AUSTIN (Austin Business Journal) — The size of what Elon Musk’s Space Exploration Technologies plans in Bastrop County, east of Austin, is coming into focus — and it appears to be massive.
To put the size into perspective, it would be larger than the footprint of half-a-dozen typical H-E-B grocery stores.
Artificial intelligence (AI) is not only affecting industries like business and healthcare. It is also playing an increasing role in the creative industries by ushering in a new era of AI-generated art. AI technologies and tools are often widely accessible to anyone, which is helping to create an entirely new generation of artists. We often […].
Facebook (now Meta) popularized the Silicon Valley ethos with the saying “Move fast and break things”. This approach might have worked when disrupting the social media business, but it’s causing all sorts of problems for them as well as other major AI players. Breaking things and moving fast might be the reason why so many AI projects are failing. According to an MIT study, over 85% of AI projects fail to deliver their stated objectives, and 70% of data science projects never make it to fruition. Clearly moving fast and breaking things doesn’t work if you’re not getting closer to success.
There’s a difference between Iterating to Success and Breaking Things.
The oft-cited Silicon Valley ethos of “Move fast and break things” isn’t working that well for AI.
Researchers have developed a machine learning algorithm that could help reduce charging times and prolong battery life in electric vehicles by predicting how different driving patterns affect battery performance, improving safety and reliability.
The researchers, from the University of Cambridge, say their algorithm could help drivers, manufacturers and businesses get the most out of the batteries that power electric vehicles by suggesting routes and driving patterns that minimize battery degradation and charging times.
The team developed a non-invasive way to probe batteries and get a holistic view of battery health. These results were then fed into a machine learning algorithm that can predict how different driving patterns will affect the future health of the battery.
California-based StepFunction, which helps enterprises improve customer retention rate, has raised $5 million.
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Modern-day software-as-a-service (SaaS) enterprises are racing to acquire new users and convert them into paying customers. The rush is great for the industry, but it has also left many organizations looking at only half of the picture.
Aug 31 (Reuters) — Chip designer Nvidia Corp (NVDA.O) said on Wednesday that U.S. officials told it to stop exporting two top computing chips for artificial intelligence work to China, a move that could cripple Chinese firms’ ability to carry out advanced work like image recognition and hamper Nvidia’s business in China.
Nvidia shares fell 6.6% after hours. The company said the ban, which affects its A100 and H100 chips designed to speed up machine learning tasks, could interfere with completion of developing the H100, the flagship chip Nvidia announced this year.
Shares of Nvidia rival Advanced Micro Devices Inc (AMD.O) fell 3.7% after hours. An AMD spokesman told Reuters the company had received new license requirements that will stop its MI250 artificial intelligence chips from being exported to China but it believes its MI100 chips will not be affected. AMD said it does not believe the new rules will have a material impact on its business.
Millions of faces and car license plates were stored in a sizable Chinese database that was publicly accessible for months before it was silently removed in August.
A tech business called Xinai Electronics with headquarters in Hangzhou on China’s east coast is the owner of the disclosed data. In China, the firm creates systems for regulating entry for people and cars to workplaces, schools, construction sites, and parking lots. Its website boasts the use of facial recognition for a variety of uses beyond building access, including personnel management, such as payroll, monitoring employee attendance and performance, while its cloud-based vehicle license plate recognition system enables drivers to pay for parking in unattended garages that are managed by staff remotely.
Facebook (now Meta) popularized the Silicon Valley ethos with the saying “Move fast and break things”. This approach might have worked when disrupting the social media business, but it’s causing all sorts of problems for them as well as other major AI players. Breaking things and moving fast might be the reason why so many AI projects are failing. According to an MIT study, over 85% of AI projects fail to deliver their stated objectives, and 70% of data science projects never make it to fruition. Clearly moving fast and breaking things doesn’t work if you’re not getting closer to success.
There’s a difference between Iterating to Success and Breaking Things.
Early AI winners align organizational and business strategies to build value and manage risk.
As the world increasingly embraces Web3, corporations are turning to metaverse applications to stay ahead of the curve. Based on Verified Market Research, the Metaverse market is anticipated to expand at a CAGR of 39.1 percent from 2022 to 2030, reaching USD 824.53 Billion in 2020 and USD 27.21 Billion in 2020. This is due to the increasing demand for AR/VR content and gaming and the need for more realistic and interactive training simulations.
These startups show Proof of Concept with a working product and clear value proposition for businesses and consumers.
● Launch a corporate accelerator: Another way to increase your exposure to the Metaverse is to launch a corporate accelerator. This will give you access to a broader range of startups and help you build a more diverse portfolio. In addition, it will allow you to offer mentorship and resources to the startups you invest in.
Continue reading “A Case Study For The Industry: LG Investing In Metaverse” »
The CHIPS Act of 2022 was signed into law on Aug. 9. It provides tens of billions of dollars in public support for revitalization of domestic semiconductor manufacturing, workforce training, and “leap ahead” wireless technology. Because we outsource most of our device fabrication — including the chips that go into the Navy’s submarines and ships, the Army’s jeeps and tanks, military drones and satellites — our industrial base has become weak and shallow. The first order of business for the CHIPS Act is to address a serious deficit in our domestic production capacity.
Notoriously absent from the language of the bill is any mention of chip security. Consequently, the U.S. is about to make the same mistake with microelectronics that we made with digital networks and software applications: Unless and until the government demands in-device security, our competitors will have an easy time of manipulating how chips function and behave. Nowhere is this more dangerous than our national security infrastructure.
