In order to help build increasingly effective care pathways in healthcare, modern artificial intelligence technologies must be adopted and embraced. Events such as the AI & Machine Learning Convention are essential in providing medical experts around the UK access to the latest technologies, products and services that are revolutionising the future of care pathways in the healthcare industry.

AI has the potential to save the lives of current and future patients and is something that is starting to be seen across healthcare services across the UK. Looking at diagnostics alone, there have been large scale developments in rapid image recognition, symptom checking and risk stratification.

AI can also be used to personalise health screening and treatments for cancer, not only benefiting the patient but clinicians too enabling them to make the best use of their skills, informing decisions and saving time.

The potential AI will have on the NHS is clear, so much so, NHS England is setting up a national artificial intelligence laboratory to enhance the care of patients and research.

The Health Secretary, Matt Hancock, commented that AI had enormous power to improve care, save lives and ensure that doctors had more time to spend with patients, so he pledged 250M to boost the role of AI within the health service.

The AI and Machine Learning Convention is a part of Mediweek, the largest healthcare event in the UK and as a new feature of the Medical Imaging Convention and the Oncology Convention, the AI and Machine Learning expo offer an effective CPD accredited education programme.

Hosting over 50 professional-led seminars, the lineup includes leading artificial intelligence and machine learning experts such as NHS Englands Dr Minai Bakhai, Faculty of Clinical Informatics Professor Jeremy Wyatt, and Professor Claudia Pagliari from the University of Edinburgh.

Other speakers in the seminar programme come from leading organisations such as the University of Oxford, Kings College London, and the School of Medicine at the University of Nottingham.

The event all takes place at the National Exhibition Centre, Birmingham on the 17th and 18th March 2020. Tickets to the AI and Machine Learning are free and gains you access to the other seven shows within MediWeek.

Health Europa is proud to be partners with the AI and Machine Learning Convention, click here to get your tickets.

Do you want the latest news and updates from Health Europa? Click here to subscribe to all the latest updates and stay connected with us here.

See the original post:
How is AI and machine learning benefiting the healthcare industry? - Health Europa

Researchers have been working to make mind-controlled prosthetics a reality for at least a decade. In theory, an artificial hand that amputees could control with their mind could restore their ability to carry out all sorts of daily tasks, and dramatically improve their standard of living.

However, until now scientists have faced a major barrier: they havent been able to access nerve signals that are strong or stable enough to send to the bionic limb. Although its possible to get this sort of signal using a brain-machine interface, the procedure to implant one is invasive and costly. And the nerve signals carried by the peripheral nerves that fan out from the brain and spinal cord are too small.

A new implant gets around this problem by using machine learning to amplify these signals. A study, published in Science Translational Medicine today, found that it worked for four amputees for almost a year. It gave them fine control of their prosthetic hands and let them pick up miniature play bricks, grasp items like soda cans, and play Rock, Paper, Scissors.

Sign up for The Algorithm artificial intelligence, demystified

Its the first time researchers have recorded millivolt signals from a nervefar stronger than any previous study.

The strength of this signal allowed the researchers to train algorithms to translate them into movements. The first time we switched it on, it worked immediately, says Paul Cederna, a biomechanics professor at the University of Michigan, who co-led the study. There was no gap between thought and movement.

The procedure for the implant requires one of the amputees peripheral nerves to be cut and stitched up to the muscle. The site heals, developing nerves and blood vessels over three months. Electrodes are then implanted into these sites, allowing a nerve signal to be recorded and passed on to a prosthetic hand in real time. The signals are turned into movements using machine-learning algorithms (the same types that are used for brain-machine interfaces).

Amputees wearing the prosthetic hand were able to control each individual finger and swivel their thumbs, regardless of how recently they had lost their limb. Their nerve signals were recorded for a few minutes to calibrate the algorithms to their individual signals, but after that each implant worked straight away, without any need to recalibrate during the 300 days of testing, according to study co-leader Cynthia Chestek, an associate professor in biomedical engineering at the University of Michigan.

Its just a proof-of-concept study, so it requires further testing to validate the results. The researchers are recruiting amputees for an ongoing clinical trial, funded by DARPA and the National Institutes of Health.

See the original post:
An implant uses machine learning to give amputees control over prosthetic hands - MIT Technology Review

This is the final, part 4 of a four-part series that breaks up a talk that I gave at the Toronto AI Meetup. Part 1, Part 2 and Part 3 were all about the foundations of machine learning, optimization, models, and even machine learning in the cloud. In this video I show an actual machine learning problem (see the GitHub repo for the code) that does the important job of distinguishing between tacos and burritos (an important problem to be sure). The primary concepts included is MLOps both on the machine learning side as well as the deliver side in Azure Machine Learning and Azure DevOps respectively.

Hope you enjoy the final of the series, Part 4! As always feel free to send any feedback or add any comments below if you have any questions. If you would like to see more of this style of content let me know!

The AI Show's Favorite links:

The rest is here:
Tying everything together Solving a Machine Learning problem in the Cloud (Part 4 of 4) - Microsoft - Channel 9

Everywhere you look there's another article written about machine learning and automation. You understand the concepts but aren't sure how it applies to your day-to-day job.

If you work with Dynamics 365 Finance and Operations or AX in a Finance or Accounts Payable role, you probably say to yourself, Theres gotta be a better way to do this. But with your limited time and resources, the prospect of modernizing your AP processes seems unrealistic right now.

If this describes you, then dont sweat! Weve done all the legwork to bring machine learning to AP and specifically for companies using Dynamics 365 or AX.

Join us to learn about:

To learn about our findings, join us on Wednesday March 25th at any of three times for our "Improving your Accounts Payable Process with Machine Learning" webinar.

Go here to read the rest:
Improving your Accounts Payable Process with Machine Learning in D365 FO and AX - MSDynamicsWorld.com

Research Will Include Artificial Intelligence and Machine Learning Approaches as well as Fundamental Theory & Simulation

WASHINGTON, D.C.Today, theU.S. Department of Energy (DOE)announced a plan to provide $30 million for new research on fusion energy.

This funding will provide $17 million for research focused specifically on artificial intelligence (AI) and machine learning (ML) approaches for prediction of key plasma phenomena, management of facility operations, and accelerated discovery through data science, among other topics.

An additional $13 million under a separate funding opportunity will be devoted to fundamental fusion theory research, including computer modeling and simulation, focused on factors affecting the behavior of hot plasmas confined by magnetic fields in fusion reactors.

Recent advancements in Artificial Intelligence and Machine Learning technologies can bring new, transformative approaches to tackling fusion energy theories and challenges, said Secretary of Energy Dan Brouillette. The research funded under these initiatives will be integral to overcoming important barriers to the development of fusion as a practical energy source.

By allocating $30 million towards fusion energy, the Department of Energy is continuing its commitment to advance scientific research and U.S. global competitiveness, said Under Secretary for Science Paul Dabbar. This funding only emphasizes our support for artificial intelligence and machine learning capabilities.

Applications for the AI/ML funding are open to national laboratories, universities, nonprofits, and private sector companies, working either singly or with multiple institutional partners.Total funding planned for the program is $17 million for projects of two to three years in duration, with $7 million available in FY 2020 and outyear funding contingent on congressional appropriations.

Applications for the theory funding are open to universities, nonprofits, and private sector companies. Funding is expected to be in the form of three-year grants. Total planned funding will be up to $13 million over three years, with up to $7 million available in FY 2020 and outyear funding contingent on congressional appropriations.

The two separate DOE Funding Opportunity Announcements, along with a companion national laboratory call for the AI/ML research, are to be found on thefunding opportunities pageof the Office of Fusion Energy Sciences within the Departments Office of Science.

###

View original post here:
Department of Energy Announces $30 Million for New Research on Fusion Energy | Department - Energy.gov

Will machines rise against us?

Sarah Ryerson, President of TMX Datalinx, is certain we dont need to worry about that. And its safe to say we can trust her opinion with data being her specialty, as well as having spent five years at Google before joining TMX.

She applies her experience on Bay Street by helping traders, investors and analysts mine the daily avalanche of data that pours out of TMX every day.

If information is power what will we be doing with data in the future?

Ryerson has the answer, explaining that we will be mining data for patterns and signals that will help us draw new insights and allow us to make better investment decisions.

Ryerson is bringing real-time, historical and alternative data together for TMX clients. Its all about picking up the signals and patterns that the combined data set that will deliver.

She also affirms that she is aiming to make this information more accessible. This will be done through platforms where investors can do their own analysis via easy-to-use distribution channels where they can get the data they want through customized queries. Ryerson notes, Machine learning came into its own because we now have the computing power and available data for that iterate and learn opportunity.

Ryerson knows that for savvy investors to get ahead of algorithms, machine learning or artificial intelligence (AI), they need more than buy-and-sell data. This could be weather data, pricing data, sentiment data from social media or alternative data. When you combine techniques to the vast amounts of data we have thats where we can derive new insights from combinations of data we havent been able to analyze before.

One of the most important elements of AI that data scientists realize is that algorithms cant be black boxes. The analysts and investors using them need transparency to understand why an algorithm is advising to buy, sell or hold.

Looking further into the future, Ryerson believes, We will be seeing more data and better investment decisions because of the insights were getting from a combined set of data.

Thats a lot of data to dissect!

See more here:
Machine learning and the power of big data can help achieve stronger investment decisions - BNNBloomberg.ca