Parts Supplier


Toyota allies with Intel to develop self-driving car ecosystem

Engadget

Toyota is teaming up with Intel, and an assortment of tech and automotive firms, to develop an ecosystem for connected cars. By sharing self-driving vehicle data, the companies aim to develop maps and improved driver assistance systems based on cloud computing. Rounding out the alliance (dubbed the "Automotive Edge Computing Consortium") will be Ericsson, Japanese auto parts-maker Denso Corp, and telecoms firm NTT DoCoMo. All those connected car tests are already racking up big data, which will ramp up exponentially over time.


Honeywell's connected thermostats now work with Google Home

Engadget

Google welcomed Honeywell into the Home family as a partner back in January, but now it's finally including both of the company's connected thermostat families, the Lyric and Total Connected Comfort. Users can control them using either Home's voice controls or through Google Assistant on an Android device. That adds to the thermostats' existing integrations with connected platforms, including Amazon Alexa, Apple HomeKit, Samsung SmartThings and user-customized IFTTT functionality.


Microsoft is putting Cortana in charge of your thermostat

Mashable

The company teamed up with popular thermostat maker Johnson Controls to create GLAS, a sleek new touchscreen wall thermostat that promises to do much more than just turn up the heat. GLAS will run on the Windows 10 IoT Core OS, which is made specifically for smart devices. The thermostat offers Cortana voice services, so you'll be able to interact with the AI directly on the wall. GLAS will likely look to stake its place as a high-end competitor to other smart thermostats like Google's Nest and Ecobee, that latter of which also offers voice control through Amazon's Alexa.


3 Steps To Jumpstart A Machine Learning Strategy

#artificialintelligence

Similar to any IT or business project, IT and OT should start with defining a significant business problem that analyzing large data sets can answer. Machine learning requires learning from experience, and in the case of certain equipment, you may have limited failure data to analyze. For example, changes in tire pressure would be a key indicator in the mining use case – Machine Learning techniques help here too. This type of analytics differs wildly from the batch data processing and weekly business intelligence dashboards that many companies use today.


DuPont Pioneer: Data Engineer

@machinelearnbot

DuPont has a rich history of scientific discovery that has enabled countless innovations and today, we're looking for more people, in more places, to collaborate with us to make life the best that it can be. Seeking a Data Engineer/Software Developer to design, develop, and implement high quality data solutions and applications for our data science and analytics platform in AWS. Education & Experience: BS degree in Computer Science, Physics, Electrical Engineering, or a related field.


Kit Cummins awarded the American Chemical Society Pauling Medal

MIT News

Department of Chemistry Professor Christopher (Kit) Cummins has been honored with the 2017 Linus Pauling Medal, in recognition of his unparalleled synthetic and mechanistic studies of early-transition metal complexes, including reaction discovery and exploratory methods of development to improve nitrogen and phosphorous utilization. It is presented annually in recognition of outstanding achievement in chemistry in the spirit of, and in honor of, Linus Pauling, who was awarded the Nobel Prize in chemistry in 1954 and the Nobel Prize for peace in 1962. Cummins joins several current members of the Department of Chemistry in being named a Linus Pauling Medal awardee, including Tim Swager (2016), Stephen Buchwald (2014), and Stephen Lippard (2009), as well as former department members Alexander Rich (1995) and John Waugh (1984). In addition, Cummins Group researchers work to develop new starting materials in phosphate chemistry, including acid forms that provide a starting point for synthesizing new phosphate-based materials with applications in next-generation battery technologies and catalysis.


This Week in Hadoop and More: Deep and Machine Learning Tools, Tips, and Projects - DZone Big Data

#artificialintelligence

There's a lot going on in the world of Big Data, Deep Learning, and Machine Learning this week. I'm also preparing for Oracle Code New York, where I will be doing a talk on NiFi, Deep Learning, Machine Learning, NLTK, streaming, IoT, and Java microservices. Deep Learning 4J Model Zoo: They save lots of time -- grab all the zoos you can. I am hoping by the end of the month to detail my progress on my self-driving Raspberry Pi car, Raspberry Pi Deep Learning, Spark, Hadoop Cluster, and wrap-up from the Code NYC talk.


The spectacular growth of Data Science, Machine Learning, Deep Learning, IoT, and AI

@machinelearnbot

If you want a thorough, everyday clean, maybe you should consider using robots. The Roomba 650 Vacuuming Robot provides a thorough clean at the push of a button.


4 Tech Stocks to Buy Before They Ride AI to the Sky

#artificialintelligence

With this in mind, which tech stocks should investors buy to cash in on this trend? Although Nvidia Corporation (NASDAQ:NVDA) now appears to be the prime beneficiary of the machine learning aspect of artificial intelligence, Nvidia stock has soared over 200% in the last year and likely already reflects a great deal of revenue from AI. Consequently, investors should wait for a better entry point before buying Nvidia stock. But four other tech stocks Intel Corporation (NASDAQ:INTC), Delphi Automotive PLC (NYSE:DLPH), Visteon Corp (NYSE:VC), and Baidu Inc (NASDAQ:BIDU) -- are definitely poised to get a meaningful boost from AI and should be bought at current levels.


Denso, Toyota collaborate in AI-based image recognition

#artificialintelligence

DNN, an algorithm modeled after the neural networks of the human brain, is expected to perform recognition processing as accurately as, or even better than the human brain. To achieve automated driving, automotive computers need to be able to identify different road traffic situations including a variety of obstacles and road markings, availability of road space for driving, and potentially dangerous situations. In DNN-based image recognition, computers can extract and learn the characteristics of objects on their own, thus significantly improving the accuracy of detection and identification of a wide range of objects. By accelerating the process to commercialize DNN-IP through the joint development and incorporating DNN-IP in in-vehicle cameras, Denso will develop high-performance, advanced driver assistance and automated driving systems, and continue to contribute to building a safe and secure automotive society for people around the world, not just for drivers and pedestrians.