, it may be
possible for machines to continually self improve upon themselves. The
reason that this may not work however is that the vocabulary or language is
not available and furthermore somehow not derivable through this
bootstrapping method.

Anything can be automated. At least, that seems to be the industry
perspective. Humans are rash, impulsive and tainted by personal bias. So
why not just replace them with machines? Apparently, that’s what Facebook
thought when they fired their entire team of news editors for political
bias, replacing them instead with a new algorithm. That did not go well.

Online maths company has partnered with scientists to identify what makes
lessons successful – and to see if AI can be used to improve teaching.

Earlier this month, Apple made a splash when it told the artificial
intelligence research community that the secretive company would start
publishing AI papers of its own. Not even a month later, it’s already
starting to make good on that promise.

As AI evolves, the role of the computer scientist is changing. Sure, the
world still needs people who can code software. But increasingly, it also
needs people who can train neural networks, a very different skill that’s
more about coaxing a result from the data than building something on your
own. Companies like Google and Facebook are not only hiring a new kind of
talent, but also reeducating their existing employees for this new future –
a future where AI will come to define technology in the lives of just about
everyone.

Can machines really think? Here is a series of interviews with some of the
AI pioneers, Jerome Wiesner, Oliver Selfridge, and Claude Shannon. A view
of the future of computer intelligence from back then.

Bridgewater Associates has a team of engineers working on a project to
automate decision-making to save time and eliminate human emotional
volatility.

Move over Mariah Carey. There’s a new holiday star in town. Behold, a new
Christmas carol for the 2016 holiday season! AI writes a Christmas carol.
Listen to it here.

How Google used artificial intelligence to transform Google Translate, one
of its more popular services – and how machine learning is poised to
reinvent computing itself.

Diabetic retinopathy (DR) is the fastest growing cause of blindness, with
nearly 415 million diabetic patients at risk worldwide. If caught early,
the disease can be treated; if not, it can lead to irreversible blindness.
Unfortunately, medical specialists capable of detecting the disease are not
available in many parts of the world where diabetes is prevalent. We
believe that Machine Learning can help doctors identify patients in need,
particularly among underserved populations.