If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
At this year's CES, I wrote about a new wave of smart hearing tech. Some were assistive devices designed for those with only mild problems. Others were full-fledged hearing aids stuffed with smart features. This year, hearing technology continues to move away from stuffy medical devices to the more razzmatazz world of consumer electronics. Two of the biggest brands, Phonak and Widex, each have new flagship offerings this year.
Suppose you're trying to engage in a conversation with a founder or CEO, you'll probably hear them speaking about artificial intelligence (AI) and machine learning (ML). And they'll probably tell you how these innovative technologies can transform their business. Machine learning (ML) has real-life applications, so typically that we often tend to overlook it! From switching on the phone by facial recognition to more complicated recommender algorithms that influence your decision to watch or shop next, machine learning is making quite a noise for now. ML is described as making machines learn to imitate human actions through complex coding started in Python, R, C, C#, Java, etc.
Last January at rstudio::conf, in that distant past when conferences still used to take place at some physical location, my colleague Daniel gave a talk introducing new features and ongoing development in the tensorflow ecosystem. In the Q&A part, he was asked something unexpected: Were we going to build support for PyTorch? He hesitated; that was in fact the plan, and he had already played around with natively implementing torch tensors at a prior time, but he was not completely certain how well "it" would work. "It", that is an implementation which does not bind to Python Torch, meaning, we don't install the PyTorch wheel and import it via reticulate. Instead, we delegate to the underlying C library libtorch for tensor computations and automatic differentiation, while neural network features – layers, activations, optimizers – are implemented directly in R. Removing the intermediary has at least two benefits: For one, the leaner software stack means fewer possible problems in installation and fewer places to look when troubleshooting.
The mathematician and computer science pioneer Alan Turing hit on a promising direction for artificial intelligence research way back in 1950. "Instead of trying to produce a program to simulate the adult mind," he wrote, "why not rather try to produce one which simulates the child's?" Now AI researchers are finally putting Turing's ideas into action. They're realizing that by paying attention to how children process information, they can pick up valuable lessons about how to create machines that learn. DARPA, the Defense Department's advanced research agency, is embracing this approach.
Estonia-based Sentinel, which is developing a detection platform for identifying synthesized media (aka deepfakes), has closed a $1.35 million seed round from some seasoned angel investors -- including Jaan Tallinn (Skype), Taavet Hinrikus (TransferWise), Ragnar Sass & Martin Henk (Pipedrive) -- and Baltics early-stage VC firm, United Angels VC. The challenge of building tools to detect deepfakes has been likened to an arms race -- most recently by tech giant Microsoft, which earlier this month launched a detector tool in the hopes of helping pick up disinformation aimed at November's U.S. election. "The fact that [deepfakes are] generated by AI that can continue to learn makes it inevitable that they will beat conventional detection technology," it warned, before suggesting there's still short-term value in trying to debunk malicious fakes with "advanced detection technologies." Sentinel co-founder and CEO Johannes Tammekänd agrees on the arms race point -- which is why its approach to this "goal-post-shifting" problem entails offering multiple layers of defence, following a cybersecurity-style template. He says rival tools -- mentioning Microsoft's detector and another rival, Deeptrace, aka Sensity -- are, by contrast, only relying on "one fancy neural network that tries to detect defects," as he puts it.
For example, if we have 43 instances of the training set in the node of which 13 belong to one class, while 30 instances belong to a second class. Given that we have only those two classes in the training dataset, we calculate Gini impurity 1 – (13/43)2 – (30/43)2 1 – 0.09 – 0.49 0.42. When the node is "pure" its Gini index is 0. On the other hand, information gain lets us find the best threshold which will reduce this impurity the most. To calculate information gain we need to calculate average impurity and then subtract that from the starting impurity. That is how we know the quality of thresholds that we used.
Amsterdam and Helsinki became the first two cities in the world to launch AI-based registers that log algorithms used in municipalities. Finnish developer Saidot created the registers used by both cities. The cities announced this development at the New Generation Internet Policy Summit organized by the European Commission. According to the Government AI Readiness Index 2020, Netherlands and Finland are the most prepared to adapt AI into government services. Currently, the AI registers in the two cities contain only a handful of applications.
Scientists have created a robotic fabric that stiffens and relaxes in response to changes in temperature, which could be used in emergency situations. The material, developed at Yale University in the US, is equipped with a system of heat sensors and threads that stiffen to change the fabric's shape. Under heat changes, it can bend and twist to transform itself into adaptable clothing, shape-changing machinery and self-erecting shelters. Video footage shows the material going from a flat, ordinary fabric to a load-bearing structure supporting a weight, a model airplane with flexible wings and a wearable robotic tourniquet that activates in response to damage. 'We believe this technology can be leveraged to create self-deploying tents, robotic parachutes, and assistive clothing,' said Professor Rebecca Kramer-Bottiglio at Yale University.
The LG Stylo 6 phone comes with a 6.8 inch screen that's bigger than the iPhone 11 Pro Max, has a stylus and a hefty 64 GB of internal storage – on par with the iPhone and Galaxy. What's also different is the retail price: $299 vs. the other phones, which both start in the $1,000 range? The phone could appeal to those looking for basic calls, texts and email reading, but when it comes to moving files, watching video and the like, you'll probably want to shop elsewhere. The Stylo 6 has picked up good reviews for most of its features except for one very important one – sluggish performance. That's the tradeoff you're going to have to make.
When customers call a company, sometimes they are not in a good mood. Years ago, I did a project advising a governmental tax agency (yes, that one) on improving customer service to citizens. My proposal was simple: give agents more freedom to be offline whenever they needed to be. Agents would be able to take breaks at any time. Once adopted, this proved to be empowering.