From virtual assistants to driverless cars, technology imitating human intelligence is on the rise. But at what ethical cost and how do boards future-proof their organisations in the face of rapid change? Earlier this year, a Japanese insurance company made headlines for doing something that company executives and directors around the world have been anticipating - and fearing - for years. Fukoku Mutual Life Insurance made 34 of its staff redundant and replaced them with artificial intelligence (AI) system IBM Watson. Japanese newspaper The Mainichi reported the company will be using Watson to determine payout amounts and check customer cases against their insurance contracts. Evidently, the future of AI is already here and technology has been changing the world at a dramatic pace.
Deep learning computer vision startup allegro.ai is set to showcase its latest product offering, hosted at the Intel partner booth (booth #307), during the Embedded Vision Summit which will take place in Santa Clara, California on May 20-May 23, 2019. The company's platform and product suite simplify the process of developing and managing deep learning-powered perception solutions - such as for autonomous vehicles, medical imaging, drones, security, logistics and other use cases. The platform enables engineering and product managers to get the visibility and control they need, while research scientists focus their time on research and creative output. The result is meaningfully higher quality products, faster time-to-market, increased returns to scale, and materially lower costs. The company's investors include Robert Bosch Venture Capital GmbH, Samsung Catalyst Fund, Hyundai Motor Company, and other venture funds.
Ng announced Tuesday that he raised money from venture capital firms New Enterprise Associates, Sequoia Capital and Greylock Partners as well as SoftBank Group Corp. Under Ng, Baidu released a voice-based operating system that users can talk to - much like Amazon's Alexa voice assistant or Apple's Siri - and also started working on self-driving cars and face recognition technology to open things like transit turnstiles when users approach. I think it's a more systematic, repeatable process than most people think," said Ng, who also taught artificial intelligence courses at Stanford University. The first company to receive money from the fund will be Landing.ai,
Unlike the holiday fruitcake we've all been avoiding the past couple of weeks, 2018 was far from stale. The year was riddled with controversies, excitement, and disappointments -- from Facebook selling it's soul … err, our souls, to the battle over net neutrality to Apple hitting over a trillion dollars to cryptocurrencies' nosedive. While already used on a wider basis in China, we will see facial recognition systems launched in the U.S. on a test-basis in large public venues in 2019. There are various technical and public policy issues to overcome, but this could be the start of a Minority Report-like world with advertising thrown into our view based on this technology. While listening to a talk by Timothy Chou, cloud computing pioneer and former President of Oracle On Demand, a few months ago combined with my SparkLabs Group co-founder's, HanJoo Lee, constant yapping about the future of the cloud, I have to agree that future of enterprise analytics can be as big as the trillion dollar workflow platform space (Oracle, Salesforce, SAP, and others).
University of Toronto graduate student Avishek "Joey" Bose, under the supervision of associate professor Parham Aarabi in the school's department of electrical and computer engineering, has created an algorithm that dynamically disrupts facial recognition systems. The project has privacy-related and even safety-related implications for systems that use so-called machine learning -- and for all of us whose data may be used in ways we don't realize. Major companies such as Amazon, Google, Facebook and Netflix are today leveraging machine learning. Financial trading firms and health care companies are using it, too -- as are smart car manufacturers. What is machine learning, anyway?