News
Researchers develop artificial intelligence that can identify cancer cells
Scientists from Osaka University in Japan have developed artificial intelligence (AI) that can identify different types of cancers based on microscopy images of their cells. The AI was also able to determine whether the cancer cells were resistant to radiation, and further learned the differences between human and animal cancers. Since the accuracy and timeliness of traditional methods of identifying cancer cells are prone to delays and errors, an accurate and automated system for accomplishing this would be beneficial to cancer research and treatment overall. The results of the scientists’ research were published in the December 2018 issue of Cancer Research.
The type of AI developed for the cell identification is called a convolutional neural network (CNN); it’s loosely based on the connectivity patterns used by neurons in the brain and primarily used for classifying images. As described in their publication, the scientists used a training set of 10,000 images each of human cervical cancer cells (ME-180) and mouse squamous cancer cells (NR-S1), e.g., the thin, flat types of cells found on the surface of the skin and thin linings around various organs throughout the body. They also included images of radioresistant clones with the set, and ultimately obtained a 96% accuracy in a 2,000 image test.
Because the types of cells in a single cancerous tumor can vary widely, identifying the specific cells present is important for determining the best treatment. Thus, having a tool to provide this information quickly and accurately could have a significant impact. The Osaka team hopes to expand the types of cancers their AI can identify and ultimately establish a universal system that can identify all cancer cell types.
Using artificial intelligence in the battle against cancer is being explored throughout the world as the number of uses devised expands. In one notable instance, a team of scientists from The Institute of Cancer Research in London and the University of Edinburgh has developed an AI technique called REVOLVER (repeated evolution of cancer) which identifies DNA mutation patterns in cancers to predict the ways they will change in response to treatment. Similar to how bacteria become resistant to antibiotics, so too can cancers become resistant to the drugs used against them. By removing the unpredictability variable in cancer behavior, scientists would be able to stay ahead of the disease’s progress and tailor treatments accordingly.
The collaboration between AI and healthcare overall is growing, not just in cancer research – even Google is making contributions to the field. Fortunately, the agencies regulating developments are also attuned to the changes. Earlier this year at the AcademyHealth 2018 Health Datapalooza, the U.S. Food and Drug Administration (FDA) Commissioner Scott Gottlieb, MD signaled the agency’s positive position towards the field. “AI holds enormous promise for the future of medicine, and we’re actively developing a new regulatory framework to promote innovation in this space and support the use of AI-based technologies,” he stated at the event. He also referred to the agency’s plans to streamline their regulations and tools to be sufficiently flexible to handle the rapid pace of advancements and “focus on the ways in which real-world data flows.”
News
Tesla takes a step towards removal of Robotaxi service’s safety drivers
Tesla watchers are speculating that the implementation of in-camera data sharing could be a step towards the removal of the Robotaxi service’s safety drivers.
Tesla appears to be preparing for the eventual removal of its Robotaxi service’s safety drivers.
This was hinted at in a recent de-compile of the Robotaxi App’s version 25.11.5, which was shared on social media platform X.
In-cabin analytics
As per Tesla software tracker @Tesla_App_iOS, the latest update to the Robotaxi app featured several improvements. These include Live Screen Sharing, as well as a feature that would allow Tesla to access video and audio inside the vehicle.
According to the software tracker, a new prompt has been added to the Robotaxi App that requests user consent for enhanced in-cabin data sharing, which comprise Cabin Camera Analytics and Sound Detection Analytics. Once accepted, Tesla would be able to retrieve video and audio data from the Robotaxi’s cabin.
Video and audio sharing
A screenshot posted by the software tracker on X showed that Cabin Camera Analytics is used to improve the intelligence of features like request support. Tesla has not explained exactly how the feature will be implemented, though this might mean that the in-cabin camera may be used to view and analyze the status of passengers when remote agents are contacted.
Sound Detection Analytics is expected to be used to improve the intelligence of features like siren recognition. This suggests that Robotaxis will always be actively listening for emergency vehicle sirens to improve how the system responds to them. Tesla, however, also maintained that data collected by Robotaxis will be anonymous. In-cabin data will not be linked to users unless they are needed for a safety event or a support request.
Tesla watchers are speculating that the implementation of in-camera data sharing could be a step towards the removal of the Robotaxi service’s safety drivers. With Tesla able to access video and audio feeds from Robotaxis, after all, users can get assistance even if they are alone in the driverless vehicle.
Investor's Corner
Mizuho keeps Tesla (TSLA) “Outperform” rating but lowers price target
As per the Mizuho analyst, upcoming changes to EV incentives in the U.S. and China could affect Tesla’s unit growth more than previously expected.
Mizuho analyst Vijay Rakesh lowered Tesla’s (NASDAQ:TSLA) price target to $475 from $485, citing potential 2026 EV subsidy cuts in the U.S. and China that could pressure deliveries. The firm maintained its Outperform rating for the electric vehicle maker, however.
As per the Mizuho analyst, upcoming changes to EV incentives in the U.S. and China could affect Tesla’s unit growth more than previously expected. The U.S. accounted for roughly 37% of Tesla’s third-quarter 2025 sales, while China represented about 34%, making both markets highly sensitive to policy shifts. Potential 50% cuts to Chinese subsidies and reduced U.S. incentives affected the firm’s outlook.
With those pressures factored in, the firm now expects Tesla to deliver 1.75 million vehicles in 2026 and 2 million in 2027, slightly below consensus estimates of 1.82 million and 2.15 million, respectively. The analyst was cautiously optimistic, as near-term pressure from subsidies is there, but the company’s long-term tech roadmap remains very compelling.
Despite the revised target, Mizuho remained optimistic on Tesla’s long-term technology roadmap. The firm highlighted three major growth drivers into 2027: the broader adoption of Full Self-Driving V14, the expansion of Tesla’s Robotaxi service, and the commercialization of Optimus, the company’s humanoid robot.
“We are lowering TSLA Ests/PT to $475 with Potential BEV headwinds in 2026E. We believe into 2026E, US (~37% of TSLA 3Q25 sales) EV subsidy cuts and China (34% of TSLA 3Q25 sales) potential 50% EV subsidy cuts could be a headwind to EV deliveries.
“We are now estimating TSLA deliveries for 2026/27E at 1.75M/2.00M (slightly below cons. 1.82M/2.15M). We see some LT drivers with FSD v14 adoption for autonomous, robotaxi launches, and humanoid robots into 2027 driving strength,” the analyst noted.
News
Tesla’s Elon Musk posts updated Robotaxi fleet ramp for Austin, TX
Musk posted his update on social media platform X.
Elon Musk says Tesla will “roughly double” its supervised Robotaxi fleet in Austin next month as riders report long wait times and limited availability across the pilot program in the Texas city. Musk posted his update on social media platform X.
The move comes as Waymo accelerates its U.S. expansion with its fully driverless freeway service, intensifying competition in autonomous mobility.
Tesla to increase Austin Robotaxi fleet size
Tesla’s Robotaxi service in Austin continues to operate under supervised conditions, requiring a safety monitor in the front seat even as the company seeks regulatory approval to begin testing without human oversight. The current fleet is estimated at about 30 vehicles, StockTwists noted, and Musk’s commitment to doubling that figure follows widespread rider complaints about limited access and “High Service Demand” notifications.
Influencers and early users of the Robotaxi service have observed repeated failures to secure a ride during peak times, highlighting a supply bottleneck in one of Tesla’s most visible autonomy pilots. The expansion aims to provide more consistent availability as the company scales and gathers more real-world driving data, an advantage analysts often cite as a differentiator versus rivals.
Broader rollout plans
Tesla’s Robotaxi service has so far only been rolled out to Austin and the Bay Area, though reports have indicated that the electric vehicle maker is putting in a lot of effort to expand the service to other cities across the United States. Waymo, the Robotaxi service’s biggest competitor, has ramped its service to areas like the San Francisco Bay Area, Los Angeles, and Phoenix.
Analysts continue to highlight Tesla’s long-term autonomy potential due to its global fleet size, vertically integrated design, and immense real-world data. ARK Invest has maintained that Tesla Robotaxis could represent up to 90% of the company’s enterprise value by 2029. BTIG analysts, on the other hand, added that upcoming Full Self-Driving upgrades will enhance reasoning, particularly parking decisions, while Tesla pushes toward expansions in Austin, the Bay Area, and potentially 8 to 10 metro regions by the end of 2025.
Tesla takes a step towards removal of Robotaxi service’s safety drivers
Mizuho keeps Tesla (TSLA) “Outperform” rating but lowers price target
