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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.”
Elon Musk
Tesla confirmed HW3 can’t do Unsupervised FSD but there’s more to the story
Tesla confirmed HW3 vehicles cannot run unsupervised FSD, replacing its free upgrade promise with a discounted trade-in.
Tesla has officially confirmed that early vehicles with its Autopilot Hardware 3 (HW3) will not be capable of unsupervised Full Self-Driving, while extending a path forward for legacy owners through a discounted trade-in program. The announcement came by way of Elon Musk in today’s Tesla Q1 2026 earnings call.
🚨 Our LIVE updates on the Tesla Earnings Call will take place here in a thread 🧵
Follow along below: pic.twitter.com/hzJeBitzJU
— TESLARATI (@Teslarati) April 22, 2026
The history here matters. HW3 launched in April 2019, and Tesla sold Full Self-Driving packages to owners on the understanding that the hardware was sufficient for full autonomy. Some owners paid between $8,000 and $15,000 for FSD during that period. For years, as FSD’s AI models grew more demanding, HW3 vehicles fell progressively further behind, eventually landing on FSD v12.6 in January 2025 while AI4 vehicles moved to v13 and then v14. When Musk acknowledged in January 2025 that HW3 simply could not reach unsupervised operation, and alluded to a difficult hardware retrofit.
The near-term offering is more concrete. Tesla’s head of Autopilot Ashok Elluswamy confirmed on today’s call that a V14-lite will be coming to HW3 vehicles in late June, bringing all the V14 features currently running on AI4 hardware. That is a meaningful software update for owners who have been frozen at v12.6 for over a year, and it represents genuine effort to keep older hardware relevant. Unsupervised FSD for vehicles is now targeted for Q4 2026 at the earliest, with Musk describing it as a gradual, geography-limited rollout.
For HW3 owners, the over-the-air V14-lite update is welcomed, and the discounted trade-in path at least acknowledges an old obligation. What happens next with the trade-in pricing will define how this chapter ultimately gets written. If Tesla prices the hardware path fairly, acknowledges what early adopters are owed, and delivers V14-lite on the June timeline it committed to today, it has a real opportunity to convert one of the longest-running sore subjects among early adopters into a loyalty story.
Elon Musk
Tesla isn’t joking about building Optimus at an industrial scale: Here we go
Tesla’s Optimus factory in Texas targets 10 million robots yearly, with 5.2 million square feet under construction.
Tesla’s Q1 2026 Update Letter, released today, confirms that first generation Optimus production lines are now well underway at its Fremont, California factory, with a pilot line targeting one million robots per year to start. Of bigger note is a shared aerial image of a large piece of land adjacent to Gigafactory Texas, that Tesla has prominently labeled “Optimus factory site preparation.”
Permit documents show Tesla is seeking to add over 5.2 million square feet of new building space to the Giga Texas North Campus by the end of 2026, at an estimated construction investment of $5 billion to $10 billion. The longer term production target for that facility is 10 million Optimus units per year. Giga Texas already sits on 2,500 acres with over 10 million square feet of existing factory floor, and the North Campus expansion is being built to support multiple projects, including the dedicated Optimus factory, the Terafab chip fabrication facility (a joint Tesla/SpaceX/xAI venture), a Cybercab test track, road infrastructure, and supporting facilities.
Credit: TESLA
Texas makes strategic sense beyond the existing infrastructure. The state’s tax structure, lower labor costs relative to California, and the proximity to Tesla’s AI training cluster Cortex 1 and 2, both located at Giga Texas and now totaling over 230,000 H100 equivalent GPUs, means the Optimus software stack and the factory producing the hardware will share the same campus. Tesla’s Q1 report also confirmed completion of the AI5 chip tape out in April, the inference processor designed specifically to power Optimus units in the field.
As Teslarati reported, the Texas facility is intended to house Optimus V4 production at full scale. Musk told the World Economic Forum in January that Tesla plans to sell Optimus to the public by end of 2027 at a price between $20,000 and $30,000, stating, “I think everyone on earth is going to have one and want one.” He has previously pegged long term demand for general purpose humanoid robots at over 20 billion units globally, citing both consumer and industrial use cases.
Tesla (NASDAQ: TSLA) reported its earnings for the first quarter of 2026 on Wednesday afternoon. Here’s what the company reported compared to what Wall Street analysts expected.
The earnings results come after Tesla reported a miss on vehicle deliveries for the first quarter, delivering 358,023 vehicles and building 408,386 cars during the three-month span.
As Tesla transitions more toward AI and sees itself as less of a car company, expectations for deliveries will begin to become less of a central point in the consensus of how the quarter is perceived.
Nevertheless, Tesla is leaning on its strong foundation as a car company to carry forward its AI ambitions. The first quarter is a good ground layer for the rest of the year.
Tesla Q1 2026 Earnings Results
Tesla’s Earnings Results are as follows:
- Non-GAAP EPS – $0.41 Reported vs. $0.36 Expected
- Revenues – $22.387 billion vs. $22.35 billion Expected
- Free Cash Flow – $1.444 billion
- Profit – $4.72 billion
Tesla beat analyst expectations, so it will be interesting to see how the stock responds. IN the past, we’ve seen Tesla beat analyst expectations considerably, followed by a sharp drop in stock price.
On the same token, we’ve seen Tesla miss and the stock price go up the following trading session.
Tesla will hold its Q1 2026 Earnings Call in about 90 minutes at 5:30 p.m. on the East Coast. Remarks will be made by CEO Elon Musk and other executives, who will shed some light on the investor questions that we covered earlier this week.
You can stream it below. Additionally, we will be doing our Live Blog on X and Facebook.
Q1 2026 Earnings Call at 4:30pm CT https://t.co/pkYIaGJ32y
— Tesla (@Tesla) April 22, 2026
Tesla confirmed HW3 can’t do Unsupervised FSD but there’s more to the story
Tesla isn’t joking about building Optimus at an industrial scale: Here we go
