If you follow my content, you know I’m fascinated with Philosophy of Mind. In this blog, I want to just briefly summarize a thought experiment that was imagined by a renowned philosopher, John Searle. It’s likely one of the first items you will read about in this subcategory of philosophy
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So, you think I’m kidding about predicting the future? Just hear me out. Predicting the future is not only possible, but even simple, if you stack the probabilities on your side by making precise statements about the object and time of the prediction.
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It seems that we have two very different problems here: There is the PAC-learning problem from theoretical computer science discussing whether or not machines can learn certain functions. And there is the Continuum Problem asking whether there are infinite sets of a certain size. What does this have to do with each other?
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Every day at work I wrote Machine Learning algorithms, managed existing models, automated workflows and I was proud that not only is AI enabling me in performing better, but it is also increasing my organization’s productivity. To my realization, it was not AI, it was years of work that humans have done by using resources that occur in nature. Do not get me wrong, I believe in AI and do not dislike it, in fact, that’s my daily driver of work. I am only against its usage at present, where it is exploiting more resources than it is generating. My idea is to use AI responsibly and use it for the betterment of lives.
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I’ve tried to rebut some ideas about GPT-3’s threat to coding. Now, I’ll extend the arguments over AI in general. There are 3 strong reasons programmers don’t need to fear AI that much.
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With wildfires becoming an ever more devastating annual phenomenon, it is in the whole planet’s interest to spot them and respond as early as possible — and the best vantage point for that is space. OroraTech is a German startup building a constellation of small satellites to power a global wildfire warning system, and will be using a freshly raised €5.8M (~$7M) A round to kick things off.
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So lately, I’ve been commenting on artificial intelligence. I’m not exactly sure why it interests me so much. I was going to spend a few moments chatting about how AI is being used in job recruitment. This blog is framed around a book written by Professor John Lennox entitled 2084. It’s all about the positive and negative aspects of development in artificial intelligence — It’s actually mostly negative, although he does praise a number of uses for such technology. I thought he was pretty fair in his assessment overall.
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It was philosopher Hubert Dreyfus who first attacked the notions behind the PSS hypothesis. In his 1972 book What Computers Can’t Do, he highlighted a key difference between human intelligence and early, symbolic AI. He argued that a good part of human knowledge is tacit knowledge — know-how experiential knowledge.
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As AI has grown from a menagerie of research projects to include a handful of titanic, industry-powering models like GPT-3, there is a need for the sector to evolve — or so thinks Dario Amodei, former VP of research at OpenAI, who struck out on his own to create a new company a few months ago. Anthropic, as it’s called, was founded with his sister Daniela and its goal is to create “large-scale AI systems that are steerable, interpretable, and robust.”
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Drug discovery is a large and growing field, in which are to be found both ambitious startups and billion-dollar big pharma incumbents. Engine Biosciences is one of the former, a Singaporean outfit with with an expert founding crew and a different approach to the business of finding new therapeutics, and it just raised $43 million to keep growing.
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Google pulled off I/O, its developers conference, without a hitch yesterday. By far, the most interesting news we heard at I/O has to be the announcement that Google intends to build a new quantum AI center in Santa Barbara where the company says it will produce a “useful, error-corrected quantum computer” by 2029.
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ou don’t have a conversation about artificial intelligence nowadays without addressing it’s consequences, good or bad. Whether AI has had negative impacts on our social discourse where blame is put on Facebook’s ad targeting algorithms or whether AI has had positive benefits in places like medicine and bad actor detection, I think most folks miss the point.
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GERO.AI hacking complex diseases and aging. Most drug candidates fail late and in the most expensive way possible lack of efficacy.
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Simulating the universe is difficult. There are, after all, potentially infinite variables to consider. Scientists typically use supercomputers to crunch data at the cosmological level, but a team of researchers from Carnegie Mellon recently figured out a way to use the same machine learning technology used to teach AI to paint or create music like a human to run advanced simulations on graphics processing units (GPUs).
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Nasa is going back to the Moon! But this time, things will be a little different, not just because they are building a lunar base, but also because they are commissioning private companies like SpaceX to build and supply it. The base itself is also an utterly ground breaking piece of space infrastructure that will develop tech that could, one day, open up lunar industries, Martian missions and even crewed missions to other celestial bodies. Welcome to the Artemis program, NASA’s bid to win our new-era space race.
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Artificial intelligence keeps inching its way into more and more aspects of our life, greatly benefitting the world. But it can come with some strings attached, such as bias. AI algorithms can both reflect and propagate bias, causing unintended harm. Especially in a field such as in healthcare.
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it may surprise you that the U.S. Environmental Protection Agency has partnered with researchers at Stanford to algorithmically root out violators of environmental rules.
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The pandemic has fuelled a wave of digitalisation within research and development, with many laboratories adapting to the new way of working by implementing digital software and technology that facilitate remote work.
The disruptive technologies that characterise Laboratory 4.0, from basic connectivity of documentation to automation, advanced analytics and artificial intelligence (AI), have the potential to radically transform the laboratory environment, enabling scientists to work smarter, not harder.
Due to the rapid the developments in AI we are witnessing, dedicated engineers and scientists have unleashed an abundance of technological creations that aid the creation of a brain-like invention. The real test of AI is that of creativity and independent thought.
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Japanese space startup Gitai has raised a $17.1 million funding round, a Series B financing for the robotics startup. This new funding will be used for hiring, as well as funding the development and execution of an on-orbit demonstration mission for the company’s robotic technology, which will show its efficacy in performing in-space satellite servicing […]
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