ThoughtSpot Raises $60 Million, Ends Funding Round for Artificial Intelligence Technology

U.S. News

ThoughtSpot, based in Palo Alto, California, said on Thursday it had raised $60 million from investors in a financing round led by venture capital firm Lightspeed Venture Partners. The investment round, completed in January but previously undisclosed, was an extension of a $60 million financing round ThoughtSpot completed a year ago and doubles the total amount raised.

Fiat teams up with BMW in race to build 'robotaxis'

Daily Mail

Fiat Chrysler will join an alliance led by BMW to develop self-driving cars, intensifying a race by carmakers and technology companies to develop'robotaxis' which can be called up via smartphone and paid for by the minute. Fiat Chrysler will join an alliance led by BMW to develop self-driving cars, intensifying a race by carmakers and technology companies to develop'robotaxis' which can be called up via smartphone and paid for by the minute BMW and its partners Intel and Mobileye said FCA would bring engineering and other expertise to the deal, paving the way to creating an industry-wide autonomous car platform which other carmakers could adopt. This includes Intel, DENSO, Ericsson, Nippon Telegraph and Telephone Corporation (NTT), NTT DOCOMO and both the Toyota InfoTechnology Center Co. and Toyota Motor Corp. Also joining the consortium are DENSO, Ericsson, Intel, Nippon Telegraph and Telephone Corporation (NTT), and NTT DOCOMO.

Big Data Science with the BD2K-LINCS Data Coordination and Integration Center Coursera


About this course: The Library of Integrative Network-based Cellular Signatures (LINCS) is an NIH Common Fund program. The idea is to perturb different types of human cells with many different types of perturbations such as: drugs and other small molecules; genetic manipulations such as knockdown or overexpression of single genes; manipulation of the extracellular microenvironment conditions, for example, growing cells on different surfaces, and more. Most importantly, the course covers computational methods including: data clustering, gene-set enrichment analysis, interactive data visualization, and supervised learning. Finally, we introduce crowdsourcing/citizen-science projects where students can work together in teams to extract expression signatures from public databases and then query such collections of signatures against LINCS data for predicting small molecules as potential therapeutics.

Matrix Factorization and Advanced Techniques Coursera


About this course: In this course you will learn a variety of matrix factorization and hybrid machine learning techniques for recommender systems. Starting with basic matrix factorization, you will understand both the intuition and the practical details of building recommender systems based on reducing the dimensionality of the user-product preference space. Then you will learn about techniques that combine the strengths of different algorithms into powerful hybrid recommenders.

AI washing muddies the artificial intelligence products market


More than 1,000 vendors with applications and platforms describe themselves as artificial intelligence products vendors, or say they employ AI in their products, according to the research firm. When a technology is labelled AI, the vendor must provide information that makes it clear how AI is used as a differentiator and what problems it solves that can't be solved by other technologies, explained Jim Hare, a research VP at Gartner, who focuses on analytics and data science. Companies that want to answer a specific question or problem should use business analytics tools. Over 50% of respondents to Gartner's 2017 AI development strategies survey said the lack of necessary staff skills was the top challenge to AI adoption.

The essential cookbooks to send to school with your kid

Los Angeles Times

I sent my daughter to school with Judy Rodgers' "The Zuni Cafe Cookbook," Waters' "The Art of Simple Food" and Alton Brown's "I'm Just Here for the Food" because she's a chemistry major at UC Berkeley and I figured the first two were local inspiration and the third would remind her that cooking is really just a chemistry experiment. Not only is "Mastering the Art of French Cooking" about as canonical as it gets, but it's a lot more utilitarian than the title suggests. Richard Olney's "Simple French Food," for one, which is a more prosaic approach. Of course, all these cookbooks may very well be languishing in my daughter's college kitchen, closed, under linear algebra textbooks and boxes of Top Ramen.

Artificial Intelligence: Marketing's New Ally


We can already see glimmers of this zeitgeist in some of the consumer applications now on the market – in any number of offerings from Google, for instance: Google Lens, which gives consumers the ability to learn more about a business simply by snapping a picture; Google Assistant and GoogleHome, which account for and respond to trends in buyers' daily habits. It only makes sense for B2B marketing technologies to follow suit, enabling personalized, one-to-one interactions in real-time across the customer journey. AI will empower marketers to build Adaptive Journeys that adapt to each individual and their preferred channel and send time and delivers the most relevant message in real-time as the customer goes about their research and discovery process. More importantly: this is the kind of continuous learning marketers require in order to deliver the customized experiences their buyers expect, and also the functionality they should prioritize in solutions they implement.

Understanding overfitting: an inaccurate meme in supervised learning


It seems like, a kind of an urban legend or a meme, a folklore is circulating in data science or allied fields with the following statement: Applying cross-validation prevents overfitting and a good out-of-sample performance, low generalisation error in unseen data, indicates not an overfit. Aim In this post, we will give an intuition on why model validation as approximating generalization error of a model fit and detection of overfitting can not be resolved simultaneously on a single model. Let's use the following functional form, from classic text of Bishop, but with an added Gaussian noise $$ f(x) sin(2\pi x) \mathcal{N}(0,0.1).$$ We generate large enough set, 100 points to avoid sample size issue discussed in Bishop's book, see Figure 2. Overtraining is not overfitting Overtraining means a model performance degrades in learning model parameters against an objective variable that effects how model is build, for example, an objective variable can be a training data size or iteration cycle in neural network.

Google acquires AIMatter, maker of the Fabby computer vision app


The search and Android giant has acquired AIMatter, a startup founded in Belarus that has built both a neural network-based AI platform and SDK to detect and process images quickly on mobile devices, and a photo and video editing app that has served as a proof-of-concept of the tech called Fabby. AIMatter had people working in Minsk, the Bay Area, and Zurich (Switzerland being particularly strong in computer vision tech, see here, here, and here). This isn't just for fun apps that let you add dog ears and crazy hair colors to your photos -- although, as Fabby, Prisma, Snapchat, Facebook and countless others have proven, filters can be a lot of fun. But there are also Google's efforts in VR and AR; and its work in building machine learning platforms like TensorFlow for developers to build their own apps using machine learning (AIMatter's SDK could be notable here).