"Companies want to build great teams by hiring the best talent. But the best people don't apply on job sites typically. So then how do you hire those people?" Founded in 2014 by Exotel co-founder Vijay Sharma, Sudheendra Chilappagari, Saiteja Veera, and Rishabh Kaul, the idea was simple- instead of the inbound approach to hiring which companies follow, Belong bet on outbound hiring to disrupt the hiring space. The predictive hiring startup aims to give companies the tools to help them reach out to the best people, making it a very personalized engagement.
The age of Artificial Intelligence(AI) has already arrived and the discussion is now shifted towards the expectations and implementation now. AI solutions are coming at a breakneck speed and companies are finding it tough to design a right AI strategy. Businesses that have started or looking to adopt AI technologies to create intelligent systems that can learn and adapt and later execute the predefined instructions. Different businesses are looking to adapt AI technologies in different processes. Healthcare, energy and utilities and data infrastructure are aggressively looking to develop AI solutions to remove manual processes of data cleansing, preparation and predicting results on case to case basis.
Digital Surgery, a health tech startup based in London, today launched what it's calling the world's first dynamic artificial intelligence (AI) system designed for the operating room. The reference tool helps support surgical teams through complex medical procedures -- cofounder and former plastic surgeon Jean Nehme described it as a "Google Maps" for surgery. "What we've done is applied artificial intelligence … to procedures … created with surgeons globally," he told VentureBeat in a phone interview. "We're leveraging data with machine learning to build a [predictive] system." Well-funded hospital systems have shown an interest in automation.
An artificial intelligence revolution has been eagerly awaited since the late 1950s, when pioneering IBM researcher Arthur Samuel trained the world's first self-learning computer to play a mean game of checkers. But only in the past few years has the long-promised technology become mature, effective and -- thanks to a variety of new offerings -- readily accessible to the channel. AI and machine learning are now taking the industry by storm, with the cloud fast-tracking adoption of solutions that make decisions, automate business processes, deliver predictions and insights and learn from their own experience. Next-gen startups are at the forefront of the revolution, delivering infrastructure, development frameworks, and intelligent applications that allow enterprises to take advantage of their data in ways never before possible.
There is nothing unusual about how little he knows about his own history. Almost everyone in the NBA today came of age in the final years that sports were more art than science. But the game has been transformed since then. A technological revolution has swept through basketball and made it possible for high-schoolers to have more data about themselves than even the most progressive NBA teams had until recently. Lin is now an investor in the latest product that's spreading through the sport and getting attention from the league's brightest minds, a new app called HomeCourt, which comes from a tech company focused on mobile artificial intelligence that was founded not long ago by former Apple engineers who were obsessed with basketball and have spent the last year developing the sort of weapon that Jeremy Lin never had.
Check out the "Decentralized data markets for training AI models" session at the Artificial Intelligence Conference in San Francisco, September 4-7, 2018. Hurry--early price ends July 20. In this post I share slides and notes from a keynote I gave at the Strata Data Conference in London at the end of May. My goal was to remind the data community about the many interesting opportunities and challenges in data itself. Much of the focus of recent press coverage has been on algorithms and models, specifically the expanding utility of deep learning.
The companies declined to disclose the size of the investment, but Volley said it will use the funding to double its team of less than 20 over the next nine months. JPMorgan's investment comes as banks increasingly look to use artificial intelligence to make better use of the growing amount of data that they hold across a variety of business lines, ranging from trading to compliance. The startup is developing software that can process data from disparate sources to create quizzes and other corporate training material such as cyber security or compliance courses. Volley had initially focused on developing an app for students that used its machine learning engine, but later pivoted to developing technology for enterprises, Kahn added. New York-based JPMorgan takes equity stakes in young technology companies that can help the bank enhance customer experience, become more efficient and protect its assets.
In a nondescript industrial park in Merrimack, New Hampshire, Jason Walker is putting the final touches on what he believes will become the iPod of working robots. Walker, 47, is co-founder of Waypoint Robotics, and the former lead quality and testing manager for the Roomba vacuuming robot. Waypoint, a tech startup about a year old, is housed in one cavernous room with high ceilings and a barebones office upfront filled with large, flat-screen monitors. Co-Founder and CEO Jason Walker is third from the left.Waypoints Robotics "We don't splurge on anything except screens and chairs," Walker says in the easy drawl of his native Kansas. Although he makes no claims to being another Steve Jobs, Walker does fervently believe that he and his small team are going to revolutionize robots in the same way Jobs revolutionized MP3 players.
The good news: Declining cloud compute and hosting costs and open-sourced machine learning algorithms like TensorFlow mean it has never been cheaper and easier to build intelligent software. The bad news: It has never been cheaper and easier to build intelligent software, so this is no longer a competitive differentiator; it's table stakes. So, as we enter the "Great Commoditization" era of software, how can a CEO de-commoditize and build a long-term competitive moat around the business? I believe data will be the gold that separates the winners from the also-rans in this next generation of machine-learning-driven software. But all data sets are not created equal.
Tech is a concentrated industry, and a recent study by CB Insights illustrates the extent to which this is the case. According to the New York City-based machine intelligence-supported research firm's latest research into artificial intelligence (AI) startup acquisitions, Google and Apple have been the top buyers of AI startups, with 14 and 13 acquisitions respectively between 2012 and 2017. They're followed by six other well-known tech giants, which have each acquired between four and six startups during the same period: Facebook, Amazon, Intel, Microsoft, Twitter, and Salesforce, with media monitoring and business intelligence software-as-a-service (SaaS) firm Meltwater rounding out the list of top nine. In its analysis of the data, published on Feb. 27, CB Insights noted that Meltwater was not only the smallest company on the list, but the only private one as well. "Meltwater is strategically using AI to monitor digital media and analyze brand sentiment and competitor activity," the organization said.