The famous inventor and computer scientist Ray Kurzweil has made some very bold predictions about the pace at which human technology is advancing toward the ultimate threshold. That epithet is a metaphor borrowed from physics terminology to express the point at which information technology--specifically artificial intelligence--becomes sufficiently advanced as to irreversibly alter the course of history on earth. Kurzweil's model predicts that by 2029 technological advancement will be occurring at such a rapid and explosive rate that humans will not be able to keep up without merging symbiotically with machines. And by 2045, AI is predicted to surpass human beings as the most intelligent and capable beings on the planet.
But the reason I believe that no investor will be funding startups calling themselves AI-powered startups (and no startup CEO will differentiate themselves as an AI-first company like Google) is because investors will assume the startup is using the best available AI techniques to solve the problem they are solving. Not having state-of-the-art AI techniques powering their software would be like not having a relational database in their tech stack in 1980 or not having a rich Windows client in 1987 or not having a Web-based front end in 1995 or not being cloud native in 2004 or not having a mobile app in 2009. In this way, AI is no different than any other foundational computer science technique that gets widely adopted. Every now and again, the industry invents a new set of "must adopt" computer science techniques that find their way into all important software we use.
The goal is to train students to build complex software systems or powerful robots that utilize multiple different AI technologies, whether it be machine learning tech to help those systems learn from data or technology that helps robots see and perceive the world similar to humans. However, there hasn't been a standardized way to develop these complex projects that require multiple AI technologies to function together. Similar to how building a skyscraper requires people with expertise in diverse fields like structural engineering and concrete mixing, building powerful software like Siri or robots requires people with expertise in many different areas of AI. "We have done a good job of covering all the component parts," Moore said of teaching different subsets of AI like machine learning and computer vision.
So, ShopClues plans to use advanced technologies to make it easier for shoppers to find the right size when buying clothes online, according to Utkarsh Biradar, vice-president of product at the company. It's also applying these technologies to help advertisers expand their reach effectively, using machine learning to identify "lookalike" targets that are similar to existing users as well as figuring out what kinds of ads users don't want to see. Ola, one of India's leading ride-hailing apps, is using data science and machine learning to track traffic, improve customer experience, understand driver habits and extend the life of a vehicle. Machine learning models log each customer's gender, brand affinity, store affinity, price preference, frequency, volume of purchases, and more, which become more accurate as the company collects more data.
NGraph from Intel is also exploring optimizations that include an even more extensive optimizations: kernel fusion, buffer allocation, training optimizations, inference optimizations, data layout and distributed training. In addition to these approaches that originate from the DL community, other approaches to optimizing machine learning algorithms have been developed by other companies. These five open source projects (XLA, NNVM, NGraph, CCT and SystemML) all perform optimizations in a global manner across either the computational graph or an alternative declarative specification. A common standard deep learning virtual machine is a futuristic dream.
XGBoost is a well-loved library for a popular class of machine learning algorithms, gradient boosted trees. Given a Dask cluster of one central scheduler and several distributed workers it starts up an XGBoost scheduler in the same process running the Dask scheduler and starts up an XGBoost worker within each of the Dask workers. Dask.arrays use Numpy arrays, Dask.dataframes use Pandas, and now the answer to gradient boosted trees with Dask is just to make it really really easy to use distributed XGBoost. Bio: Matthew Rocklin is an open source software developer focusing on efficient computation and parallel computing, primarily within the Python ecosystem.
Your.MD, an AI-driven health information service delivered via a bot, has raised $10 million in new funding. It is part chatbot, helping users figure out what might be wrong with them via a conversational interface that drills down into your symptoms, and part next-generation search engine to surface detailed and verified information on various medical conditions. Alongside this, the London-headquartered startup has developed what it calls the "OneStop Health platform," a marketplace of trusted health service providers and products, some of which it has a commercial partnership. In a call with Your.MD founder and CEO Matteo Berlucchi, I likened the combination of Your.MD's next-generation search engine combined with the OneStop Health platform to the way Google's own search engine captures and then monetises intent.
I am thinking that sounds crazy--a response that might actually make Sam Altman happy. "I think we'll fund ten thousand companies next year," he says. More than 50 companies that went through the program are worth more than $100 million each and, of course, there are the multi-billion dollar valuations of YC's big three: Dropbox, Airbnb, and Stripe. Within the organization, there's a single talking point to describe YC's evolution: YC started as a family business but now it's more like a university.
"Artificial intelligence (AI) technology," according to SAP's senior director of advanced analytics, Chandran Saravana, on Coffee Break with Game Changers Radio, presented by SAP on May 3, 2017. He and SAP's senior director of Big Data initiatives, David Jonker, joined producer/moderator Bonnie D. Graham (follow on Twitter: @SAPRadio and #SAPRadio) for a lively discussion on "Machine Learning: Man vs. Machine Or Man Machine?" But when it comes to machine learning, you have to bridge a big gap to teach the machines how to learn – so machines can become smarter every day." Listen to Coffee Break with Game-Changers Radio: "Machine Learning Trends – Part 1: Enabling the Intelligent Enterprise" on demand.