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Best Permission Practices Sought for Data Collection in AI Research




AI researchers in healthcare are more cognizant of whether the data they need was collected in accordance with the best permission practices. (GETTY IMAGES)

By John P. Desmond, AI Trends Editor

AI researchers in healthcare are refining ways to ensure data they work with has been obtained with proper permissions, including from patients.

This becomes more challenging as smartphone apps asking for medical information become more popular, and consumers may click through agreement pages without of course reading the fine print.

Google for example has built its portfolio primarily on a 15-to-35-year-old consumer market, and now wants to consider targeting an older demographic. “Now they just want to go out to the retirement communities and start collecting data from residents to figure out how they can pitch their product to that demographic,” stated Camille Nebeker, an associate professor at the UC San Diego medical school, in a recent account in Bloomberg Law.

But how the tech companies have historically collected information and what data researchers need for studies, can be disconnected. Nebeker has studied data from AliveCor’s Kardia device that detects abnormal heartbeats, to improve the health of aging patients. The data was collected in a way that meets the requirements for studying human subjects, known as the Common Rule 45 C.F.R. 46).

Large datasets are needed to train machine learning models. Getting clearance to use the data in a research center can be complicated. “The concern is that the data are being used without the originators of the content agreeing to the use,” stated Susan Gregurick, associate director for data science and director of the Office of Data Science Strategy at the National Institutes of Health, to Bloomberg.

Beware of Unexpected Risks in Data Science/AI Research

An exploratory workshop on Privacy and Health Research in a Data-Driven World was recently held by the Office for Human Research Protections (OHRP), a unit of the federal agency HHS. Dr. Jerry Menikoff, director of the OHRP, presented on “Unexpected Forms of Risk in Data Science/Artificial Intelligence Research.”

He described the experience between Cambridge Analytica and Facebook, in which data originated from Facebook users who thought they were taking a personality quiz, and wound up entered into a database along with data on all their Facebook friends, that was sold to political campaigns in efforts to influence voters. “ No academic research was ever published as a result of this research,” Dr. Menikoff noted.

The experience prompted Dr. Menikoff to produce a list of “hallmarks of a research ethics scandal,” things for practitioners of ethical research to watch out for:

  • Metrics jumping between domains, e.g., psychiatry to social media profiles to electoral data,
  • Research that is exempt under Common Rule for narrow technical reasons,
  • Blurred lines between academic and commercial research,
  • Use of Application Program Interface (API) tools intended for commercial and advertising purposes to gather data for academic research,
  • Abuse of mTurk workers (workers accessed through an Amazon crowdsourcing mechanism), ● Deceptive/opaque recruiting tactics for human subjects – a strong signal of unethical research,
  • Predictive population models as research output become tools for intervention in individual lives, and
  • Downstream effects nearly impossible to imagine because the models are highly portable and far more valuable than the actual data.

Working Group on AI Seeks to Bridge Computer Science and Biomedical Research

The NIH of HHS has a working group on AI charged to build a bridge between the computer science and biomedical communities; to generate training that combines the two subjects for research; to understand career paths in the new AI economy may look different; to identify the major ethical considerations, and to make suggestions. Their AI Working Group Update was issued in December 2019.

Among the group’s recommendations: support flagship data generation efforts; publish criteria for ML-friendly datasets; design and apply “datasheets” and “model cards” for biomedical ML; develop and publish consent and data access standards for biomedical ML; and publish ethical principles for the use of ML in biomedicine.

The direction in data collection for AI research is away from scandal and towards best practices.

Read the source articles in  Bloomberg Law, information on Common Rule 45 C.F.R. 46), the account in Privacy and Health Research in a Data-Driven World and the AI Working Group Update from the NIH unit.



Man paralyzed from neck down uses AI brain implants to write out text messages




Video A combination of brain implants and a neural network helped a 65-year-old man paralyzed from the neck down type out text messages on a computer at 90 characters per minute, faster than any other known brain-machine interface.

The patient, referred to as T5 in a research paper published [preprint] in Nature on Wednesday, is the first person to test the technology, which was developed by a team of researchers led by America’s Stanford University.

Two widgets were attached to the surface of T5’s brain; the devices featured hundreds of fine electrodes that penetrated about a millimetre into the patient’s gray matter. The test subject was then asked to imagine writing out 572 sentences over the course of three days. These text passages contained all the letters of the alphabet as well as punctuation marks. T5 was asked to represent spaces in between words using the greater than symbol, >.

Signals from the electrodes were then given to a recurrent neural network as input. The model was trained to map each specific reading from T5’s brain to the corresponding character as output. The brain wave patterns recorded from thinking about handwriting the letter ‘a’, for example, were distinct from the ones produced when imagining writing the letter ‘b’. Thus, the software could be trained to associate the signals for ‘a’ with the letter ‘a’, and so on, so that as the patient thought about writing each character in a sentence, the neural net would decode the train of brain signals into the desired characters.

With a data set of 31,472 characters, the machine learning algorithm was able to learn how to decode T5’s brain signals to each character he was trying to write correctly about 94 per cent of the time. The characters were then displayed so he was able to communicate.

Here’s a gentle video explaining the experiment.

Youtube Video

Unfortunately, there’s no delete button in this system; T5 had to push on even if he had made a mistake, such as imagining transcribing the wrong letter or punctuation mark. The character error rate was reduced from six per cent to 3.4 per cent by implementing an auto-correct feature. It’s about as accurate as today’s state-of-the-art speech-to-text systems, the researchers claimed.

It should be noted that the character error rate for free typing, when T5 was not transcribing text given by the researchers, was higher at 8.54 per cent and reduced to 2.25 per cent when an auto-correcting language model was used.

“Together, these results suggest that, even years after paralysis, the neural representation of handwriting in the motor cortex is probably strong enough to be useful for a BCI,” the team wrote, referring to a brain-computer interface. T5 was paralyzed due to a spinal cord injury, but the part of his brain that controls movement is still intact.

John Ngai, director of the US National Institutes of Health’s BRAIN Initiative, who was not directly involved in the research, called the study “an important milestone” for BCIs and machine learning algorithms. “This knowledge is providing a critical foundation for improving the lives of others with neurological injuries and disorders,” he said in a statement. The NIH, a government organization, helped fund the research.

Not a fit for all

Although the study seems promising, the team admitted there are a lot of challenges to overcome before this kind of technology can be commercialized or otherwise used by many more people. First of all, it has only been demonstrated on one person so far. The team will have to, as the tech stands today, retrain their model for each individual’s brain signals, and the performance may not be consistent from patient to patient.

“Why performance varies from person to person is still an unknown question,” Frank Willett, lead author of the study and a research scientist at Stanford’s Neural Prosthetics Translational Laboratory, told The Register.

“One cause is likely that the sensors sometimes record from different numbers of neurons – so sometimes when the sensor is placed into a person’s brain, it is particularly ‘hot’ and records a lot of neurons, while other times it does not. This is an open question in the field, and designing sensors that can always record many neurons is an important goal that others are working on.”

The academics also continuously retrained the system on T5’s brain signals to calibrate the software before they conducted experiments. Willett said that a system used in the real-world would have to work on minimal training data and that users shouldn’t have to retrain the machines every day.

“To translate the technology into a real product, it needs to be streamlined – the user should be able to use the BCI without needing to take too much time to train it,” he said.

“So we need to improve the algorithms so that they can work well with only a little bit of training data. In addition, it should be smart enough to automatically track how neural activity changes over time, so that the user does not have to pause to retrain the system each day.”

To translate the technology into a real product, it needs to be streamlined

The invasive nature of the electrodes is also an ssue; they have to stay implanted in a patient’s brain and will have to be made out of a material that is durable and safe. “Finally, the microelectrode device should be wireless and fully implanted,” Willett added. The software must also be able to run on a desktop computer or smartphone: it’s no good having to lug around heavy custom equipment.

“It is important to recognize that the current system is a proof of concept that a high-performance handwriting BCI is possible (in a single participant); it is not yet a complete, clinically viable system,” the paper concluded.

“More work is needed to demonstrate high performance in additional people, expand the character set (for example, capital letters), enable text editing and deletion, and maintain robustness to changes in neural activity without interrupting the user for decoder retraining. More broadly, intracortical microelectrode array technology is still maturing, and requires further demonstrations of longevity, safety and efficacy before widespread clinical adoption.” ®

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A Swiss Blockchain-Based Analytical Platform for the Cryptocurrency Market: Meet Dohrnii

A Swiss Blockchain-Based Analytical Platform for the Cryptocurrency Market: Meet Dohrnii

A Swiss Blockchain-Based Analytical Platform for the Cryptocurrency Market: Meet DohrniiA new project has long started exploring the use cases of AI for trading and is set on a mission to become a pioneer in bringing them to the cryptocurrency market. Today, we will take a closer look at how trading has evolved over the years and where the cryptocurrency industry stands less than a

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A new project has long started exploring the use cases of AI for trading and is set on a mission to become a pioneer in bringing them to the cryptocurrency market. Today, we will take a closer look at how trading has evolved over the years and where the cryptocurrency industry stands less than a decade since its inception.

The Development of Trading Tech Over the Years

If you think back to how the cradle of trading Wall Street was operating on the 80s – with DOS-based computers with green numbers and black screens and phones being the pinnacles of technology at the time – it is mindblowing what tools are available today even to those who do not trade professionally. Globalization has shaken the trading industry at its core – since the 1970s, computational algorithms and simulations such as the Monte Carlo method have been evolving, with the new century marking a drastic revolution in their capabilities and application scopes.

New and Emerging Investing Trends

In 1971, the first electronic stock market was launched by NASDAQ. It was a revolutionary concept that was regarded as a major step towards the future of the investing sector. Shortly after in 1980, online trading followed, allowing brokers to communicate with their clients digitally and to facilitate buy and sell orders directly. Then, the internet emerged, allowing everyone to conduct thorough research on companies and new investing opportunities easily accessible at their fingertips.

Parallel to these advancements, trading technology focused on the analysis of the markets was rapidly evolving. Algorithmic trading, which uses programmatic rules to analyze the markets, ultimately giving traders the power to execute orders exponentially quicker and with less bias than human operators are able to, bridged the gap between informational technology and investing, forming a never ending duo (Source: Stacker). More recently, companies like Wealthfront and Betterment introduced the first robo advisors, which allowed for a humanless financial planning and investing and laid out the foundations for a computer-driven future of the trading sector. AI, blockchain and cryptocurrencies followed, bringing us to where we stand today.

However, as a novel sector, the cryptocurrency market is still trailing behind in terms of analytical technology that is available to the traders. The analysis tools used traditionally in trading are rarely applicable to crypto due to the fundamental differences to the stock market and the inherent volatility of the industry. Many old school traders believe it is impossible to come up with reliable models that can be applied for cryptocurrency trading.  Surprisingly, recent research states otherwise – the truth is that data is the fundament that can enable the creation of statistically reliable models – even in cryptocurrency trading. That is, if you had a close to unlimited capabilities of gathering and analyzing a variety of market data. While you might think this is unlikely, technology has come a long way – particularly in the areas of Artificial Intelligence and its application to trading. Big companies such as BlackRock and their portfolio management software Aladdin have long started to stretch the boundaries of the potential technology can bring within the trading ecosystem. Such software is developed over a prolonged period of time by a large team of experts and is perfected continuously to become reliable. As such, the access to such software is greatly limited to the average investor, presenting the trading scene with asymmetries and one-sided power in favor of the wealthiest.

Dohrnii Takes the Initiative

The Dohrnii ecosystem combines a digital crypto academy, an analytical trading platform and a trading module, forming a comprehensive trading environment for crypto traders who wish to get into cryptocurrency trading or to bring their skillset to a new level. Each trader is delivered a personalized experience along their journey – from the starting onboarding process, the skill of traders is evaluated and a custom educational program is compiled for their profile. As they progress and start trading, their preferences and performance are also analyzed, allowing for Dohrnii to design personalized investment advice such as portfolio adjustments and deliver them to the traders through the robo advisor. What is more, the traders have access to a wide variety of tools that are unique to the cryptocurrency trading scene – from advanced market analysis to trading signals and price predictions, Dohrnii introduces features that were once reserved to the biggest investors on the stock markets to the average crypto trader.

The technology that is turning the wheels of the Dohrnii ecosystem is where the magic happens. By using the latest advancements in Artificial Intelligence and blockchain, Dohrnii is making tools that used to be available only to the biggest investment companies and hedge funds accessible to the average trader, thereby democratizing fintech technology and bringing the market into a natural equilibrium. This equilibrium is of utmost importance, as it will dissolve the current situation of a partial monopoly caused by the discrepancies in the access to advanced trading technology, which translates in much better advantage for several key players.

The Dohrnii Foundation is a non-profit organization based in Zug, Switzerland. It was founded in 2020 by a team of professionals with longstanding experience in multiple areas, all of whom with one common goal – to transform the world of cryptocurrency trading from a black box to an understandable discipline everyone has the ability to comprehend. The experts behind the Dohrnii Foundation have a diversified skill set, ranging from finance, trading, fintech, technology and blockchain, forming the fundamental backbone required for the creation of the Dohrnii ecosystem.

If you are interested in learning more about the Dohrnii project, the tools the ecosystem is offering to the traders and the innovative technology behind it, visit

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AI-powered identity access management platform Authomize raises $16M




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Cloud-based authorization startup Authomize today announced that it raised $16 million in series A funding led by Innovation Endeavors, bringing the startup’s total raised to $22 million to date. CEO and cofounder Dotan Bar Noy says that the capital will be used to support Authomize’s R&D and hiring efforts this year, as expansion ramps up.

One study found that companies consider implementing adequate identity governance and administration (IGA) practices to be among the least urgent tasks when it comes to securing the cloud. That’s despite the fact that, according to LastPass, 82% of IT professionals at small and mid-size businesses say identity challenges and poor practices pose risks to their employers.

Authomize, which emerged from stealth in June 2020, aims to address IGA challenges by delivering a complete view of apps across cloud environments. The company’s platform is designed to reduce the burden on IT teams by providing prescriptive, corrective suggestions and securing identities, revealing the right level of permissions and managing risk to ensure compliance.

“As security has evolved from endpoints and networks, attention has increasingly moved to identity and access management, and specifically the authorization space. Many of the CISOs and CIOs we spoke with expressed the need for a system that would secure and manage permissions from a single platform. They took access decisions based on hunches, not data, and when they tried to take data-driven decisions, they found out that the data was outdated. Additionally, most, if not all, of the process has been manually managed, making the IT and security teams the bottleneck for growth,” Noy told VentureBeat in an interview via email.

Authomize’s secret sauce is a technology called Smart Groups that aggregates data from enterprise systems in real time and infers the right-sized permissions. Using this data in tandem with graph neural networksunsupervised learning methods, evolutionary systems, and quantum-inspired algorithms, the platform offers action and process automation recommendations.

AI-powered recommendations

Using AI, Authomize detects relationships between identities and company assets throughout an organization’s clouds. The platform offers an inventory of access policies, blocking unintended access with guardrails and alerting on anomalies and risks. In practice, Authomize constructs a set of policies for each identity-asset relationship. It performs continuous access modeling, self-correcting as it incorporates new inputs like actual usage, activities, and decisions.

Of course, Authomize isn’t the only company in the market claiming to automate away IGA. ForgeRock, for instance, recently raised $93.5 million to further develop its products that tap AI and machine learning to streamline activities like approving access requests, performing certifications, and predicting what access should be provisioned to users.

But Authomize has the backing of notable investor M12 (Microsoft’s venture fund), Entrée Capital, and Blumberg Capital, along with acting and former CIOs, CISOs, and advisers from Okta, Splunk, ServiceNow, Fidelity, and Rubrik. Several undisclosed partners use the company’s product in production, Authomize claims — including an organization with 5,000 employees that tapped Smart Groups to cut its roughly 50,000 Microsoft Office 365 entitlements by 95%. And annual recurring revenue growth is expected to hit 600% during 2021.

Authomize recently launched an integration with the Microsoft Graph API to provide explainable, prescriptive recommendations for Microsoft services permissions. Via the API, Authomize can evaluate customers’ organization structure and authorization details, including role assignments, group security settings, SharePoint sites, OneDrive files access details, calendar sharing information, applications, and service principal access scopes and settings.

“Our technology is allowing teams to make authorization decisions based on accurate and updated data, and we also automate day-to-day processes to reduce IT burden … Authomize currently secures more than 7 million identities and hundreds of millions of assets, and our solution is deployed across dozens of customers,” Noy said. “Using our proprietary [platform], organizations can now strike a balance between security and IT, ensuring human and machine identity have only the permission they need. Our technology is built to connect easily to the entire organization stack and help solve the increasing complexity security, and IT teams face while reducing the overall operational burden.”

Authomize, which is based in Tel Aviv, Israel, has 22 full-time employees. It expects to have more than 55 by the end of the year as it expands its R&D teams to develop new entitlement eligibility engine and automation capabilities and increases its sales and marketing operations in North America.


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10 cool tech events you shouldn’t miss out on this June




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