Government-backed Shenzhen Capital Group has led Series B funding for Origin Quantum.
A Purdue University chemical engineer has improved upon traditional methods to produce off-the-shelf human immune cells that show strong antitumor activity, according to a paper published in the peer-reviewed journal Cell Reports.
Xiaoping Bao, a Purdue University assistant professor from the Davidson School of Chemical Engineering, said CAR-neutrophils, or chimeric antigen receptor neutrophils, and engraftable HSCs, or hematopoietic stem cells, are effective types of therapies for blood diseases and cancer. Neutrophils are the most abundant white cell blood type and effectively cross physiological barriers to infiltrate solid tumors. HSCs are specific progenitor cells that will replenish all blood lineages, including neutrophils, throughout life.
“These cells are not readily available for broad clinical or research use because of the difficulty to expand ex vivo to a sufficient number required for infusion after isolation from donors,” Bao said. “Primary neutrophils especially are resistant to genetic modification and have a short half-life.”
Innovative Solutions For Unmet Needs Of Older Adults & Their Caregivers — Keith Camhi, Managing Director, Techstars Future of Longevity Accelerator — A Partnership With Melinda Gates Pivotal Ventures.
Keith Camhi is Managing Director, Techstars Future of Longevity Accelerator (https://www.techstars.com/accelerators/longevity), a program, run in partnership with Pivotal Ventures (https://www.pivotalventures.org/), an investment and incubation company created by Melinda French Gates, focusing on innovative solutions to address the unmet needs of older adults and their caregivers. The longevity accelerator core program themes include: Caregiver Support, Care Coordination, Aging in Place, Financial Wellness and Resilience, Preventive Health (both Physical and Cognitive), and Social Engagement.
Keith was previously the SVP of Accelerators for Techstars globally and was inspired to move to the MD role for the longevity program based on having built a venture-backed startup serving older adults himself, having experienced the gaps in America’s care giving infrastructure firsthand, and wanting to support entrepreneurs who are building solutions to address this substantial market opportunity.
More on Tesla’s new battery tech.
Cory and Antonio overview Tesla’s improved cell interconnects, current collector layout, voltage sensor harness (VSH), and battery management system (BMS) of the 4,680 structural pack.
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AI Philosophy
The AI model was trained using answers from Dennett on a range of questions about free will, whether animals feel pain and even favorite bits of other philosophers. The researchers then asked different groups of people to compare the AI’s responses and Dennett’s real answers and see if they could tell them apart. They used responses from 302 random people online who followed a link from Schwitzgebel’s blog, 98 confirmed college graduates from the online research platform Prolific, and 25 noted Dennett experts. Immersion in Dennett’s philosophy and work didn’t prevent anyone from struggling to identify the source of the answers, however.
The research platform participants only managed an average success rate of 1.2 out of 5 questions. The blog readers and experts answered ten questions, with the readers hitting an average score of 4.8 out of 10. That said, not a single Dennett expert was 100% correct, with only one answering nine correctly and an average of 5.1 out of 10, barely higher than the blog readers. Interestingly, the question whose responses most confused the Dennett experts was actually about AI sentience, specifically if people could “ever build a robot that has beliefs?” Despite the impressive performance by the GPT-3 version of Dennett, the point of the experiment wasn’t to demonstrate that the AI is self-aware, only that it can mimic a real person to an increasingly sophisticated degree and that OpenAI and its rivals are continuing to refine the models so that similar quizzes will likely get harder to pass.
Mad Scientists Created Franken-Pigs
Posted in futurism
A model for autonomous underwater robots could help them navigate previously-unexplored waters.
I don’t buy the Jeanne Calment story.
How can we live longer and be healthier? These startups are trying to extend our lives.
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Multivariable calculus, differential equations, linear algebra—topics that many MIT students can ace without breaking a sweat—have consistently stumped machine learning models. The best models have only been able to answer elementary or high school-level math questions, and they don’t always find the correct solutions.
Now, a multidisciplinary team of researchers from MIT and elsewhere, led by Iddo Drori, a lecturer in the MIT Department of Electrical Engineering and Computer Science (EECS), has used a neural network model to solve university-level math problems in a few seconds at a human level.
The model also automatically explains solutions and rapidly generates new problems in university math subjects. When the researchers showed these machine-generated questions to university students, the students were unable to tell whether the questions were generated by an algorithm or a human.
Scientists and engineers are constantly developing new materials with unique properties that can be used for 3D printing, but figuring out how to print with these materials can be a complex, costly conundrum.
Often, an expert operator must use manual trial-and-error—possibly making thousands of prints—to determine ideal parameters that consistently print a new material effectively. These parameters include printing speed and how much material the printer deposits.
MIT researchers have now used artificial intelligence to streamline this procedure. They developed a machine-learning system that uses computer vision to watch the manufacturing process and then correct errors in how it handles the material in real-time.