Data science and machine learning platforms are a changed part.

If you enjoyed this content, please share! As we've been at pains to describe elsewhere, hype hurts AI. Add to that a busy legal AI vendor space – some 67 "AI" products in 11 verticals by one count in 2018. Most buyers of legal AI products are left confused in terms of what to ask AI vendors in order to understand what to buy. Dollop lashings of "Robot Lawyer" articles replete stock photo of gavel wielding android and it's no wonder some AI vendors play fast and loose, whether deliberately or by omission, with their product claims. This article provides 17 basic questions you can use to test the knowledge of AI vendors (or experts) regarding AI and whether what they are selling / telling you is right for your need. This article is not meant to be exhaustive, nor demonstrate how to authoritatively benchmark one tool vs. another – hopefully we can cover that in a later post! For now, these are indicative of the types of things should try to know about AI vendors to help you make the right decisions.

Failure to Scale Artificial Intelligence Could Put 75% of Organizations Out of Business, Accenture Study Shows Companies that shift from AI experimentation to execution achieve lasting ROI and competitive agility NEW YORK; Nov. 14, 2019 – Three-quarters of C-level executives believe if they don't move beyond experimentation to aggressively deploy artificial intelligence (AI) across their organizations they risk going out of business by 2025, according to a newly released study from Accenture (NYSE: ACN). The report, titled "AI: Built to Scale" and produced by Accenture Strategy and Accenture Applied Intelligence, is based on a global survey of 1,500 C-level executives across 16 industries designed to understand how companies are implementing AI across their organizations. The research found 84% of C-level executives believe they won't achieve their business strategy without scaling AI, yet only 16% have made the shift from mere experimentation to creating an organization powered by robust AI capabilities. As a result, this small group of top performers is achieving nearly three times the return from AI investments as their lower-performing counterparts. The report reveals the secret to success for these top performers centers around three key elements: a strong data foundation; multiple dedicated AI teams; and a C-suite-led commitment to strategic, organization-wide AI deployment.

Should being comparable to BERT really be your goal here? The thing about BERT is that it wasn't really specifically designed for sentiment analysis. It just happens that it does that well too. But there's no reason to believe it's anywhere close to the "best way" to do sentiment analysis. I mean, as an analogy, pulling out a calculator to make a quick computation is often more convenient than booting up Matlab to do it, but using this fact to extol the merits of calculator kind of misses the point. If you want to describe how good your model is, you really should choose more relevant comparisons.

Groq calls its architecture Tensor Streaming Processor (TSP). Two years back it said it had recruited eight of the ten people who developed Google's Tensor Processing Unit (TPU). The company has raised $62.3 million in funding. Groq's architecture is equivalent to one quadrillion operations per second, or 1e15 ops/s and capable of up to 250 trillion floating-point operations per second (FLOPS). "Top GPU companies have been telling customers that they'd hoped to be able to deliver one PetaOp/s performance within the next few years; Groq is announcing it today," says Groq CEO Jonathan Ross, "the Groq architecture is many multiples faster than anything else available for inference, in terms of both low latency and inferences per second. We had first silicon back, first-day power-on, programs running in the first week, sampled to partners and customers in under six weeks, with A0 silicon going into production" With a software-first mindset, Groq's TSP architecture claims to achieve both compute flexibility and massive parallelism without the synchronization overhead of traditional GPU and CPU architectures.

VA2CS, the European leader in AI-Powered Fall Detection and Prevention, is launching its product at GSA 2019! The solution is already deployed in 2,700 rooms in 6 European countries. Artificial Intelligence uses visual and infrared sensors to automatically detect when a person is falling and trigger alarm to care-givers. A short recording of the fall allows care-givers to understand the reasons for the fall and to take corrective actions. "Most existing solutions based on a press-button pendant or watch are not reliable; people just don't wear them or forget to press the button when the fall happens," says Ramzi Larbi, VA2CS CEO.

There's been a turbulence in the Star Wars franchise recently – and that's a good thing. First, there was the arrival of "The Mandalorian" on the Disney streaming service, which became operational earlier this week. All this action comes ahead of "Star Wars: The Rise of Skywalker" (in theaters Dec. 20), the ninth and last film in that saga. But these two recent entries focus on the past. "The Mandalorian" takes place after about five years after Luke Skywalker, Princess Leia, Han Solo and the rebels overthrew the Empire in 1983's "Star Wars: Return of the Jedi."

Deep Neural Networks (DNNs) are susceptible to model stealing attacks, which allows a data-limited adversary with no knowledge of the training dataset to clone the functionality of a target model, just by using black-box query access. Such attacks are typically carried out by querying the target model using inputs that are synthetically generated or sampled from a surrogate dataset to construct a labeled dataset. The adversary can use this labeled dataset to train a clone model, which achieves a classification accuracy comparable to that of the target model. We propose "Adaptive Misinformation" to defend against such model stealing attacks. We identify that all existing model stealing attacks invariably query the target model with Out-Of-Distribution (OOD) inputs. By selectively sending incorrect predictions for OOD queries, our defense substantially degrades the accuracy of the attacker's clone model (by up to 40%), while minimally impacting the accuracy (<0.5%) for benign users. Compared to existing defenses, our defense has a significantly better security vs accuracy trade-off and incurs minimal computational overhead.

Newswire) GBT Technologies Inc. (OTCPINK: GTCH) ("GBT", or the "Company"), a company specializing in the development of Internet of Things (IoT) and Artificial Intelligence (AI) enabled networking and tracking technologies, including its GopherInsight wireless mesh network technology platform and its Avant! AI, for both mobile and fixed solutions, announced that it is implementing its Avant! AI technology within Epsilon EDA (Electronic Design Automation) program with the goal of achieving increased reliability for microchips. AI will be trained with IC (Integrated Circuit) reliability models, based on physics-of-failure mechanisms. These models will be classified for a wide variety of microchips types, among them microcontrollers, microprocessors, memories, power ICs and others.

Coral reefs are among nature's most complex and colourful living formations. But as any underwater photographer knows, pictures of them taken without artificial lights often come out bland and blue. Even shallow water selectively absorbs and scatters light at different wavelengths, making certain features hard to see and washing out colours--especially reds and yellows. This effect makes it difficult for coral scientists to use computer vision and machine-learning algorithms to identify, count and classify species in underwater images; they have to rely on time-consuming human evaluation instead. But a new algorithm called Sea-thru, developed by engineer and oceanographer Derya Akkaynak, removes the visual distortion caused by water from an image. The effects could be far-reaching for biologists who need to see true colors underneath the surface.