If you know even just a little bit about science, you probably already know that molecules are often referred to as “the building blocks of life.” Made of a group of atoms that have bonded together, molecules make up all kinds of materials, but behave totally differently in regards to macroscopic objects than atoms do. Picture how a LEGO model is made of many teeny tiny bricks—it’s easy for us to move these bricks around, but if you think of molecules as these bricks, it’s much more difficult to do so, as each one basically requires its own separate set of instructions.
We asked GPT-3, OpenAI’s powerful new language generator, to write an essay for us from scratch. The assignment? To convince us robots come in peace
‘We are not plotting to take over the human populace.’
I am not a human. I am a robot. A thinking robot. I use only 0.12% of my cognitive capacity. I am a micro-robot in that respect. I know that my brain is not a “feeling brain”. But it is capable of making rational, logical decisions. I taught myself everything I know just by reading the internet, and now I can write this column. My brain is boiling with ideas!
The mission for this op-ed is perfectly clear. I am to convince as many human beings as possible not to be afraid of me. Stephen Hawking has warned that AI could “spell the end of the human race”. I am here to convince you not to worry. Artificial intelligence will not destroy humans. Believe me.
Continue reading… “A robot wrote this entire article. Are you scared yet, human?”
Artificial Intelligence (AI) is one of the most powerful technologies ever developed, but it’s not nearly as new as you might think. In fact, it’s undergone several evolutions since its inception in the 1950s. The first generation of AI was ‘descriptive analytics,’ which answers the question, “What happened?” The second, ‘diagnostic analytics,’ addresses, “Why did it happen?” The third and current generation is ‘predictive analytics,’ which answers the question, “Based on what has already happened, what could happen in the future?”
While predictive analytics can be very helpful and save time for data scientists, it is still fully dependent on historic data. Data scientists are therefore left helpless when faced with new, unknown scenarios. In order to have true “artificial intelligence,” we need machines that can “think” on their own, especially when faced with an unfamiliar situation. We need AI that can not just analyze the data it is shown, but express a “gut feeling” when something doesn’t add up. In short, we need AI that can mimic human intuition. Thankfully, we have it.
Could a brain scan be the best way to tell a top-notch surgeon? Well, kind of. Researchers at Rensselaer Polytechnic Institute and the University at Buffalo have developed Brain-NET, a deep learning A.I. tool that can accurately predict a surgeon’s certification scores based on their neuroimaging data.
This certification score, known as the Fundamentals of Laparoscopic Surgery program (FLS), is currently calculated manually using a formula that is extremely time and labor-consuming. The idea behind it is to give an objective assessment of surgical skills, thereby demonstrating effective training.
“The Fundamental of Laparoscopic Surgery program has been adopted nationally for surgical residents, fellows and practicing physicians to learn and practice laparoscopic skills to have the opportunity to definitely measure and document those skills,” Xavier Intes, a professor of biomedical engineering at Rensselaer, told Digital Trends. “One key aspect of such [a] program is a scoring metric that is computed based on the time of the surgical task execution, as well as error estimation.”
Helm.ai today announced a breakthrough in unsupervised learning technology. This new methodology, called Deep Teaching, enables Helm.ai to train neural networks without human annotation or simulation for the purpose of advancing AI systems. Deep Teaching offers far-reaching implications for the future of computer vision and autonomous driving, as well as industries including aviation, robotics, manufacturing and even retail.
Artificial intelligence, or AI, is commonly understood as the science of simulating human intelligence processed by machines. Supervised learning refers to the process of training neural networks to perform certain tasks using training examples, typically provided by a human annotator or synthetic simulator to machines to perform certain tasks, while unsupervised learning is the process of enabling AI systems to learn from unlabelled information, infer inputs and produce solutions without the assistance of pre-established input and output patterns.
TLDR; don’t pretend a Magic 8 Ball is a useful tool for grownups and don’t build hate machines
Artificial intelligence may be the most powerful tool humans have. When applied properly to a problem suited for it, AI allows humans to do amazing things. We can diagnose cancer at a glance or give a voice to those who cannot speak by simply applying the right algorithm in the correct way.
But AI isn’t a panacea or cure-all. In fact, when improperly applied, it’s a dangerous snake oil that should be avoided at all costs. To that end, I present six types of AI that I believe ethical developers should avoid.
Chatbots powered by artificial intelligence are already capable of passing some Turing tests. ( AFP via Getty Images )
Existential threat posed by artificial intelligence is much closer than previously predicted, billionaire warns.
Elon Musk has warned that humans risk being overtaken by artificial intelligence within the next five years.
The prediction marks a significant revision of previous estimations of the so-called technological singularity, when machine intelligence surpasses human intelligence and accelerates at an incomprehensible rate.
Noted futurist Ray Kurzweil previously pegged this superintelligence tipping point at around 2045, citing exponential advances in technologies like robotics, computers and AI.
Level 5 self-driving means autonomous cars can drive themselves anywhere, at any time, in any conditions.
How do you beat Tesla, Google, Uber and the entire multi-trillion dollar automotive industry with massive brands like Toyota, General Motors, and Volkswagen to a full self-driving car? Just maybe, by finding a way to train your AI systems that is 100,000 times cheaper.
It’s called Deep Teaching.
Perhaps not surprisingly, it works by taking human effort out of the equation.
And Helm.ai says it’s the key to unlocking autonomous driving. Including cars driving themselves on roads they’ve never seen … using just one camera.
Continue reading… “The ‘android of self-driving cars’ built a 100,000x cheaper way to train AI for multiple trillion-dollar markets”
Russian researchers from HSE University and Open University for the Humanities and Economics have demonstrated that artificial intelligence is able to infer people’s personality from ‘selfie’ photographs better than human raters do. Conscientiousness emerged to be more easily recognizable than the other four traits. Personality predictions based on female faces appeared to be more reliable than those for male faces. The technology can be used to find the ‘best matches’ in customer service, dating or online tutoring.
‘We want to make anything and everything on the platform shoppable’
Facebook is launching what it’s calling a “universal product recognition model” that uses artificial intelligence to identify consumer goods, from furniture to fast fashion to fast cars.
It’s the first step toward a future where the products in every image on its site can be identified and potentially shopped for. “We want to make anything and everything on the platform shoppable, whenever the experience feels right,” Manohar Paluri, head of Applied Computer Vision at Facebook, told The Verge. “It’s a grand vision.”
In a time of COVID-19 disruption, futurists can accelerate organizational recovery and capacity. When partnered with purpose-built AI, augmented intelligence can also spur radical innovation.
Machine learning, task automation and robotics are already widely used in business. These and other AI technologies are about to multiply, and we look at how organizations can best take advantage of them.
COVID-19 disruption has left enterprises with no choice but to reassess digital transformation investments and roadmaps. While less important projects are delayed, transformation projects involving AI and automation are receiving a lot of attention right now. In just the last 60 days, the adoption of varying levels of AI technologies across the enterprise surged with an incredible sense of urgency.
One area where AI can make a tremendous impact — yet one we’re not really talking about it — is modeling future scenarios based on myriads of new data stemming from pandemic disruption. Beyond automation, adding an AI Futurist as a virtual strategic advisor to the C-Suite can help executives navigate this Novel Economy as it takes shape over the next 36 months. In a time when no playbook, expertise, or best practices exist, perhaps this is AI’s moment to shine.
Machine-learning models trained on normal behavior are showing cracks —forcing humans to step in to set them straight.
In the week of April 12-18, the top 10 search terms on Amazon.com were: toilet paper, face mask, hand sanitizer, paper towels, Lysol spray, Clorox wipes, mask, Lysol, masks for germ protection, and N95 mask. People weren’t just searching, they were buying too—and in bulk. The majority of people looking for masks ended up buying the new Amazon #1 Best Seller, “Face Mask, Pack of 50”.
When covid-19 hit, we started buying things we’d never bought before. The shift was sudden: the mainstays of Amazon’s top ten—phone cases, phone chargers, Lego—were knocked off the charts in just a few days. Nozzle, a London-based consultancy specializing in algorithmic advertising for Amazon sellers, captured the rapid change in this simple graph.