Dyson to build electric vehicle plant in Singapore


Dyson has announced plans to build a manufacturing plant in Singapore dedicated to producing electric vehicles, marking the first of such facilities for the UK company. Slated to complete in 2020, the two-storey advanced automotive manufacturing plant was part of Dyson's £2.5 billion (US$3.25 billion) global investment in new technology. Construction work on the Singapore site would begin in December, the company said in a statement Tuesday. Country's government has introduced initiatives to train 12,000 people in artificial intelligence skillsets, including industry professionals and secondary school students. Dyson in September 2017 said it had been working on an electric vehicle and planned to launch its first such offering in 2021.

Ericsson: 5G a 'commercial reality' as networks sales rise


Ericsson is also looking towards artificial intelligence (AI) and automation to push its business, as the company reported 54 billion Swedish kronor (SEK) (almost $6 billion) in net sales for the third quarter, up from 49 billion SEK in the same quarter last year. Cost of sales for the quarter was 34 billion SEK, down from 36 billion SEK this time last year, while research and development (R&D) expenses were down from 10.5 billion SEK to 9.4 billion SEK. Opex was 16 billion SEK, down from 17 billion SEK. Total net income for the quarter was 2.6 billion SEK, an improvement on the 3.5 billion SEK loss this time last year, with net sales in its Networks division up by 13 percent year on year to 35.9 billion SEK. Across Networks, products were up by 17 percent to 25 billion SEK and services rose by 5 percent to 10.6 billion SEK.

Samsung Buys Network Analytics Startup


Looking to build up its AI portfolio, Samsung Electronics said it is acquiring Spain's Zhilabs as it charts its transition to 5G wireless networks supporting Internet of Things devices with the Spanish company's AI-based network and service analytics. Terms of the acquisition were not disclosed. The South Korean electronics giant did say it would retain Zhilabs' executive team and that the wholly-owned unit would operate independently. The deal announced on Wednesday (Oct. The deployment of "5G will enable unprecedented services attributed to the generation of exponential data traffic, for which automated and intelligent network analytics tools are vital," said Youngky Kim, president of Samsung Electronics' networking business.

Algorithmia Survey: Large Enterprises Have Taken the Lead in Machine Learning


Companies of all sizes are not satisfied with their machine learning process and various challenges to widespread adoption remain. SEATTLE, Oct. 16, 2018 (GLOBE NEWSWIRE) -- Algorithmia announces the results of a survey on enterprise machine learning. The comprehensive survey, titled "State of Enterprise Machine Learning," is a first for Algorithmia and was designed to explore the ways in which companies of all sizes are utilizing machine learning. The survey was completed by over 500 data science and machine learning professionals, the majority of whom were based in North America. A report detailing the survey's findings can be foundhere.

GoodTime raises $5 million to bring artificial intelligence into the interview process


GoodTime, the algorithmically enhanced interview process management platform, has raised $5 million in a new round of funding led by Bullpen Capital. The company uses natural language processing to link interview candidates with the right interviewers inside an organization. The idea is to make the hiring process run more smoothly for large organizations and give overworked human resources staffers a new organizational tool in their toolbox to build better staffing processes. To do this Ahryun Moon, Jasper Sone and Peter Lee, the co-founders of GoodTime, have built a tool that uses the calendar as its organizing principle. The idea is that the sooner interviews can be booked with the right people, the better it is for an organization.

Build, buy, or both? The AI implementation conundrum


AI has the thrilling ability to transform a range of businesses. But let's be frank: it's also a beautiful, massive disappointment for many companies. Here's a common trajectory for many AI and data science projects in an enterprise: A company decides to incorporate AI into their business. They spend one to two years searching for AI experts to build a team of solid data scientists, but not necessarily industry experts. The team works for a year or so on a project, only for the company to discover that the project is irrelevant and they need very different people.

Base10 Partners launches $137 million early-stage AI startup fund


Base10 Partners today announced the launch of a $137 million fund to invest in early-stage startups that will use AI to change industries by empowering workers instead of automating them out of jobs. The prime directive of the debut fund will be to back companies in industries like real estate, construction, waste management, and logistics -- what managing partner Adeyemi Ajao calls "automation for the real economy" and "solving problems for 99 percent of people." Base10 Partners was created in 2017 by Ajao and cofounder TJ Nahigian, a company spokesperson told VentureBeat in an email. With Ajao as managing director, Base10 is one of the first venture-backed funds with a black managing director to raise more than $100 million in its first fund. Prominent investment funds recently created by people of African descent include the $100 million New Voices Fund from Richelieu Dennis and Arlan Hamilton's Backstage Capital, which raised $36 million earlier this year to back underrepresented founders, particularly black women.

Why Google needs to make machine learning its growth fuel


These and many other fascinating insights are from CB Insight's report, Google Strategy Teardown (PDF, 49 pp., opt-in). The report explores how Alphabet, Google's parent company is relying on Artificial Intelligence (AI) and machine learning to capture new streams of revenue in enterprise cloud computing and services. Also, the report looks at how Alphabet can combine search, AI, and machine learning to revolutionise logistics, healthcare, and transportation. It's a thorough teardown of Google's potential acquisitions, strategic investments, and partnerships needed to maintain search dominance while driving revenue from new markets. CB Insights found Google is experiencing rising TAC in their core ad and search businesses.

Neo4j 3.5 Poised to Power the Next Generation of AI & Machine Learning Systems


Neo4j, the market leader in connected data, announced today the upcoming release of Neo4j 3.5, the native graph platform designed to drive the success and adoption of real-time business applications, including artificial intelligence (AI) and machine learning (ML) systems. Neo4j customers – including eBay and Caterpillar – have demonstrated that connected graph datasets are a foundational element of enterprise AI applications. Graph-based data models provide the necessary context for AI applications by capturing facts related to and relationships among people, processes, applications, data and machines. Informed by successful AI customer deployments – including knowledge graphs, fraud detection, recommendation systems and conversation engines – Neo4j 3.5 delivers foundational features for AI-powered systems of connection to generate bottom-line business value. "The way we organize and represent knowledge in AI-powered systems has a profound effect on what and how they can learn," said Bowles.

Identifying Real Estate Opportunities using Machine Learning Machine Learning

The real estate market is exposed to many fluctuations in prices, because of existing correlations with many variables, some of which cannot be controlled or might even be unknown. Housing prices can increase rapidly (or in some cases, also drop very fast), yet the numerous listings available online where houses are sold or rented are not likely to be updated that often. In some cases, individuals interested in selling a house (or apartment) might include it in some online listing, and forget about updating the price. In other cases, some individuals might be interested in deliberately setting a price below the market price in order to sell the home faster, for various reasons. In this paper we aim at developing a machine learning application that identifies opportunities in the real estate market in real time, i.e., houses that are listed with a price substantially below the market price. This program can be useful for investors interested in the housing market. The application is formally implemented as a regression problem, that tries to estimate the market price of a house given features retrieved from public online listings. For building this application, we have performed a feature engineering stage in order to discover relevant features that allows attaining a high predictive performance. Several machine learning algorithms have been tested, including regression trees, k-NN and neural networks, identifying advantages and handicaps of each of them.