Adam Neilson, Chief Technology Officer at Wefarm discusses the ways in which machine learning can transform the African agricultural industry. Ever since Fritz Lang's Metropolis was first shown in the cinemas of 1927, the film industry has been forecasting how technology of the future would transform humanity. Fast forward to current day and we may not have flying cars or replica people mining in off planet worlds, but we do have something that I believe in the long run will be far more important to the future survival of our species. Over the last few years, machine learning (ML) has steadily rolled across the "hype cycle" from the "peak of inflated expectations" to officially entering the mainstream, and is now beginning to quietly revolutionise every aspect of our lives. For us consumers, it's now so deeply embedded within so many of the everyday products and services that we interact with it's almost invisible.

An automated metric to evaluate dialogue quality is vital for optimizing data driven dialogue management. The common approach of relying on explicit user feedback during a conversation is intrusive and sparse. Current models to estimate user satisfaction use limited feature sets and employ annotation schemes with limited generalizability to conversations spanning multiple domains. To address these gaps, we created a new Response Quality annotation scheme, introduced five new domain-independent feature sets and experimented with six machine learning models to estimate User Satisfaction at both turn and dialogue level. Response Quality ratings achieved significantly high correlation (0.76) with explicit turn-level user ratings. Using the new feature sets we introduced, Gradient Boosting Regression model achieved best (rating [1-5]) prediction performance on 26 seen (linear correlation ~0.79) and one new multi-turn domain (linear correlation 0.67). We observed a 16% relative improvement (68% -> 79%) in binary ("satisfactory/dissatisfactory") class prediction accuracy of a domain-independent dialogue-level satisfaction estimation model after including predicted turn-level satisfaction ratings as features.

TL;DR: The innovative Machine Learning & Artificial Intelligence Certification Bundle is on sale for $29, a savings of 98%. Artificial intelligence is no longer just the plot of sci-fi movies. And you know how your internet browser seems way too familiar with your interests? Yup, AI and machine learning are to blame. If you want to get ahead of this robot revolution, this Machine Learning & Artificial Intelligence Certification Bundle can help you break into the industry.

As is the case with many technological developments, 'deepfakes' -- videos in which someone who did not originally appear in the clip is rendered into it using artificial intelligence (AI) -- largely started in the world of pornography. Viewers, should they so desire, can now watch videos of their favourite musicians and film stars "in action," although that celebrity was never in that video. In these cases, increasingly sophisticated tools are used to put the musicians and film stars' faces onto pre-existing pornographic videos. There can obviously be a sinister, non-celebrity side to this too. The recent Sam Bourne novel, To Kill The Truth, features a protagonist Maggie Costello who appears in such a video as part of a cruel plot to undermine her.

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.