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There isnt a new story every week about an interesting new application of artificial intelligence and machine learning happening out there somewhere. There are actually at least five of those stories. Maybe 10. Sometimes, even more.

Like how UK officials are using AI tospot invasive plant species and stop thembefore they cause expensive damage to roads. Or how artificial intelligence is playing a key role inthe fight against COVID-19. Or even in the ultimate in mind-bending Black Mirror-type ideas, how AI is actually being used to help tobuild and manageother AIs.

Scariness aside, the power of artificial intelligence and machine learning to revolutionize the planet is taking hold in virtually every industry imaginable. With implications like that, it isnt hard to understand how a computer science type trained in AI practices can become a key member of any business witha paycheck to match.

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AI and Machine Learning Are Changing Everything. Here's How You Can Get In On The Fun - ExtremeTech

By applying unsupervised and automated machine learning techniques to the analysis of millions of cancer cells, Rebecca Ihrie and Jonathan Irish, both associate professors of cell and developmental biology, have identified new cancer cell types in brain tumors. Machine learning is a series of computer algorithms that can identify patterns within enormous quantities of data and get smarter with more experience. This finding holds the promise of enabling researchers to better understand and target these cell types for research and therapeutics for glioblastoma an aggressive brain tumor with high mortality as well as the broader applicability of machine learning to cancer research.

With their collaborators, Ihrie and Irish developed Risk Assessment Population IDentification (RAPID), an open-source machine learning algorithm that revealed coordinated patterns of protein expression and modification associated with survival outcomes.

The article, Unsupervised machine learning reveals risk stratifying glioblastoma tumor cells was published online in the journal eLife on June 23. RAPID code and examples are available on the cytolab Github page.

For the past decade, the research community has been working to leverage machine learnings ability to absorb and analyze more data for cancer cell research than the human mind alone can process. Without any human oversight, RAPID combed through 2 million tumor cells with at least 4,710 glioblastoma cells from each patient from 28 glioblastomas, flagging the most unusual cells and patterns for us to look into, said Ihrie. Were able to find the needles in the haystack without searching the entire haystack. This technology lets us devote our attention to better understanding the most dangerous cancer cells and to get closer to ultimately curing brain cancer.

Fed into RAPID were data on cellular proteins that govern the identity and function of neural stem cells and other brain cells. The data type used is called single-cell mass cytometry, a measurement technique typically applied to blood cancer. Once RAPIDs statistical analysis was complete and the needles in the haystack were found, only those cells were studied. One of the most exciting results of our research is that unsupervised machine learning found the worst offender cells without needing the researchers to give it clinical or biological knowledge as context, said Irish, also scientific director of Vanderbilts Cancer & Immunology Core. The findings of this study currently represent the biggest biology advance from my lab at Vanderbilt.

The researchers machine learning analysis enabled their team to study multiple characteristics of the proteins in brain tumor cells in relation to other characteristics, delivering new and unexpected patterns. The collaboration between our two labs, the support that we received for this high-risk work from Vanderbilt and the Vanderbilt-Ingram Cancer Center (VICC) and the fruitful collaboration with neurosurgeons and pathologists who provided a unique opportunity to study human cells right out of the brain allowed us to achieve this milestone, said Ihrie and Irish in a joint statement. The co-first authors of the paper are former Vanderbilt graduate students Nalin Leelatian, a current neuropathology resident at Yale (Irish lab), and Justine Sinnaeve (Ihrie lab). Through her research and work on this topic, Leelatian earned the American Brain Tumor Association (ABTA) Scholar-in-Training Award, American Association for Cancer Research (AACR) in April 2017.

The applicability of this research extends beyond cancer research to data analysis techniques for broader human disease research and laboratory modeling of diseases using multiple samples. The paper also demonstrates that these complex patterns, once found, can be used to develop simpler classifications that can be applied to hundreds of samples. Researchers studying glioblastoma brain tumors will be able to refer to these findings as they test to see if their own samples are comparable to the cell and protein expression patterns discovered by Ihrie, Irish, and collaborators.

This work was supported by the Michael David Greene Brain Cancer Fund, a discovery grant for brain tumor research established in 2004. The grant was recently renewed for another five years to support Ihrie and Irishs continued research on glioblastoma. Additional support was provided by the National Institutes of Health, including the National Cancer Institute and National Institute of Neurological Disorders and Stroke, VICC and VICC Ambassadors, the Vanderbilt International Scholars program, a Vanderbilt University Discovery Grant, an Alpha Omega Alpha Postgraduate Award, a Society of Neurological Surgeons/RUNN Award, a Burroughs Wellcome Fund Physician-Scientist Institutional Award, the Vanderbilt Institute for Clinical and Translational Research, and the Southeastern Brain Tumor Foundation.

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Discovery of aggressive cancer cell types by Vanderbilt researchers made possible with machine learning techniques - Vanderbilt University News

Economists use the term hysteresis to describe the phenomenonthat, when conditions in an economy change, the effects of that change often remain even after the conditions return to normal.

COVID and its impact on the workforce may provide a good example of hysteresis, said HAI Distinguished Fellow and MIT professor Erik Brynjolfsson, who will join Stanford faculty in July 2020 as the director of the new Digital Economy Lab.

To keep workers safe and continue functioning, companies have ramped up remote work and are aggressively automating someoperations and exploring machine learning.

Some of these changes are going to be permanent, he said during Stanford HAIs recent online conference COVID+AI: The Road Ahead. The question is, what parts of the economy are going to be most affected by the adoption of these technologies, andwhich parts will be less affected?

Brynjolfsson worked with Carnegie Mellon professor Tom Mitchell, MIT postdoc Daniel Rock, and others on a series of papers identifying the tasks most suitable for machine learning (ML). They applied this rubric to 950 occupations and 18,000 specific occupation tasks.

More tasks in lower-wage jobs could be replaced by machine learning applications, they found. For example, ML today can better recognize a cucumber or a banana and handle some cashier skills. But some high-paid jobs can also be impacted, such as airline pilots. No occupation is completely immune, Brynjolfsson said.

Certain industries are also more impacted than others, he noted. Manufacturing, retailing, transportation, and accommodation and food services have many tasks suitable for machine learning.

Additionally, different areas of the country will be affected unevenly. The kinds of work that people do in Wyoming are very different from what they do in Manhattan or Miami, he said.

The researchers data also allowed them to examine ML impact on individual occupations. Roles like tellers, executive assistants, and personal bankers have a large percentage of tasks that are suitable for ML.

Our tool gives them a way to have a path for what to do next, Brynjolfsson said. Personal bankers could develop more skills not subject to machine learning, like leadership, product development, or customer relations, and move away from the skills more suitable to ML like credit authorization. Another option: Find new roles with similar skill sets. A personal banker might transition to business analyst or mortgage loan officer, roles ML is less likely to disrupt.

With a little bit of training, they're in a position to be much less vulnerable to the machine learning revolution, he noted.

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How Work Will Change Following the Pandemic - Stanford University News

The report titled, Machine Learning For Managing Diabetes Market boons an in-depth synopsis of the competitive landscape of the market globally, thus helping establishments understand the primary threats and prospects that vendors in the market are dealt with. It also incorporates thorough business profiles of some of the prime vendors in the market. The report includes vast data relating to the recent discovery and technological expansions perceived in the market, wide-ranging with an examination of the impact of these intrusions on the markets future development.

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Best Report Machine Learning For Managing Diabetes Market (COVID 19 Updated) Climbs on Positive Outlook of Excellent Growth by 2027: Allscripts...

GPUs became the hardware of choice for deep learning largely by coincidence. The chips were initially designed to quickly render graphics in applications such as video games. Unlike CPUs, which have four to eight complex cores for doing a variety of computation, GPUs have hundreds of simple cores that can perform only specific operationsbut the cores can tackle their operations at the same time rather than one after another, shrinking the time it takes to complete an intensive computation.

It didnt take long for the AI research community to realize that this massive parallelization also makes GPUs great for deep learning. Like graphics-rendering, deep learning involves simple mathematical calculations performed hundreds of thousands of times. In 2011, in a collaboration with chipmaker Nvidia, Google found that a computer vision model it had trained on 2,000 CPUs to distinguish cats from people could achieve the same performance when trained on only 12 GPUs. GPUs became the de facto chip for model training and inferencingthe computational process that happens when a trained model is used for the tasks it was trained for.

But GPUs also arent perfect for deep learning. For one thing, they cannot function as a standalone chip. Because they are limited in the types of operations they can perform, they must be attached to CPUs for handling everything else. GPUs also have a limited amount of cache memory, the data storage area nearest a chips processors. This means the bulk of the data is stored off-chip and must be retrieved when it is time for processing. The back-and-forth data flow ends up being a bottleneck for computation, capping the speed at which GPUs can run deep-learning algorithms.

NEURAL MAGIC

In recent years, dozens of companies have cropped up to design AI chips that circumvent these problems. The trouble is, the more specialized the hardware, the more expensive it becomes.

So Neural Magic intends to buck this trend. Instead of tinkering with the hardware, the company modified the software. It redesigned deep-learning algorithms to run more efficiently on a CPU by utilizing the chips large available memory and complex cores. While the approach loses the speed achieved through a GPUs parallelization, it reportedly gains back about the same amount of time by eliminating the need to ferry data on and off the chip. The algorithms can run on CPUs at GPU speeds, the company saysbut at a fraction of the cost. It sounds like what they have done is figured out a way to take advantage of the memory of the CPU in a way that people havent before, Thompson says.

Neural Magic believes there may be a few reasons why no one took this approach previously. First, its counterintuitive. The idea that deep learning needs specialized hardware is so entrenched that other approaches may easily be overlooked. Second, applying AI in industry is still relatively new, and companies are just beginning to look for easier ways to deploy deep-learning algorithms. But whether the demand is deep enough for Neural Magic to take off is still unclear. The firm has been beta-testing its product with around 10 companiesonly a sliver of the broader AI industry.

We want to improve not just neural networks but also computing overall.

Neural Magic currently offers its technique for inferencing tasks in computer vision. Clients must still train their models on specialized hardware but can then use Neural Magics software to convert the trained model into a CPU-compatible format. One client, a big manufacturer of microscopy equipment, is now trialing this approach for adding on-device AI capabilities to its microscopes, says Shavit. Because the microscopes already come with a CPU, they wont need any additional hardware. By contrast, using a GPU-based deep-learning model would require the equipment to be bulkier and more power hungry.

Another client wants to use Neural Magic to process security camera footage. That would enable it to monitor the traffic in and out of a building using computers already available on site; otherwise it might have to send the footage to the cloud, which could introduce privacy issues, or acquire special hardware for every building it monitors.

Shavit says inferencing is also only the beginning. Neural Magic plans to expand its offerings in the future to help companies train their AI models on CPUs as well. We believe 10 to 20 years from now, CPUs will be the actual fabric for running machine-learning algorithms, he says.

Thompson isnt so sure. The economics have really changed around chip production, and that is going to lead to a lot more specialization, he says. Additionally, while Neural Magics technique gets more performance out of existing hardware, fundamental hardware advancements will still be the only way to continue driving computing forward. This sounds like a really good way to improve performance in neural networks, he says. But we want to improve not just neural networks but also computing overall.

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The startup making deep learning possible without specialized hardware - MIT Technology Review