Monthly Archives: April 2021

Artificial Intelligence

A Citizens Guide to Artificial Intelligence, by a cast of thousands (John Zerilli, John Danaher, James Maclaurin, Colin Gavaghan, Alistair Knott, Joy Liddicoat, and Merel Noorman) is a nice high level view of some of the issues surrounding the adoption of artificial intelligence (AI). The author bios describe them as all lawyers and philosophers except for Noorman, and with that crowd its no surprise the book is much better at discussing the higher level impacts than AI itself. Luckily, theres a whole lot more of the latter than there is the former. The real issue is theyre better at explaining things than at coming to logical conclusions. Well get to that, but its still a useful read.

The issue about understanding of AI is shown early, when they first give a nice explanation of false positives and false negatives, but then write Its hard to measure the performance of unsupervised learning systems because they dont have a specific task. As this column has repeatedly mentioned, the key use of unsupervised learning is the task of detecting anomalous behavior, especially when anomalies are sparse. The difference between supervised and unsupervised learning is in knowing what youre looking for:

Supervised: Hey, heres attack XYZ!

Unsupervised learning: Hey, heres this weird thing that might be an attack!

So skim chapter one to get to the good stuff. Chapter two is about transparency, and Figure 2.1 is a nice little graphic about the types of transparency they are describing. What I really like is that accessibility is in the top tier. It doesnt matter if the designers and owners of a system are claiming to be responsible and are also inspecting the results to check accuracy; if the information isnt accessible to all parties involved in and impacted by the AI system, theres a problem.

The one issue I have with the transparency chapter is in the section human explanatory standards. They seem to be claiming that since were hard to understand, why should we expect better from AI systems? They state, A crucial premise of this chapter has been that standards of transparency should be applied consistently, regardless of whether were dealing with humans or machines. Yes, a silly premise. We didnt create ourselves. Were building AI systems for the same reasons weve built other thing in order to do things easier or more accurately than we can do them. Since were building the system, we should expect to be able to require more transparency to be built into a system.

The next three chapters are on bias, responsibility & liability, and control. They are good overviews of those issue. The control chapter is intriguing because its not just about us controlling the systems, but also covers issues about giving up control to systems.

Privacy is a critical issue, and chapter six is nice coverage of that. The most interesting section is on inferred data. We talk about inference engines, making inferences on the data; but the extension of that to privacy is to say there might be ethical limits to what engines should be allowed to infer. Theres the old case of a system knowing a young woman is pregnant and sending pregnancy sales pitches to her home before she had told her parents, but there are far worse situations. Consider societies that are intolerant of sexual orientation, but that can be inferred from other data. A government could use that to persecute people. Theres a wide spectrum in between those examples, and the chapter does a nice job of getting people to think about the issue.

The next chapter covers autonomy and makes some very good points. One is that humans have always challenged each others autonomy, but that AI and lack of laws and regulations make it far easier for governments and a few companies to remove our autonomy in much more opaque ways than have previously been available.

Algorithms in government and employment are given a good introduction in the next chapters, but with a lot of the same information seen elsewhere. The most interesting part of the back portion of the book comes in chapter ten, about oversight and regulation. There is a suggestion that, given the complexity of AI, there is logic to creating a new oversight agency for the national government. As they point out, an FDA for AI. Think of it in business terms, its a center of excellence in AI, able to formulate national policy for business and citizens, while also serving to help other agencies adapt the general policies to their specific oversight areas. That makes excellent sense.

No book is perfect, but Im partially surprised that a book with so many authors attached flows as well. Then I remember they all are academics, used to research papers with multiple authors. Of course, with that many academics, the risk is always that a book will sound like a research paper. Fortunately, they seem to have escaped that problem. A Citizens Guide is a good read to help people understand key issues in having AI make the major impact on society that it will. More people need to realize that quickly and get governments to focus on protecting people.

Link:

A Citizens Guide To Artificial Intelligence: A Nice Focus On The Societal Impact Of AI - Forbes

The Institute for Protein Design (IPD) at the UW announced March 31 a $5 million grant from Microsoft to collaborate on applying artificial intelligence to protein design.

Microsofts chief scientific officer Eric Horvitz and the IPDs director David Baker, in an article with GeekWire, said they believe that this collaboration will lead to major strides in medicine and technology, and accelerate the scientific response to future pandemics.

The IPD designs proteins molecules that carry out a wide range of functions from defending against pathogens to harnessing energy during photosynthesis from scratch, with the goal of making a whole new world of synthetic proteins to address modern challenges, according to the institutes website.

Researchers at the IPD have developed promising anti-viral and ultra-potent vaccine candidates against SARS-CoV-2, the virus that causes COVID-19, that are currently in human clinical trials.

And in protein design, form follows function.

We use 3D protein structures on the computer to design the protein sequences, Brian Coventry, a research scientist in the Baker Lab at the IPD, said. When we order the protein sequence, its function in real life should exactly mirror that on the computer.

But that does not always happen.

The problem with this method, which is based on the first principles of both physics and chemistry, is that it produces an abundance of possible proteins which must be tested, the majority of which do not have the exact desired form, Coventry said.

Coventry recently worked on a team that developed a SARS-CoV-2 antiviral medication candidate, and he stressed that for antivirals, it is important that the designed protein be precisely atomically correct.

In the context of a pandemic, the fast development of highly accurate therapeutic synthetic proteins is desirable. This is where deep learning, a subset of artificial intelligence modeled after the brains neural networks, comes into play.

There is a lot of room for improvement, Minkyung Baek, a postdoctoral scholar in the Baker Lab at the IPD, said about the first principles-based method of protein design. Baek believes that deep learning methods can be used to quickly discriminate between possible proteins and optimize design to produce proteins that are more stable and bind more tightly to targets.

Deep learning models are given a training data set, in this case experimental results of the structures of designed proteins, and then can learn based on real-world data. They use that information to predict and design protein structures, Baek said.

Microsoft has given the IPD access to their cloud computing service Azure, which will enable them to train and test deep learning models about 10 times faster, according to Baek.

Baek hopes that this will speed up the development of effective deep learning models, which will be helpful not only for designing proteins that match existing biological proteins, but also for discovering the structure of naturally occurring proteins.

There are many real-world situations where the structure of the target is not precisely known. In these situations, researchers must predict the shape of the metaphorical lock and design the key simultaneously.

Being able to better predict the structure of a protein when given its genetic code is important, with Baek using the variants of the COVID-19 virus as an example.

Using our deep learning base, we can predict the protein structure of the variant, and starting from there we may get some clue [about] why that variant may have been more severe or easy to spread, Baek said.

But these deep learning models have some limitations. They are limited by the available training data set, are not always generalizable to multiple situations, and do not explain the reasoning behind their decisions, Coventry said.

Despite these factors, Coventry and Baek are both optimistic about the potential for deep learning to improve the protein design process.

At the end of the day, Id like to see a 100% success rate, you know, Coventry said. Someday Im sure its possible.

Reach reporter Nuria Alina Chandra at news@dailyuw.com. Twitter: @AlinaChandra

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Proteins, artificial intelligence, and future of pandemic responses - Dailyuw

Matrix style graphics are seen on an Apple iPhone in this photo illustration on January 22, 2019. (Photo by Jaap Arriens/NurPhoto via Getty Images)

Have you ever wondered how Netflix knows the perfect show to recommend next? Or how Facebook suggests just the person you were looking to follow?

Researchers have found the power of those predicting algorithms can also "predict" the biological language of cancer and neurodegenerative diseases like Alzheimers - which could help change completely what researchers know about treating and preventing such diseases.

The study, done at St. Johns College, University of Cambridge, found the artificial intelligence technology could "crack the language" of such diseases. The study was published in the scientific journal PNAS.

The findings could be used in the future to "correct the grammatical mistakes inside cells that cause disease," a news release from the college stated.

One in three people will be diagnosed with cancer in their lifetime, according to the American Cancer Society. And one in three older adults dies with Alzheimers or another dementia, according to the Alzheimers Association. Other neurodegenerative diseases, like Parkinsons and Huntingtons, affect millions worldwide.

Researchers say this breakthrough in technology could lead to learning how to help alleviate symptoms - and maybe even prevent the disease from happening at all.

"Bringing machine-learning technology into research into neurodegenerative diseases and cancer is an absolute game-changer," said Professor Tuomas Knowles, lead author of the paper and a Fellow at St Johns College. "Ultimately, the aim will be to use artificial intelligence to develop targeted drugs to dramatically ease symptoms or to prevent dementia happening at all."

A combination of physical activity, not smoking, light alcohol consumption, a good diet and cognitive activities may help lower the risk of the disease by as much as 60 percent, a study found.

Technology users encounter a variety of algorithms daily. The algorithms use "powerful machine-learning" data to "make highly educated guesses about what people will do next," as St. Johns College explained it. Thats how Netflix and other streaming services can offer you the best recommendations.

Dr. Kadi Liis Saar, first author of the paper and a research fellow at St Johns College, used similar machine-learning technology to train a large-scale language model to look at what happens when something goes wrong with proteins inside the body to cause disease, the college wrote.

Further use of machine-learning could transform future cancer and neurodegenerative disease research. Discoveries could be made with machine-learning beyond what scientists already know - and potentially even beyond what the human brain could ever understand, the college said.

Excerpt from:

Artificial intelligence, like algorithms, could crack the language of cancer and Alzheimers, study finds - FOX 2 Detroit

For some, artificial intelligence and data science are fantastic technologies that will benefit people and society. For others, theyre terrifying assaults on individual privacy and dire threats to human existence. The point is, these latest innovations are evolving fast in the hotbeds of business, science and government, and it can be difficult for regular citizens and civil society to keep up, particularly nonprofits and others involved in addressing the spectrum of societys needs. What is philanthropys role in the growth, use and regulation of these powerful and protean technologies?

These are some of the questions Vilas Dhar considers in his role as president of the Patrick J. McGovern Foundation, a relatively young grantmaker whose late founder built a fortune in publishing and industry research, tracking the expanding computer industry in the late 20th and early 21st centuries. McGoverns International Data Group published a number of popular computer industry magazines such as Computerworld, PC World and InfoWorld.

McGovern the person was a believer in technologys potential to improve society and the human condition. He is remembered for some notable philanthropic moves involving science and technology, including a $350 million pledge in 2000 that established the McGovern Institute for Brain Research at MIT.

McGovern the foundationestablished in 2015, a year after the death of its namesakeis also active in the information technology world. The foundation has so far made about $295 million in grants, in areas like tech education, climate change, digital health and pandemic response, as well as data science and AI ethics.

Now, in a move common in business but rare in the philanthropy and nonprofit world, the McGovern Foundation has augmented its powers through a high-profile merger. It recently announced that the Cloudera Foundationa philanthropy created by Silicon Valley data and AI software company Cloudera Inc. to bring data analytics technology to the nonprofit sectorhas merged its $9 million endowment, staff and operations into McGovern.

Dhar says the merger with Cloudera creates an organization thats neither exclusively a philanthropic foundation nor a technology company. Its a hybrid that says were an impact-driven organization that will pull from the private sector when we need to, will pull from technology companies when we need to, and will pull from the long history of philanthropy in this country to build something that actually drives outcomes for people, he said. To me, thats the direction of where the field is already going and should be going.

Most often, grantseekers just require cash to maintain or expand services, pay employees and to keep the lights on. But when it comes to a novel and developing field like data science, it can pay to have a funding partner with the experience to envision potential solutions and the hands-on expertise to design those solutions. Toward that end, the newly expanded McGovern Foundation plans to be something of a technology consulting group for philanthropy and nonprofits.

Claudia Juech, the now-former CEO of Cloudera Foundation, will have a central role in the new hybrid organization, directing activities around data enablement for nonprofits as the head of its new Data and Society program. According to Juech, McGoverns approach will involve resourcing the field as nonprofits seek new ways to apply data science to their work. While creating solutions for specific nonprofits will be part of the job, more central to the mission going forward will be creating tools to let nonprofits everywhere access new technology. We can only work with so many organizations, she said.

McGoverns Data and Society team will create and share a portfolio of solutions to serve as practical examples of whats possible in the field of data and AI for social change, guided by equity principles and the ethical use of data. The bigger question, Juech said, is how can we make this accessible to the broader sector?

What are some possibilities for nonprofits as they delve into these new data and AI applications? As in business, one potential area is predictive tools that let organizations better plan and prepare for future problems and needs. Its evolving, Juech said. A lot of nonprofits are using data science to look backward, to understand what happened. But what is possible these days is to see more of what could happen. For example, the Cloudera Foundation helped Womens World Banking create tools to predict the future of womens financial inclusion and empowerment in emerging markets. Another grantee is using data to forecast malaria outbreaks in West Africa.

Of course, artificial intelligence and data science are hot-button issues these days, with many observers voicing unease about potential dangers, including racial and algorithmic biases or the loss of privacy. This is no theoretical worry. One of the most widespread applications of AI affects nearly everyone in the U.S. and billions around the worldthat is, social media companies use of algorithms to populate individual newsfeeds, which has contributed to political polarization, volatility and even violence in the U.S. and abroad.

Those concerns have not escaped Dhar. Though hes a self-described tech optimist, he nevertheless believes philanthropy must keep potential pitfalls front and center, and that nonprofits and the people they serve must be part of the conversationrather than leaving it all up to tech companies and government. The answer isnt to get rid of one or get rid of the other, he said. Its to let civil society be the ones who are coming into that conversation and promoting all of our best interests.

Excerpt from:

Artificial Intelligence and Data Science Are Top of Mind as These Two Grantmakers Join Forces Inside Philanthropy - Inside Philanthropy

ByPelion Press Team

Today,Pelion, theConnected IoT Device service provider, and subsidiary of Arm, jointly announced a partnership withJohnson Controls(NYSE: JCI), the global leader for smart, healthy and sustainable buildings. This partnership will accelerate innovation in connectivity, security and intelligence at the edge for Johnson Controls OpenBlue technology.

This partnership combines Johnson Controls deep domain expertise in healthy buildings with Pelions device and edge management capabilities to usher in an era of truly smart, updatable facilities at cloud scale. OpenBlues AI capabilities at the edge will consolidate diverse points of intelligence distributed across various floors, sites or even continents into insights and actions, creating an updatable building that can self-heal and evolve over its lifespan.

This innovation mirrors the automotive sector, where software, multiple sensors and AI-trained models have transformed the industry by enabling autonomous driving and software updates that blend data to continually improve vehicle capabilities and experience. Johnson Controls is applying the concept to the built environment. They will leverage Pelions flexible device management capabilities to unite diverse device types and application layers to feed AI models that respond to dynamic workloads.

Johnson Controls has the strategic foresight to rely on a partner to streamline the complexity of IoT device management security and secure firmware updates over the air. Pelions connected device platform will standardize the onboarding process for all systems, including the edge and endpoint devices that run on them, plus offer world-class public key infrastructure for secure and simple integration with third-parties.

This secure, open and flexible approach to device management will allow OpenBlue to run any device and hardware configuration, from hardware gateways to constrained temperature sensors.

In order to provide sustainable, low cost and low power intelligent processing at the edge, the partnership will utilize proven energy-efficient processors from Pelions parent company, Arm, which are a key part of Johnson Controls distributed hardware deployment.

Read more about the future of smart, healthy, and sustainable buildings in this blog.

Excerpt from:

Johnson Controls and Pelion Partner on Artificial Intelligence / Internet of Things (AIoT) For Smart, Healthy, and Sustainable Buildings - IoT For All

DUBLIN, April 12, 2021 /PRNewswire/ --Cosmo Pharmaceuticals NV (SIX: COPN) today announced the FDA approval of GI Geniusintelligent endoscopy system, its revolutionary device for lesion detection during colonoscopy.

The GI Genius module FDA approval marks a pivotal milestone for Cosmo after more than 10 years of research and investments focused to generate disruptive innovations in the field of colon's disease and optimization of the colonoscopy procedure. The development of GI Genius intelligent endoscopy module has been possible thanks to the leadership position of Cosmo, its unique proprietary library of high-definition loss-less videos of colonoscopies and its proprietary software and algorithms. The device is the first of its kind to obtain the FDA approval through the De Novo application. The device operates in real time to assist the endoscopist in the detection of lesions, is very simple to use and is compatible with all endoscopes. Cosmo is the sole manufacturer. Medtronic is the exclusive world-wide distributor.

According to Prof. Michael Wallace, Fred C. Andersen Professor of Medicine at Mayo Clinic and Editor in Chief of Gastrointestinal Endoscopy:"While colon cancer is the second deadliest cancer worldwide, it is the most preventable cancer yet full prevention remains unfulfilled. 1 in 20 US adult will be diagnosed with this disease in their lifetime but, encouragingly, 90% of patients can beat it if it is diagnosed early enough. Colonoscopy is the gold standard and most common screening method, but it's not perfect, as performance varies based on many factors including physician skill level. The GI Genius intelligent endoscopy module will help improve the accuracy of colonoscopy and reduce the number of undetected precancerous lesions, as has been shown in a recent randomized study (Repici et al.), where detection rates improved very significantly with GI Genius technology versus standard colonoscopy, regardless of skill level or endoscope used."

Mauro Ajani, Chairman of Cosmo, commented:"This landmark approval is tremendous news for Cosmo. The first ever approval of an artificial intelligence device for lesion detection in colonoscopies further strengthens Cosmo's commitment to fight colorectal cancer. This approval is a major milestone after many years of strategic investments into the colon diseases and positions Cosmo at the forefront of cutting-edge innovation. Through the strong partnership with Medtronic over the last two years, Cosmo has expanded its position in the medical device market within its core GI expertise, has gained global access and will ultimately reach more patients. We are extremely pleased and very optimistic about the impact the GI Genius intelligent endoscopy module will have on the US patient population undergoing colonoscopy."

Alessandro Della Ch, CEO of Cosmo, said:"This approval is gratifying in many ways as it is aligned with our mission of serving markets with unmet needs. Through our global distribution partnership with Medtronic we are excited to pursue an opportunity which is worth at least $ 1.1bn, looking only at the opportunity for artificial intelligence in the colonoscopy market. Leveraging on the strength of Medtronic's large US commercial footprint, we look forward to successfully and rapidly developing this market."

The GI Geniusintelligent endoscopy system is a registered trademark of Medtronic plc.

The FDA announcement of the approval can be found at the following link: https://www.fda.gov/news-events/press-announcements/fda-authorizes-marketing-first-device-uses-artificial-intelligence-help-detect-potential-signs-colon.

About Cosmo Pharmaceuticals

Cosmo is a specialty pharmaceutical company focused on developing and commercialising products to treat selected gastrointestinal disorders and improve endoscopy quality measures through aiding the detection of colonic lesions. Cosmo has also developed medical devices for endoscopy and has recently entered into a partnership with Medtronic for the global distribution of GI Genius its artificial intelligence device for use in coloscopies and GI procedures. Cosmo has licensed Aemcoloto Red Hill Biopharma Ltd. for the US and has licensed Relafalkto Dr. Falk Gmbh for the EU and other countries. For additional information on Cosmo and its products please visit the Company's website: http://www.cosmopharma.com.

Calendar Annual General Meeting, Amsterdam - May 28, 2021 Half-Year 2021 Report -July 30, 2021

Disclaimer

Some of the information contained in this press release contains forward-looking statements. Readers are cautioned that any such forward-looking statements are not guarantees of future performance and involve risks and uncertainties, and that actual results may differ materially from those in the forward-looking statements as a result of various factors. Cosmo undertakes no obligation to publicly update or revise any forward-looking statements.

This communication is not an offer of securities of any issuer. Securities may not be offered or sold in the United States absent registration or an exemption from the registration requirement of the US Securities Act of 1933.

This press release constitutes neither an offer to sell nor a solicitation to buy securities and it does not constitute a prospectus within the meaning of article 652a and/or 1156 of the Swiss Code of Obligations or a listing prospectus within the meaning of the listing rules of the SIX Swiss Exchange or any similar document. The offer will be made solely by means of, and on the basis of, a securities prospectus to be published. An investment decision regarding the securities to be publicly offered should only be made on the basis of the securities prospectus.

This press release is made to and directed only at (i) persons outside the United Kingdom, (ii) investment professionals falling within Article 19(5) of the Financial Services and Markets Act 2000 (Financial Promotion) Order 2005 (the "Order"), and (iii) high net worth individuals, and other persons to whom it may lawfully be communicated, falling within Article 49(2)(a) to (d) of the Order. Any person who is not a relevant person should not act or rely on this press release or any of its contents.

This press release does not constitute an "offer of securities to the public" within the meaning of Directive 2003/71/EC of the European Union (the "Prospectus Directive") of the securities referred to in it (the "Securities") in any member state of the European Economic Area (the "EEA"). Any offers of the Securities to persons in the EEA will be made pursuant to an exemption under the Prospectus Directive, as implemented in member states of the EEA, from the requirement to produce a prospectus for offers of the Securities.

Logo - https://mma.prnewswire.com/media/1459450/Cosmo_Pharmaceuticals_Logo.jpg

Contact: Niall Donnelly, CFO & Head of Investor Relations Cosmo Pharmaceuticals N.V.Tel.: +353 1 817 03 70 [emailprotected]

SOURCE Cosmo Pharmaceuticals N.V.

https://www.cosmopharma.com

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Cosmo announces FDA approval of GI Genius intelligent endoscopy module, its revolutionary Artificial Intelligence device for lesion detection during...

LinkedInGuru, founded by Canadian entrepreneur Aaron Vasant, helps job seekers thrive in the highly competitive job market using AI advancement and modern design strategies.

TORONTO(BUSINESS WIRE)The Guru team is making waves in the resume writing industry through integrating artificial intelligence and design into their process to optimize the credibility of clients resumes and increase their chances of landing an interview.

Aaron, Founder and CEO of LinkedInGuru, explains, With the introduction of applicant tracking systems and keyword scanners, the front end or early stages of the recruitment process is now almost never done by the human eye. Only nine Fortune 500 companies dont use bots or an ATS system to review resumes before passing them on.

ATS can scan and filter those who possess specific keywords and skills using data-driven recruitment software. Quite often, upwards of 75% of applicants do not make it past the initial screening process.

Aaron decided to create the company after making a variety of remarkable discoveries throughout the course of his MBA. His research led him to discover the core functionality behind the applicant tracking systems, which he has used in developing LinkedInGurus strategy and success formula.

If your resume makes it past the bots, eventually, a human will read it. This is where Aaron acknowledged that the content and design must be captivating so they want to learn more about you. You must strike the perfect balance between keywords, design, and content to have the best resume possible.

LinkedInGurus proprietary method has seen a lot of success, citing that 94% of their clients have received an interview within 60 days of purchasing their service. The company is trusted by professionals at Amazon, Tesla, Walmart, Google, CNN, Adidas, Microsoft, and more!

The process is pretty simple, you begin by answering their standardized recruitment questionnaire, then select your favourite ATS friendly template from their proven library, and the draft will be completed within 5-7 business days.

Contacts

Media Contact:

Name: Aaron Vasant

Company: LinkedInGuru

Email: support@linkedinguru.caWebsite: LinkedInGuru.ca

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LinkedInGuru Innovates Resume Writing by Utilizing Artificial Intelligence and Design - TechDecisions

The intersection of between computers, neurotechnologies, and the human brain.

This past week, Elon Musks new venture Neuralink made headlines by showing a video of a monkey playing Pong with his mind, controlled by a surgically implanted wireless device that can directly read brain signals and interpret its intended commands. The technologies that enable such communication between a computer and the brain are called brain-machine interfaces (BMIs).

Brain-machine interfaces - or brain-computer interfaces, the terms are used interchangeably - are technologies designed to directly plug into the nervous system: the brain, retinas in the eyes (which are actually a part of the brain itself), spinal cord, or peripheral nervous system. The Neuralink example and other similar technologies are designed to read and decode neural signals from individual neurons in selected parts of the brain in an attempt to understand the brains outputs. Instead of the outputs going to the arm of a monkey or human controlling a joy stick to play Pong or some other video game, they go to a computer which plays the game instead.

How do they achieve this? Specially designed electrodes are surgically implanted into a target region of the brain where the neural signals need to be recorded. Those signals are then decoded and the intent of the brain interpreted by mathematical models and computer algorithms that take advantage of what is known about how the brain works. Eventually, the commands interpreted by the computer are used to execute desired functions or tasks, such as controlling a robotic arm, generating synthesized speech, or playing video games.

Because surgically implanted BMIs are highly invasive, their use is restricted to restoring clinical function in patients suffering debilitating neurological disorders, in particular motor disorders such as paralysis following spinal cord injury or stroke, locked-in syndrome, and amyotrophic lateral sclerosis (ALS). The impact these technologies can have on the quality of life of these patients and their families cannot be overstated.

Until relatively recently, surgically implantable BMIs necessitated wired connections between the brain and the computer the wires were plugged into. But this has a number of serious disadvantages and risks. The electrodes can move in unintended ways as mechanical forces are exerted on the wires, and it can lead to a significant risk of infection or other types of injury. More recently though, BMIs implanted in the brain have gone wireless. The entire device is self contained within the skull and brain with no external wires protruding out. They communicate with external computers using various through the air protocols and algorithms in a similar way your Bluetooth and WiFi devices work.

In contrast, non-invasive BMIs are very different from surgically implanted invasive BMIs. Non-invasive BMIs rely on electroencephalography(EEG) and related methods to read and interpret brain waves. They do not require surgically implanted electrodes, but rather external electrodes integrated into form factors a user can wear and take off as needed - like a cap. The video game industry and virtual and augmented reality worlds have a strong interest in non-invasive BMIs, for example. These market segments are one of the main economic drivers for research in this area. Unfortunately though, the resolution and quality of measured brain signals these non-invasive methods provide are generally not sufficient for the needs demanded by clinical applications.

The earliest work using EEG to measure and attempt to make sense of brain signals is over 100 years old, dating back to the 1920s. And the engineering accomplishments behind the press Neuralink has been receiving lately is grounded in years of pioneering work by a number of research groups from around the world. In 2012 researchers from Brown University in Providence, Rhode Island, along with colleagues at Massachusetts General Hospital and Harvard University in Boston, and the Institute of Robotics and Mechatronics in Germany, showed that a wired BMI could successfully be used in human patients with tetraplegia - a severe form of paralysis in all four limbs - to control a robotic arm to drink, and to control a computer screen to read email.

This effort is part of the BrainGate project, a collaborative effort between Brown University, Case Western University, Massachusetts General Hospital, Stanford University, and the Department of Veteran Affairs. In their most recent work, published just a few days ago, the team introduced a wireless version in humans of a previous wired prototype. The patients were able to surf the web and use other apps on a commercially available tablet computer.

In some of the earliest work in the field, researchers at Duke University in 2014 were able to wirelessly record from 1800 distinct neurons in the brains of freely moving monkeys for nearly five years. And in 2016 the same group showed that monkeys implanted with their wireless BMI could use the system to continuously manipulate and drive a wheel chair.

And now, converging with advances taking place in machine learning, BMIs are on the verge of entirely new capabilities.

There is a tremendous amount of engineering that goes into developing BMIs. State of the art micro- and nano-fabrication, mathematical and computer modeling, extensive neurobiological experiments, pre-clinical testing in animal models, and clinical testing in humans all need to take place. Because of the up-front complexity and effort required in building these devices, once it is built and tested the design and engineering details are pretty much fixed. This means that the functionality of the BMI, what it can do and how it operates, is by necessity also fixed and limited to the constraints imposed by its design specifications.

The problem, however, is that the requirements and needs of different patients will vary to significant degrees from individual to individual. Even for patients diagnosed with the same disorder, how the parameters of a BMI are fine tuned may need to be different in order to achieve optimized performance tailored to the individual. For example, how many neurons to record from and how decoding algorithms should interpret changes in recorded signals. And equally, if not more significant, the needs of the individual patient themselves will change and evolve over time as disease progresses or even over time as a normal part of aging.

Even more challenging, how a BMI needs to interact with the brain may vary on relatively short and highly dynamic, i.e. changing, time scales within the course of minutes or hours. For example, depending on the physical nature of an activity a patient is engaged in, or the degree of an intellectual demand associated with a specific task, the BMI may need to quickly adapt. What the brain needs to do to change the channel on the TV is very different than what it needs to do if it is playing a difficult video game, for example.

Even the time of day and cognitive state of the individual may have an effect on the demands put on a BMI. Are you trying to focus on a task late in the evening when you are tired? Or is it the morning and you are fresh and ready to go?

In short: a one-size-fits-all BMI cannot be truly optimized to the needs of an individual patient after it is surgically implanted.

Some BMI technologies already incorporate physiological feedback or patient input to adjust their outputs and functions. But in general, human interaction is needed, such as subjective or perceptual feedback from the patient, or manually adjusting parameters by a doctor. The integration of state of the art machine learning to achieve optimized near real-time functionality in BMIs - in other words, adaptive and autonomous smart BMIs - is still in its earliest stages.

With the integration of machine learning, BMIs may one day be able to learn and anticipate the contextual needs of situations a patient finds themselves in. Such BMIs will be able to adjust their outputs and functions in near real-time to accommodate changing cognitive and physical demands. Or be able to apply what they learn in one scenario and under a specific set of conditions to the needs of the patient under a different set of conditions in a new scenario. All without necessitating interpretation or involvement from a human.

For sure, there are many open questions and engineering challenges to be solved before this becomes really possible. For example, demands on computing power and where on the hardware in the patient or cloud any machine learning will take place have to be considered. This is particularly serious in this case because what happens if the BMI needs an internet connection to function properly, but the patient finds themselves in an internet dead zone? Other considerations include the need for further optimized algorithm development, and the need for specialized hardware designed to work specifically with advanced algorithms. And the list goes on.

Yet, despite the challenges, real progress is being made. In one study researchers demonstrated a proof of concept wireless BMI system that took advantage of state of the art flexible electronics and convolution neural networks, one of the most successful approaches to machine learning, in order to allow implanted patients to control a wheelchair.

And in another study, researchers used reinforcement learning, another type of machine learning, to optimize the calibration of a BMI while at the same time transferring what the BMI learned in one scenario to exploring new knowledge (a form of learning referred to as transfer learning - because information is transferred to a new situation). There are even textbooks now aimed at machine learning and artificial intelligence applications to BMIs.

In the end, one day, future patients that need BMIs, as well as their families and loved ones, will be the ultimate beneficiaries of these technologies and the confluence of efforts by thousands of scientists, engineers, and doctors. And that is a hope worth collectively pursuing.

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How Machine Learning Will Enable Technologies That Anticipate What The Brain Thinks - Forbes