1 – Pepper, the 4-foot-tall Robot that “Reads Emotions,” Makes her US debut
Japan’s famous robot Pepper made its North America debut this week at the b8ta showroom floor in Palo Alto. The robot was first launched into Japan’s public sphere two years ago in SoftBank stores, and there are currently about 10,000 Pepper robots across Japan and Europe. Executives from SoftBank Robotics America are now looking to introduce Pepper to North America, likely beginning in a B2B role and gradually evolving to fulfill both business and consumer roles (such as a household assistant). Pepper is primarily designed to replace human tasks that are “dull” i.e. answer common customer service questions, and do so in a friendly manner and in over a dozen languages and growing. Price points for North American customers have not yet been specified, though Steve Carlin, SoftBank’s vice president for marketing, said prices will likely start in the “hundreds of dollars” per month.
(Read the full article on Ars Technica)
2 – Kx Financial Analytics Technology Tackles ‘Big Data’ Crop Research at Biotech Leader Earlham Institute
Palo Alto-based Kx Systems is partnering with UK-based Earlham Institute (EI) to tackle big data analysis in bioinformatics. Kx, which has already established itself as a leader is financial analytics technology, will collaborate with EI, a global leader in research on genomics and computational biology, to study crop growth patterns and modern agricultural methods. Kx-created predictive models for crop development and adaptation aligns with EI’s long-term vision of focusing on the development of agri-tech and high performance computing (HPC) and also follows the UK government’s recently announced strategy of building a sustainable bio-economy.
(Read the full article on Kx News)
3 – Ford Plans to Sell Cars without Steering Wheels by 2021, Doubles Down on Silicon Valley R&D
Ford announced on Tuesday that it plans to deliver fully autonomous on-demand ride services by 2021. Ford CEO Mark Fields and CTO Raj Nair elaborated to the media its plans to expand its Palo Alto-based research and development (R&D) lab six times over to more than 180,000 square feet and also grow its workforce to 260 by the end of 2017. Tuesday’s announcement coincided with another press release that revealed Ford’s co-leading a $150-million investment in Velodyne LiDAR, a startup working on lasar radar for self-driving vehicles. Ford also announced on Tuesday that it had acquired Israeli company SAIPS, a computer vision and machine learning company focused on autonomous vehicle technology. Over the past few years, other major automakers have also set up Silicon-Valley based R&D labs, including Volkswagen, Hyundai, Tesla & GM, BMW, Toyota, Mercedes Benz, and Nissan. Auto companies’ abilities to set up and maintain partnerships with key technology players appears crucial to ongoing competition.
(Read the full article on Silicon Valley Business Journal)
4 – Calico Appoints Daphne Koller as Chief Computing Officer
Calico, a Google-funded research and development company focused on aging research and therapeutics, has brought on Daphne Koller, Ph.D, as its new chief computing officer. Koller, formerly president and co-founder of Coursera, will lead a team focused on the creation of computational and machine learning tools for analyzing biological and medical data sets.
“High-throughput experimental protocols are transforming biology into a data science. But the potential of the massive data sets that are and that could be produced will only be fully tapped via the development of powerful computational tools,” said Dr. Koller.
Koller has received much recognition for her work as an academic and professional in the field of computer science, as well as as an entrepreneur, including being named by President Obama as a Presidential Ambassador for Global Entrepreneurship.
(Read the full article on Calico News)
5 – Precision Medicine Study Highlights Role of Machine Learning
A newly published study in Nature by the Stanford University School of Medicine shows evidence that computers can now be trained to more accurately analyze slides of lung tissue cancer than human pathologists. The researchers used a machine learning approach to pinpoint features of lung cancer and discern between two different types – adenocarcinoma and squamous cell carcinoma; it’s a technology that also able to more accurately predict patient survival spans than pathologists. Michael Snyder, professor and chair of genetics said,
“We launched this study because we wanted to begin marrying imaging to our ‘omics’ studies to better understand cancer processes at a molecular level. This brings cancer pathology into the 21st century and has the potential to be an awesome thing for patients and their clinicians.”
While the study focused on lung cancer, the research team expressed its opinion that similar machine learning techniques could be used to help diagnose other types of cancer.
Image credit: FarmingUK