Archive for the ‘Machine Learning’ Category

Googles new ML Kit SDK keeps all machine learning on the device – SlashGear

Smartphones today have become so powerful that sometimes even mid-range handsets can support some fancy machine learning and AI applications. Most of those, however, still rely on cloud-hosted neural networks, machine learning models, and processing, which has both privacy and efficiency drawbacks. Contrary to what most would expect, Google has been moving to offload much of that machine learning activity from the cloud to the device and its latest machine learning development tool is its latest step in that direction.

Googles machine learning or ML Kit SDK has been around for two years now but it has largely been tied to its Firebase mobile and web development platform. Like many Google products, this creates a dependency on a cloud-platform that entails not just some latency due to network bandwidth but also risks leaking potentially private data in transit.

While Google is still leaving that ML Kit + Firebase combo available, it is now also launching a standalone software development kit or SDK for both Android and iOS app developers that focuses on on-device machine learning. Since everything happens locally, the users privacy is protected and the app can function almost in real-time regardless of the speed of the Internet connection. In fact, an ML-using app can even work offline for that matter.

The implications of this new SDK can be quite significant but it still depends on developers switching from the Firebase version to the standalone SDK. To give them a hand, Google created a code lab that combines the new ML Kit with its CameraX app in order to translate text in real-time without connecting to the Internet.

This can definitely help boost confidence in AI-based apps if the user no longer has to worry about privacy or network problems. Of course, Google would probably prefer that developers keep using the Firebase connection which it even describes as getting the best of both products.

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Googles new ML Kit SDK keeps all machine learning on the device - SlashGear

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

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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.

The skills to get into this exploding field can be had in training likeThe Ultimate Artificial Intelligence Scientist Certification Bundle ($34.99, over 90 percent off).

The collection features four courses and almost 80 hours of content, introducing interested students to the skills, tools and processes needed to not only understand AI, but apply that knowledge to any given field. With nearly 200,000 positive reviews offered from more than a million students who have taken the courses, its clear why these Super Data Science-taught training sessions attract so many followers.

The coursework begins at the heart of AI and machine learning with thePython A-Zcourse.

The language most prominently linked to the development of such techniques, students follow step-by-step tutorials to understand how Python coding works, then apply that training to actual real-world exercises. Even learners who had never delved into AIs inner workers said the course made them fascinated to learn more in data science.

With the basic underpinnings in hand, students move toMachine Learning A-Z, where more advanced theories and algorithms take on practical shape with a true users guide to crafting your own thinking computers. Students get a true feel for machine learning from professional data scientists, who help even complex ideas like dimensionality reduction become relatable.

InDeep Learning A-Z, large data sets work hand-in-hand with programming fundamentals to help students unlock AI principles in some exciting projects. Students work with artificial neural networks and put them into practice to see how machines can actually think for themselves.

Finally,Tensorflow 2.0: A Complete Guide on the Brand New Tensorflowtakes a closer look at Tensorflow, one of the most powerful tools AI experts use to craft working networks. Actual Tensorflow exercises will explain how to build models and construct large-scale neural networks so machines can understand all the information theyre processing, then use that data to define their own solutions to problems.

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

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

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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How Work Will Change Following the Pandemic – Stanford 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

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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...