Dubbed the “unicorn,” the odd object is also among the smallest black holes ever found, and it may help solve an enduring mystery in astrophysics.
It’s hard to overemphasize how far computers have come and how they have transformed just about every aspect of our lives. From rudimentary devices like toasters to cutting-edge devices like spacecrafts, you’ll be hard pressed not to find these devices making use of some form of computing capability.
The future of lidar is uncertain unless, as Voyant hopes to do, its price and size are reduced to fractions of their current values. As long as lidars are sandwich-sized devices that cost thousands, they won’t be ubiquitous — so Voyant has raised some cash to bring its smaller, cheaper, more easily manufactured, yet still highly capable lidar to production.
When I wrote up the company’s seed round back in 2019, the goal was more or less to shrink lidar down from sandwich to fingernail size using silicon photonics. But the real challenge faced by nearly every lidar company is getting the price down. Between a strong laser, capable receptor and a mechanical or optical means of directing the beam, it just isn’t easy making something cheap enough that, like an LED or touchscreen, you can easily put several of them in a vehicle that costs less than $30,000.
CEO Peter Stern joined the company just as COVID was getting started, and they were looking for a way to turn a promising prototype developed by co-founders Chris Phare and Steven Miller into a working and marketable product. After going back to basics they ended up with a photonics-based frequency-modulated continuous wave (FMCW) system (just go with it for now) that could be manufactured at existing commercial fabs.
Our bodies have some healing and regenerative capabilities. For most of us, cuts will mend, we’ll recover from mild infections, and at the cellular level, | Genetics And Genomics.
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The transit authority (RIPTA) was hacked. Now the ACLU is questioning why thousands of people with no relationship to RIPTA had their personal information leaked.
A team of engineers from the University of California San Diego has unveiled a prototype four-legged soft robot that doesn’t need any electronics to work. The robot only needs a constant source of pressurized air for all its functions, including its controls and locomotion systems.
Most soft robots are powered by pressurized air and are controlled by electronic circuits. This approach works, but it requires complex components, like valves and pumps driven by actuators, which do not always fit inside the robot’s body.
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Having launched 31 orbital Falcon 9 missions and four suborbital Starship tests, 2021 was the most active year for SpaceX to date. These launches included a number of new reuse records, including flying a booster for the eleventh time, flying the same booster twice in under a month, flying a fairing half for the fifth time, and setting a turnaround record for Dragon.
Falcon 9 Boosters
2021 brought only two new Falcon 9s into the fleet: B1067 and B1069, which first flew on the CRS-22 and CRS-24 missions, respectively. All of the other 29 Falcon 9 missions were flown on flight-proven boosters. These flights included the first eighth, ninth, tenth, and eleventh flight of a first stage, meeting and surpassing CEO Elon Musk’s stated goal to fly a Falcon 9 first stage 10 times without major refurbishment.
We see that text data is ubiquitous in nature. There is a lot of text present in different forms such as posts, books, articles, and blogs. What is more interesting is the fact that there is a subset of Artificial Intelligence called Natural Language Processing (NLP) that would convert text into a form that could be used for machine learning. I know that sounds a lot but getting to know the details and the proper implementation of machine learning algorithms could ensure that one learns the important tools in the process.
Since the r e are newer and better libraries being created to be used for machine learning purposes, it would make sense to learn some of the state-of-the-art tools that could be used for predictions. I’ve recently come across a challenge on Kaggle about predicting the difficulty of the text.
The output variable, the difficulty of the text, is converted into a form that is continuous in nature. This makes the target variable continuous. Therefore, various regression techniques must be used for predicting the difficulty of the text. Since the text is ubiquitous in nature, applying the right processing mechanisms and predictions would be really valuable, especially for companies that receive feedback and reviews in the form of text.
TealBook, a startup that leverages AI to update and maintain a database of supply chain data, has raised $50 million in capital.
