"Many researchers … speculate that the information-processing abilities of biological neural systems must follow from highly parallel processes operating on representations that are distributed over many neurons. [Artificial neural networks] capture this kind of highly parallel computation based on distributed representations"
– from Machine Learning (Section 4.1.1; page 82) by Tom M. Mitchell, McGraw Hill Companies, Inc. (1997).
Could a robot do my job as a radiologist? If you asked me 10 years ago, I would have said, "No way!" But if you ask me today, my answer would be more hesitant, "Not yet -- but perhaps someday soon." In particular, new "deep learning" artificial intelligence (AI) algorithms are showing promise in performing medical work which until recently was thought only capable of being done by human physicians. For example, deep learning algorithms have been able to diagnose the presence or absence of tuberculosis (TB) in chest x-ray images with astonishing accuracy.
It's common to hear phrases like'machine learning' and'artificial intelligence' and believe that somehow, someone has managed to replicate a human mind inside a computer. This, of course, is untrue--but part of the reason this idea is so pervasive is because the metaphor of human learning and intelligence has been quite useful in explaining machine learning and artificial intelligence. Indeed, some AI researchers maintain a close link with the neuroscience community, and inspiration runs in both directions. But the metaphor can be a hindrance to people trying to explain machine learning to those less familiar with it. One of the biggest risks of conflating human and machine intelligence is that we start to hand over too much agency to machines.
AJ Abdallat is a serial entrepreneur. He loves to create and the businesses he puts his hands on tend to succeed. Abdallat's latest company is Beyond Limits, a leading developer of advanced artificial intelligence solutions and leading the way in developing the technology in innovative ways. Abdallat took over the CEO reins of Beyond Limits in 2014. During his time with the company, the entrepreneur has pushed the brand to tackle industrial and enterprise challenges. A graduate of the University of California, Berkeley, Abdallat has been working with dynamic companies since 1988 as co-founder or CEO of several Caltech/Jet Propulsion Laboratory (JPL) startups. With a long track record in AI, Abdallat hopes to steer Beyond Limits -- and the world -- into a future that finally fulfills the promises of technology.
The Deep Learning Summit is returning to Toronto from October 25 – 26, 2018 and will cover the latest advancements in deep learning technology. Global leaders in the field will address how industry leaders and start-ups are applying deep learning techniques across industry and society. The first ever AI for Government Summit, another event stream, will provide a unique opportunity to interact with government bodies, policymakers, strategists and directors of innovation to explore the use of machine learning to increase efficiency, reduce costs and meet the high demands of the public sector. What's more, the Canadian Government have committed over $125 million to AI developments. Headline partners include Accenture, Qualcomm, Graphcore AI and CBC/Radio Canada who will all be sharing their expertise in the field, participating in workshops, discussions, presentations, demonstrations and exhibitions.
With the weekend here (woot!), we rounded up the best deals on electronics, stuff for the kitchen, and Amazon devices for video streaming and home security so you can do some online shopping during your down time. We also have deals on web design and coding courses from Udemy, in case you're the kind of person who can't just relax. SEE ALSO: You know what's awesome? The new Tile Mate Bluetooth trackers are on sale for $40 off, that's what. Walmart has a number of deals on the Apple iPad, such as Apple's 10.5-inch iPad Pro Wi-Fi in Rose Gold, which is on sale for $570.99,
Apple has quietly bought Spektral, a Danish machine learning startup that specializes in real-time green screen technology. The $30 million deal actually happened last year, but it was reported today by Danish newspaper Børsen. Apple has been focusing more and more on its AR capabilities lately, and this latest acquisition may be meant to boost the iPhone's AR features for Memoji or FaceTime or as a part of its plans for an augmented reality headset, which Bloomberg reported may be coming in 2020. Spektral, which previously went by the name CloudCutout, uses machine learning and computer vision techniques to "cut out" people from video backgrounds in real time on smartphones. "Combining deep neural networks and spectral graph theory with the computing power of modern GPUs, our engine can process images and video from the camera in real-time (60 fps) directly on the device," the company explained on its website.
A liquid state machine (LSM) is a particular kind of spiking neural network. An LSM consists of a large collection of units (called nodes, or neurons). Each node receives time varying input from external sources (the inputs) as well as from other nodes. Nodes are randomly connected to each other. The recurrent nature of the connections turns the time varying input into a spatio-temporal pattern of activations in the network nodes.
IBM today introduced AI OpenScale, a new technology platform that addresses key challenges of artificial intelligence (AI) adoption, such as concerns over how AI applications make decisions, the global shortage of AI skills and the complexities of working with disparate AI tools from multiple vendors. IBM's new technology platform is the first of its kind. It will enable companies to manage AI transparently throughout the full AI lifecycle, irrespective of where their AI applications were built or in which environment they currently run. AI OpenScale can detect and address bias across the spectrum of AI applications, as those applications are being run. As part of AI OpenScale, IBM also will debut NeuNetS, a major scientific breakthrough in which AI builds AI – making it possible to create complex, deep-neural networks from scratch.
Mention the phrase "robot psychologist" and meme-worthy images of automatons or perhaps human-like robot hosts fictionalized by HBO's Westworld may come to mind. Yet part of what practicing psychologists do, such as administering certain types of psychological tests, assessments, and questionnaires, can be automated -- the technical capabilities exist today. And the technology is growing exponentially more sophisticated. For example, researchers at MIT have created an artificial neural network computer model that can detect depression from natural conversation . Will robot psychologists be commonplace one day?
In this post, I will try to shed some light on the intuition about Restricted Boltzmann Machines and the way they work. This is supposed to be a simple explanation with a little bit of mathematics without going too deep into each concept or equation. So let's start with the origin of RBMs and delve deeper as we move forward. Boltzmann machines are stochastic and generative neural networks capable of learning internal representations and are able to represent and (given sufficient time) solve difficult combinatoric problems. They are named after the Boltzmann distribution (also known as Gibbs Distribution) which is an integral part of Statistical Mechanics and helps us to understand the impact of parameters like Entropy and Temperature on the Quantum States in Thermodynamics.