Amazon Alexa developers are jumping for joy. Yesterday, Amazon announced that they would be providing AWS credit to developers who have published Alexa skills. This removes significant cost roadblocks which developers face when building new Alexa skills. With the recently announced Alexa Fund Fellowship, they are also trying to tackle another issue – the lack of proper training.
What do you think the most significant roadblocks and challenges are facing developers working in the home assistant space? Training? Cost? Competition? Customer Adoption? Something else? Drop us a comment please.
If you’ve followed parts 1, 2, 3, 4 and 5 of this series you know that you really don’t need a lot of math to get started with AI. You can dive right in with practical tutorials and books on the subject.
FinTech startups are increasingly focusing on building smarter, faster machines as they seek to gain a better understanding of artificial intelligence and its potential to solve customer problems, a new report by the Startupbootcamp FinTech London programme and PwC can reveal.
Working with hundreds of startups and financial services companies, the report’s authors have seen a significant change in culture over the past year. Almost 1 in 7 (16%) of applications to the incubator programme in 2016 looked to build new prototypes and many were focused on AI and machine learning.
One company that has been using deep learning to take on aging is InSilico Medicine. The company uses deep neural networks (DNNs) to sort through huge amounts of biological data. The DNNs look for biomarkers (measurable indicators of your biological state such as those included in blood tests) that correlate with aging. For humans, this would be an impossibly complicated and time-consuming task.
Last week, InSilico Medicine announced a product called Ageless Cell. The supplement contains four natural compounds that DNNs have shown can rejuvenate older cells. LEF has access to blood tests from its customers who take the product. That means data should be available in less than a year. If it works, we can expect
other DNN-developed geroprotectors.
When a car company issues a recall, it’s typically on dealerships to reach out to affected customers. But since vehicles can change hands, leaving records out of date, dealers aren’t always able to provide drivers with this at times vital information. One company that addresses this issue is Recall Masters, founded by programmer Chris Miller.
Recall Masters, which employs 20 people and even a lobbyist in Washington, D.C., collects data from more than 50 different sources, then utilizes machine learning to analyze it. The startup can then determine if a vehicle qualifies for a recall and who its current owner is — even if it has been resold multiple times — by poring over billions of transactions, according to Miller. He dubs the process “digital forensics.”
Alzbeta Dlha, a student at UCL, is currently writing a paper on bridging the gender gap in tech startups, aiming to help growing startups attract more talented female engineers and boost team diversity. It would be great if you could help her make the research more relevant by filling in this short survey about your pain points in HR management (it takes under 3 minutes).
In return, she will be happy to share the results of her work and best practices for diversity management from industry leaders (Google, Facebook, Amazon, McKinsey). Also it would be great if you could forward this to your friends who might be interested in this.
Tata Consultancy Services, a leading global IT services, consulting and business solutions organization, unveiled its Global Trend Study titled, “Getting Smarter by the Day: How AI is Elevating the Performance of Global Companies.” Focused on the current and future impact of AI, the study polled 835 executives across 13 global industry sectors in four regions of the world,
finding that 84% of companies see the use of AI as “essential” to competitiveness, with a further 50% seeing the technology as “transformative.”
Exploring the views and actions of decision makers from global companies with average revenues of $20 billion, the study revealed AI is spreading across almost all areas of a company. The biggest adopters of AI today are, not surprisingly, IT departments, with two-thirds (67%) of survey respondents using AI to detect security intrusions, user issues and deliver automation. However, by 2020, almost a third (32%) of companies believe AI’s greatest impact will be in sales, marketing or customer service, while one in five (20%) see AI’s impact being largest in non-customer facing corporate functions, including finance, strategic planning, corporate development,
After launching its first car last year, Alibaba is digging deeper into the automobile industry. The Chinese Internet and e-commerce behemoth is the lead investor in smart car tech developer WayRay’s $18 million Series B round, the startup announced.
Founded in 2012, WayRay makes holographic navigation systems. According to its funding announcement, WayRay has already spent $10 million of its own capital, as well as previous venture funding, on the technology that underpins Navion, an augmented-reality dashboard that overlays directions and other information onto a driver’s view of the road. The company plans to launch a consumer version of Navion in 2017.
In this special episode of the Data Show, O’Reilly’s Jenn Webb speaks with Maxwell Ogden, director of Code for Science and Society. Recently, Ogden and Code for Science have been working on the ongoing rescue of data.gov and assisting with other data rescue projects, such as Data Refuge; they’re also the nonprofit developers supporting Dat, a data versioning and distribution manager, which came out of Ogden’s work making government and scientific data
open and accessible.
He said, “I would say in general our three focus areas are access to research data for scientists, and then access to public data for journalists, and then access to government data for civic hackers or governments that want to publish data themselves. Those are government, science, and journalism, are the three most exciting public data areas that I think need a lot better tools for a lot of this stuff.”