Algorithms + ethics. Apple discusses their facial recognition tech. Amazon Go might just be a Go. http://cognitionx.com/news-briefing/.
Look out for the research following our roundtable on the Impact of AI on HR & Recruitment happening this Monday (20th November). The last event was a great success with senior HR professionals discussing the future of the industry.
We’ll continue our discussion on Monday evening to hear their experiences of implementing AI, the projects which have and haven’t worked, the tools and vendors they would recommend and the breadth of use cases for AI in HR.
For a full debrief of that conversation, as well as more information on the topic in general, head to our Research Hub or get in touch with us directly.
Amazon Go represents Amazon’s most ambitious effort yet to transform the brick-and-mortar shopping experience by eliminating the checkout line, saving customers time and furthering the company’s reputation for convenience. The idea is to let consumers walk in, pick up items and then pay for them without ever standing in line at a cashier.
Seven months after launching, challenges remain, but the “just walk out” technology has improved markedly, says the person familiar with the situation, who requested anonymity to speak freely about the project. And in a sign that the concept is almost ready for prime time, hiring for the Amazon Go team has shifted from the engineers and research scientists needed to perfect the platform to the construction managers and marketers who would build and promote the stores to consumers.
At Google I/O this year, Google quietly introduced a new chatbot analytics platform called Chatbase, a project developed within the company’s internal R&D incubator, Area 120. Yesterday, that platform is being publicly launched to all, after testing with hundreds of early adopters including Ticketmaster, HBO, Keller Williams, Viber, and others.
The idea behind Chatbase’s cloud service is to offer tools to more easily analyse and optimise chatbots. This includes giving bot builders the ability to understand what works to increase customer conversions, improve the bot’s accuracy, and create a better user experience. This data is available through an analytics dashboard, where developers can track specific metrics like active users, sessions, and user retention. These insights give an overall picture of the bot’s health and see general trends.
Algorithms that predict whether someone is a criminal based on past behaviour, gender and where they live could be “discriminatory”, Liberty said. The human rights group was giving evidence to the Commons science and technology committee.
The MPs are investigating the growing use of algorithms in decision making. They are concerned businesses and public bodies are relying on computer programs to make life-changing decisions – despite the potential scope for errors and misunderstandings. Durham Police have already launched a system which uses algorithms to help decide whether to keep a suspect in custody.
Apple started using deep learning for face detection in iOS 10. With the release of the Vision framework, developers can now use this technology and many other computer vision algorithms in their apps. They faced significant challenges in developing the framework so that they could preserve user privacy and run efficiently on-device. This article discusses these challenges and describes the face detection algorithm.
They discuss their algorithmic approach to deep-learning-based face detection, including:
how they fully leverage their GPU and CPU (using BNNS and Metal)
memory optimisations for network inference, and image loading and caching
how they implemented the network in a way that did not interfere with the multitude of other simultaneous tasks expected of iPhone
Japanese telecommunication and automotive giants SoftBank and Honda are teaming up for a joint research program that will look at how fifth generation mobile networks, or 5G, can be used to improve connected car technology.
Covering vehicle-to-vehicle (V2V) and internet-connectivity technologies, SoftBank and Honda
will work toward creating new on-board antennas to facilitate communications between high-speed vehicles while also looking at ways to support “recovery” technologies for areas with weak signals.
G Wu and Jing He started Adeptmind, a tool that gives retailers a way to implement a smarter search engine on their sites by collecting related data to all of their products and zero in on what customers are actually looking for. To do that, Adeptmind said that it has raised $4.5M in a financing round from Fidelity Canada.
“A lot of times NLP companies will have fairly ‘comprehensive’ knowledge graphs where you do internal labeling, but most of the data comes from the product catalog,” Wu, the CEO, said. “As such anything not in the product catalog will not be understood. There’s no free lunch when it comes to machine learning. We target crawl a large portion of the web. Based on the web we do targeted crawling so any relevant information we ingest.
Baidu announced a smart speaker and a cute-looking interactive home robot at an event in Beijing today. Jesse Lyu, general manager of Baidu’s Intelligent Hardware Unit, preceded his product launch presentation at the Baidu World conference with a tribute to the iPhone. He noted that every smartphone that went on the market after it looked pretty much the same, and that’s because the iPhone was the “definitive product.”
Minutes later, Lyu, who is also the founder
of Raven Tech, a startup acquired by Baidu in February, revealed his vision for a definitive product in the age of AI: Raven H, a smart speaker that looks like a neat stack of square orange, red, blue, and green panels. Lyu also revealed the Raven R, a similar device that can also move around on six axes.
Noodle.ai is firmly focused on the enterprise, and its mission is to bring business executives, process experts and AI technologies together to address complex business challenges and deliver enhanced outcomes. ZDNet talked to Stephen Pratt to learn more.
The basic premise is ‘AI as a service’ — allowing customers to ‘pay by the drink’, as it were, to deploy AI technologies in their companies. We started out by looking at the landscape of where AI can apply within enterprises, and we’re absolutely convinced that, over time, learning algorithms are going to replace rules-based software to help companies make complex business decisions. Looking a decade into the future, I think it’ll be very hard to find rules-based software that’s driving business decision-making.