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Cellino, a company developing a platform to automate stem cell production, presented today at TechCrunch Disrupt 2021s Startup Battlefield to detail how its system, which combines AI technology, machine learning, hardware, software and yes, lasers! could eventually democratize access to cell therapies. It aims to bring down costs associated with the manufacturing of human cells, while also increasing yields.

Founded by a team whose backgrounds include physics, stem cell biology and machine learning, Cellino operates in the regenerative medicine industry. This space is currently undergoing a revolution, where new developments in gene and cell therapies could lead to breakthrough cures for a number of leading diseases. For example, the use of personalized human retinal cells could be transplanted to halt or reverse age-related macular degeneration, which can cause blindness. But today, such cell therapies are out of reach for most people because the process of cell production hasnt been automated or made scalable and efficient.

Instead, human cells being used now in these clinical trials are mostly being made by hand by scientists who are looking at cells and evaluating using their many years of training and expertise which cells are low quality and need to be removed. They then scrape away those unwanted cells with a pipette tip. The process, as you can imagine, is time-consuming and produces only a small yield. In this manual process, youd see a yield of about 10% to 20% of cells that would be able to pass the final quality assurance tests required for human transplant.

Cellino is working to improve this process in order to produce more cells of higher quality. Its goal is to push the yield to at least 80% over the next three years.

To do so, Cellinos system is automating all the human steps in the production process using machine learning techniques.

To identify which cells are high quality or low quality, the company is collecting large training data sets where its teaching algorithms to make determinations about cell quality based on a variety of factors. This includes the cell morphology meaning, the shape, size and density of cells. Fluorescence-based surface markers can also be used to identify other factors of importance to the line of cells being produced, like the location of proteins on the cell, for example.

By using machine learning and AI to do the identification based on standard and well-accepted biological assays used by the FDA, the system could move away from human annotation and the variability that introduces into the process of human cell production.

After Cellinos software has identified which low-quality cells need to be removed, it then uses a laser to target them. The laser creates large enough cavitation bubbles to kill the cell, but its done in a highly localized way where youre not harming the neighboring cells, as thermal heat does not dissipate to the nearby cells. This is also a more precise technique than the manual method. (Cellinos system has a 5-micron resolution, while cells are 10-15 microns in size). This results in a throughput of about 5,000 cells per minute, which is highly efficient compared with manual techniques.

Over time, this automation and efficiency could bring the cost down from nearly a million dollars per patient, which is what clinicians have to pay today to run a clinical trial, when outsourcing cell production. Cellino aims to get the cost down into the tens of thousands of dollars over time.

By scaling cell production, personalized cell therapies could also help a broader range of patients compared with other techniques relying on banks of stem cells. These arent always genetically diverse samples, leaving smaller ethnic groups out of the progress being made in this space. Banked cells also require recipients to take immunosuppressants, as the cells arent your own and the body may reject them.

The use of lasers is an idea developed by Cellino co-founder and CEO Nabiha Saklayen, who patented an invention in cellular laser editing while at Harvard earning her PhD in Physics. She was encouraged to turn the technology into a startup by her collaborators, who included had leading biologists like George Church and David Scadden.

Not all scientists become entrepreneurs, and I became an entrepreneur because I had an amazing support network around me, notes Saklayen, of the push to join the startup space. She immediately recruited Marinna Madrid, an applied physicist she had worked with for years on the co-invention of laser-based intracellular delivery techniques, as her other co-founder. To gain more mentorship about growing a startup, Saklayen turned to the Boston area startup ecosystem.

I didnt know anything about startups. I wanted to work with people who knew how to build companies, how to commercialize technology, how to build instruments and the Boston ecosystem is fantastic in that way. So I started connecting with lots of people in those early weeks anybody that was in the biotech realm or Harvard Business School, Saklayen explains.

This led her to Cellino co-founder and CTO Mattias Wagner, who had built companies before in the optics and instrumentation space.

Thats how the founding team came together. It was very complementary because Marina and I were co-inventors of the original technology that inspired the platform and Mattias brought this tremendous background in semiconductors and optical instrumentation, says Saklayen.

Since its 2017 founding, Cellino has gone on to raise $16 million in seed funding in a round co-led byThe Engine and Khosla Ventures, with participation from Humboldt Fund and 8VC.

The company is now collaborating with the NIH on compatibility studies. Currently, that means Cellino is making stem cells on its system which its then comparing with the ones made at the NIH that are already being tested in humans for personalized cell therapies for retinal diseases. Cellino later hopes to use its system to address areas like Parkinsons, muscle disorders and skin grafts, among others.

The company wanted to present at TechCrunch Disrupt to share more about what its building and to source new talent.

For me, its about talking about this idea around democratization and industrialization of cell therapies. I really want to get that message out because that is the movement we need to drive over the next decade for all of these cell therapies to be accessible to all patients, says Saklayen.

Cellinos angle is also very unique in the sense that, because we have this automated system to manufacture human cells, our system could make cells for every human being in this country, in the world, she continues. And there are a lot of cell therapy approaches that are looking to use off-the-shelf cells and off-the-shelf therapies, which will only work for certain parts of the population. As the U.S. becomes more diverse, ethnically, we need personalized solutions for everybody.

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Cellino is using AI and machine learning to scale production of stem cell therapies - TechCrunch

IBM Research and Arctoris announce a research collaboration to test a closed-loop platform.

Ulysses is the world's first fully automated drug discovery platform developed and operated by Arctoris based in Oxford, Boston and Singapore.

Image: Arctoris

IBM Research and Arctoris are bringing the power of artificial intelligence and robotic automation to the process of developing new drugs. The two companies aim to make smarter choices early on in the process, iterate faster and improve the odds of finding an effective treatment.

IBM Research contributed two platforms to the project. RXN for Chemistry uses natural language processing to automate synthetic chemistry and artificial intelligence to make predictions about which compound has the highest chance of success. That information is passed on to RoboRXN, an automated platform for molecule synthesis.

Arctoris, a drug discovery company, brought Ulysses to the project. The company's automated platform uses robots and digital data capture to conduct lab experiments in cell and molecular biology and biochemistry and biophysics. Experiments conducted with Ulysses generate 100 times more data points per assay compared to industry-standard manual methods, according to Arctoris.

IBM Research will design and synthesize new chemical matter that Arctoris will test and analyze. The resulting data will inform the next iteration of the experiment.

SEE: Drug discovery company works with ethnobotanists and data scientists

Thomas A. Fleming, Arctoris co-founder and COO, described this project as "a world-first closed-loop drug discovery project" that combines AI and robotics-powered drug discovery.

"This collaboration will showcase how the combination of our unique technology platforms will lead to accelerated research based on better data enabling better decisions," he said in a press release.

A research paper about closed-loop drug discovery describes the process as a centralized workflow controlled by machine learning. The system generates a hypothesis, synthesizes a lead drug candidate, tests it and then stores the data. This comprehensive process could "reduce bottlenecks and standards discrepancies and eliminate human biases in hypothesis generation," according to the paper.

Automating lab work results in better data which in turn means less rework and a savings of time and money, Poppy Roworth, head of laboratory at Arctoris, explained in a blog post. She described the benefits of automation this way: "I no longer have to manually pipette each well at a time of a 96 or 384 well plate, which is highly beneficial for my sanity when there is a stack of more than 5 or 10 to get through." By automating the protocol, scientists can use time previously spent in the lab on "planning the next experiment, designing new projects with clients, reading literature and keeping up to day with other projects."

Matteo Manica, a research scientist at IBM Research Europe, Zurich, is coordinating the project and said in a press release that this work is a unique opportunity to quantify the impact of AI and automation technologies in accelerating scientific discovery.

"In our collaboration, we demonstrate a pipeline to perform iterative design cycles where generative models suggest candidates that are synthesized with RoboRXN and screened with Ulysses," he said. "The data produced by Ulysses will then be used to establish a feedback loop to retrain the generative AI and improve the proposed leads in a completely data-driven fashion."

More than 3,000 researchers in 16 locations on five continents work for IBM Research. Arctoris is a biotech company headquartered in Oxford with offices in Boston and Singapore. The collaboration is ongoing.

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Robots and machine learning researchers combine forces to speed up the drug development process - TechRepublic

Solutions Review editors compiled this list of the best AWS machine learning courses and online training to use when growing your skills.

With this in mind, weve compiled this list of the best AWS machine learning courses and online training to consider if youre looking to grow your cloud artificial intelligence and automation skills for work or play. This is not an exhaustive list, but one that features the best AWS machine learning courses and training from trusted online platforms. This list of the best AWS machine learning courses below includes links to the modules and our take on each.

Platform: Coursera

Description: This course will teach you how to get started with AWS Machine Learning. Key topics include: Machine Learning on AWS, Computer Vision on AWS, and Natural Language Processing (NLP) on AWS. Each topic consists of several modules deep-diving into variety of ML concepts, AWS services as well as insights from experts to put the concepts into practice.

Platform: edX

Description: This course will teach application developers how to use Amazon SageMaker to simplify the integration of machine learning into their applications. Key topics include an overview of Machine Learning and problems it can help solve, using a Jupyter Notebook to train a model based on SageMakers built-in algorithms and, using SageMaker to publish the validated model.

Platform: Pluralsight

Description: In this course, youll learn how to analyze, visualize, preprocess and feature engineer datasets to make them ready for subsequent machine learning steps. Youll also learn how to prepare your data for the machine learning pipeline by doing preprocessing and feature engineering.

Platform: Pluralsight

Description: First, youll explore what ML is and how it relates to artificial intelligence and deep learning. Next, youll learn how to identify and frame opportunities for machine learning. Then, youll discover the end-to-end machine learning process: fetching, cleaning, and preparing data, training and evaluating models, and deploying and monitoring models.

Platform: Udacity

Description: Learn advanced machine learning techniques and algorithms and how to package and deploy your models to a production environment. Gain practical experience using Amazon SageMaker to deploy trained models to a web application and evaluate the performance of your models. A/B test models and learn how to update the models as you gather more data, an important skill in industry.

Platform: Udemy

Description: In addition to the9-hour video course, a 30-minutequick assessment practice examis included that consists of the same topics and style as the real exam. Youll also getfour hands-on labsthat allow you to practice what youve learned, and gain valuable experience in model tuning, feature engineering, and data engineering.

Platform: Udemy

Description: This course is designed for anyone who is interested in AWS cloud-based machine learning and data science. Learners should have familiarity with Python, an AWS account, basic knowledge of Pandas, Numpy, and Matplotlib. The ideal student for this course is willing to learn and participate in the course Q&A forum when help is needed.

Platform: Udemy

Description: With over 500 slides and over 50 practice questions, this course is by far the most comprehensive course on the market that provides students with the foundational knowledge to pass the AWS Machine Learning Certification exam like a pro! This course covers the most important concepts without any fillers or irrelevant information.

Tim is Solutions Review's Editorial Director and leads coverage on big data, business intelligence, and data analytics. A 2017 and 2018 Most Influential Business Journalist and 2021 "Who's Who" in data management and data integration, Tim is a recognized influencer and thought leader in enterprise business software. Reach him via tking at solutionsreview dot com.

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The 8 Best AWS Machine Learning Courses and Online Training for 2021 - Solutions Review

As a young startup, SambaNova Systems is already making a mark in the fast-growing AI hardware industry.

The vendor, based in Palo Alto, Calif., started in 2017 with a mission of transforming how enterprises and research labs with high compute power needs deploy AI, and providing high-performance and high-accuracy hardware-software systems that are still easy to use, said Kunle Olukotun, co-founder and chief technologist.

Its technology is being noticed. SambaNova has attracted more than $1.1 billion in venture financing. With a valuation of $5.1 billion, it is one of the most well-funded AI startups and it is already competing with the likes of AI chip giant Nvidia.

SambaNova's hallmark is its Dataflow architecture. Using the extensible machine learning services platform, enterprises can specify various configurations, whether grouping kernelstogether on asingle chip, or on multiple chips, in a rack or on multiple racks in the SambaNova data center.

Essentially, the vendor leases to enterprise clients the processing power of its proprietary AI chips and creates machine learning models based on domain data supplied by the customer, or customers can buy SambaNova chips and run their own AI systems on them.

While other vendors have offered either just chips or just the software, SambaNova provides the entire rack, which will make AI more accessible to a wider range of organizations, said R "Ray" Wang, founder and principal analyst at Constellation Research.

"The irony of AI automation is that it's massively manual today," Wang said. "What [SambaNova is] trying to do is take away a lot of that manual process and a lot of the human error and make it a lot more accessible to get AI."

Wang added that SambaNova offers AI chips that are among the most powerful on the market.

While it's known in some ways as an AI hardware specialist, SambaNova prides itself in taking a "software-defined approach" to building its AI technology stack.

"We didn't build some hardware thinking: 'OK, now developers go out and figure it out,'" said Marshall Choy, vice president of product at SambaNova. Instead, he said the vendor focused on the problems of scale, performance, accuracy and ease of use for machine learning data flow computing. Then they built the infrastructure engine to support those needs.

The irony of AI automation is that it's massively manual today. R 'Ray' WangFounder and principal analyst, Constellation Research

SambaNova breaks up its customers into two groups: the Fortune 50 and the "Fortune everybody else." For the first group, SambaNova's data platform enables enterprise data teams to innovate and generate new models, Choy said.

The other group is made up of enterprises that lack the time, resources or desire to become experts in machine learning and AI. For these organizations, SambaNova offers Dataflow as a service.

SambaNova says this approach helps smaller enterprises by reducing the complexities of buying and maintaining hardware infrastructure and selecting, optimizing and maintaining machine learning models.

This creates a "greater AI equity and accessibility of technology than has previously been held in the hands of only the biggest, most wealthy tech companies," Choy said.

SambaNova has already attracted some big-name customers.

Oneis the U.S. Department of Energy's Argonne National Laboratory in Illinois.

Using SambaNova's DataScale system, Argonne trained a convolutional neural network (CNN) with images beyond 50k x 50k resolution. Previously, when Argonne tried to train the CNN on GPUs, they found that the images were too large and had to be resized to 50% resolution, according to SambaNova.

"We're seeing new ways of computing," Wang said. "This approach to getting to AI is going to be one of many. I think other people are going to try different approaches, but this one seems very promising."

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SambaNova makes a mark in the AI hardware realm - TechTarget