Developing an effective vaccine for the current pandemic as well as treatment options for COVID-19 patients is easier said than done. The drug discovery and development processes are by no means a walk through the park, even after a potential lead is identified, countless hurdles still need to be overcome before any drug makes it to the public.

Traditional new drug development is an expensive process and is comprised of a discovery phase that includes target-based drug screening and optimization, among other processes to identify candidates to advance towards further development. Subsequent drug development involves drug combination design with these candidates, and clinical trials. Unfortunately, success rates are very low, said Dean Ho of the National University of Singapore. Ho and his collaborator, Professor Xianting Ding of Shanghai Jiao Tong University, have turned to AI to solve this problem.

For rapidly spreading pathogens with unpredictable clinical courses, [such as the current SARS-CoV-2 outbreak], this process takes too long, even with the assistance of emerging technologies, added Ho.

Even re-purposing known drugs for combination therapies can be quite challenging as choosing the right combination as well as dosage precludes the optimization of treatment outcomes, said Ho. This also limits how many drugs can be simultaneously explored, as conventional drug screening protocols cannot cope with the large data pools that get generated as a result. Given this challenge, traditional new drug development and traditional repurposing are inherently sub-optimal, he added.

In a recent paper published in Advanced Therapeutics, the team developed an AI-based platform called Project IDentif. AI that was shown to quickly screen and identify viable combination therapies for the past, present, and future infections.

Using traditional re-purposing to address SARS or MERS would be very challenging due to the aforementioned issues, said Ding. However, with a platform like IDentif.AI, combination therapy optimisation could be accomplished within days. IDentif.AI is a disease-agnostic platform. As such, it does not have to be reprogrammed, and can be immediately deployed against any novel or established pathogen.

According to the team, the core importance of IDentif.AI is that it simultaneously reconciles the optimal drugs and doses against virtually any disease model from the aforementioned extraordinarily large drug/dose parameter spaces. When good drugs are given at the wrong dose, there may be no treatment efficacy at all, said Ho. At the same time, drug dosing may also have a role in determining which drugs belong in a combination in the first place. Therefore, simultaneously pinpointing the right drugs and doses is absolutely essential.

To run a search, a small set of pre-designed combinations of drugs is given to provide a sample of the drug-dose parameter space. Imagine filling up an entire room with tiny marbles, with each marble representing a possible drug-dose combination. Our job is to find ranked list of best to worst marbles from a room filled with billions of them. This pre-designed set of combinations doesnt pinpoint every single one of them, but at least samples enough of the space to guide us to where the best one is and tells us the drugs/dosages of that optimal combination, explained Ding.

After this first set of experiments is done and the full drug-dose space is essentially mapped out for us, IDentif.AI operates off the concept that drugs and doses (inputs) are related to treatment outcomes (e.g., antiviral activity, drug toxicity) using a smooth quadratic surface (resembling a smooth mountain with one peak), added Ho. This surface is calibrated and mapped out by these set of unique initial experiments such as preventing a virus from infecting a healthy cell or shrinking a tumour (maximizing efficacy), or preventing healthy cell death (minimizing toxicity), etc.).

The map is therefore unique to every study, using different drugs and disease models, and can represent a population of cells, animals, people, or even a single patient, says Ding. To develop a population-optimized regimen, we can take biological samples pooled from a population of patients. This pooled sample can be run against a standardized cell infection model and within days, a combination will be derived. The surface map will be based on the viral/infected cell population represented by a large population of patients.

For a personalized case, if there is a patient with a high viral load, we can run the test using only their own sample, and within days, we can develop a regimen just for that patient, and the surface map will be represented by only their own sample.

And not every single drug combination needs to be screened, as once the team runs a threshold number of experiments, the map can be created and used to guide the team through the rankings of best to worst combinations based on optimal inhibition of infection and minimal toxicity.

What is really neat about IDentif.AI its ability to interrogate such as huge drug-dose space, which has already directly led to successful clinical outcomes and other indications, said Ho. As proof of concept, the team was able to identify an effective combination therapy that successfully inhibited A549 lung cell infection by the vesicular stomatitis virus (VSV) within three days of project. This compared to the months or even years that conventional drug discovery searches require, which are still only capable of exploring a tiny chemical space with poor clinical outcomes, demonstrates this technologys critical importance.

The reality is that the world will be confronted with challenges such as COVID-19 again, said Ho. We simply dont have the time or resources to wait for vaccines or antibody therapy every time. Lessons learned from COVID-19 have shown us that we cannot continue to relinquish valuable time in identifying optimal repurposed combinations. This will not solve the problem and will lead to drug shortages when some could have in fact been used correctly if a systematic optimization process was conducted.

IDentif.AI is also being adapted for additional pathogens such as Dengue fever and even the possibility of a SARS-CoV-2 mutation. If it mutates to a stage where a novel combination will be needed, IDentif.AI will be prepared to rapidly respond, said Ho.

Our aim is to give Project IDentif.AI to the world so that the next epidemic can potentially be contained or prevented using rapidly optimized drug repurposing. Implementing IDentif.AI is remarkable, rapid, and economical. As such, our work has involved healthcare economics, global health security, and surveillance experts to help us develop strategies to scale this towards widespread use on a cost-neutral basis.

Visit link:
Recruiting artificial intelligence in battle against COVID-19 and future pandemics - Guardian

Much of the work undertaken by artificial intelligence involves a training process known as machine learning, where AI gets better at a task such as recognising a cat or mapping a route the more it does it. Now that same technique is being use to create new AI systems, without any human intervention.

For years, engineers at Google have been working on a freakishly smart machine learning system known as theAutoML system(or automatic machine learning system), which is already capable of creating AI that outperforms anything we've made.

Now, researchers have tweaked it to incorporate concepts of Darwinian evolution and shown it can build AI programs that continue to improve upon themselves faster than they would if humans were doing the coding.

The new system is called AutoML-Zero, and although it may sound a little alarming, it could lead to the rapid development of smarter systems - for example, neural networked designed to more accurately mimic the human brain with multiple layers and weightings, something human coders have struggled with.

"It is possible today to automatically discover complete machine learning algorithms just using basic mathematical operations as building blocks," write the researchers in their pre-print paper. "We demonstrate this by introducing a novel framework that significantly reduces human bias through a generic search space."

The original AutoML system is intended to make it easier for apps to leverage machine learning, and already includes plenty of automated features itself, but AutoML-Zero takes the required amount of human input way down.

Using a simple three-step process - setup, predict and learn - it can be thought of as machine learning from scratch.

The system starts off with a selection of 100 algorithms made by randomly combining simple mathematical operations. A sophisticated trial-and-error process then identifies the best performers, which are retained - with some tweaks - for another round of trials. In other words, the neural network is mutating as it goes.

When new code is produced, it's tested on AI tasks - like spotting the difference between a picture of a truck and a picture of a dog - and the best-performing algorithms are then kept for future iteration. Like survival of the fittest.

And it's fast too: the researchers reckon up to 10,000 possible algorithms can be searched through per second per processor (the more computer processors available for the task, the quicker it can work).

Eventually, this should see artificial intelligence systems become more widely used, and easier to access for programmers with no AI expertise. It might even help us eradicate human bias from AI, because humans are barely involved.

Work to improve AutoML-Zero continues, with the hope that it'll eventually be able to spit out algorithms that mere human programmers would never have thought of. Right now it's only capable of producing simple AI systems, but the researchers think the complexity can be scaled up rather rapidly.

"While most people were taking baby steps, [the researchers] took a giant leap into the unknown," computer scientist Risto Miikkulainen from the University of Texas, Austin, who was not involved in the work, told Edd Gent at Science. "This is one of those papers that could launch a lot of future research."

The research paper has yet to be published in a peer-reviewed journal, but can be viewed online at arXiv.org.

See the original post:
Google Engineers 'Mutate' AI to Make It Evolve Systems Faster Than We Can Code Them - ScienceAlert

Updated: May 25, 2018:

JD Supra is a legal publishing service that connects experts and their content with broader audiences of professionals, journalists and associations.

This Privacy Policy describes how JD Supra, LLC ("JD Supra" or "we," "us," or "our") collects, uses and shares personal data collected from visitors to our website (located at http://www.jdsupra.com) (our "Website") who view only publicly-available content as well as subscribers to our services (such as our email digests or author tools)(our "Services"). By using our Website and registering for one of our Services, you are agreeing to the terms of this Privacy Policy.

Please note that if you subscribe to one of our Services, you can make choices about how we collect, use and share your information through our Privacy Center under the "My Account" dashboard (available if you are logged into your JD Supra account).

Registration Information. When you register with JD Supra for our Website and Services, either as an author or as a subscriber, you will be asked to provide identifying information to create your JD Supra account ("Registration Data"), such as your:

Other Information: We also collect other information you may voluntarily provide. This may include content you provide for publication. We may also receive your communications with others through our Website and Services (such as contacting an author through our Website) or communications directly with us (such as through email, feedback or other forms or social media). If you are a subscribed user, we will also collect your user preferences, such as the types of articles you would like to read.

Information from third parties (such as, from your employer or LinkedIn): We may also receive information about you from third party sources. For example, your employer may provide your information to us, such as in connection with an article submitted by your employer for publication. If you choose to use LinkedIn to subscribe to our Website and Services, we also collect information related to your LinkedIn account and profile.

Your interactions with our Website and Services: As is true of most websites, we gather certain information automatically. This information includes IP addresses, browser type, Internet service provider (ISP), referring/exit pages, operating system, date/time stamp and clickstream data. We use this information to analyze trends, to administer the Website and our Services, to improve the content and performance of our Website and Services, and to track users' movements around the site. We may also link this automatically-collected data to personal information, for example, to inform authors about who has read their articles. Some of this data is collected through information sent by your web browser. We also use cookies and other tracking technologies to collect this information. To learn more about cookies and other tracking technologies that JD Supra may use on our Website and Services please see our "Cookies Guide" page.

We use the information and data we collect principally in order to provide our Website and Services. More specifically, we may use your personal information to:

JD Supra takes reasonable and appropriate precautions to insure that user information is protected from loss, misuse and unauthorized access, disclosure, alteration and destruction. We restrict access to user information to those individuals who reasonably need access to perform their job functions, such as our third party email service, customer service personnel and technical staff. You should keep in mind that no Internet transmission is ever 100% secure or error-free. Where you use log-in credentials (usernames, passwords) on our Website, please remember that it is your responsibility to safeguard them. If you believe that your log-in credentials have been compromised, please contact us at privacy@jdsupra.com.

Our Website and Services are not directed at children under the age of 16 and we do not knowingly collect personal information from children under the age of 16 through our Website and/or Services. If you have reason to believe that a child under the age of 16 has provided personal information to us, please contact us, and we will endeavor to delete that information from our databases.

Our Website and Services may contain links to other websites. The operators of such other websites may collect information about you, including through cookies or other technologies. If you are using our Website or Services and click a link to another site, you will leave our Website and this Policy will not apply to your use of and activity on those other sites. We encourage you to read the legal notices posted on those sites, including their privacy policies. We are not responsible for the data collection and use practices of such other sites. This Policy applies solely to the information collected in connection with your use of our Website and Services and does not apply to any practices conducted offline or in connection with any other websites.

JD Supra's principal place of business is in the United States. By subscribing to our website, you expressly consent to your information being processed in the United States.

You can make a request to exercise any of these rights by emailing us at privacy@jdsupra.com or by writing to us at:

You can also manage your profile and subscriptions through our Privacy Center under the "My Account" dashboard.

We will make all practical efforts to respect your wishes. There may be times, however, where we are not able to fulfill your request, for example, if applicable law prohibits our compliance. Please note that JD Supra does not use "automatic decision making" or "profiling" as those terms are defined in the GDPR.

Pursuant to Section 1798.83 of the California Civil Code, our customers who are California residents have the right to request certain information regarding our disclosure of personal information to third parties for their direct marketing purposes.

You can make a request for this information by emailing us at privacy@jdsupra.com or by writing to us at:

Some browsers have incorporated a Do Not Track (DNT) feature. These features, when turned on, send a signal that you prefer that the website you are visiting not collect and use data regarding your online searching and browsing activities. As there is not yet a common understanding on how to interpret the DNT signal, we currently do not respond to DNT signals on our site.

For non-EU/Swiss residents, if you would like to know what personal information we have about you, you can send an e-mail to privacy@jdsupra.com. We will be in contact with you (by mail or otherwise) to verify your identity and provide you the information you request. We will respond within 30 days to your request for access to your personal information. In some cases, we may not be able to remove your personal information, in which case we will let you know if we are unable to do so and why. If you would like to correct or update your personal information, you can manage your profile and subscriptions through our Privacy Center under the "My Account" dashboard. If you would like to delete your account or remove your information from our Website and Services, send an e-mail to privacy@jdsupra.com.

We reserve the right to change this Privacy Policy at any time. Please refer to the date at the top of this page to determine when this Policy was last revised. Any changes to our Privacy Policy will become effective upon posting of the revised policy on the Website. By continuing to use our Website and Services following such changes, you will be deemed to have agreed to such changes.

If you have any questions about this Privacy Policy, the practices of this site, your dealings with our Website or Services, or if you would like to change any of the information you have provided to us, please contact us at: privacy@jdsupra.com.

As with many websites, JD Supra's website (located at http://www.jdsupra.com) (our "Website") and our services (such as our email article digests)(our "Services") use a standard technology called a "cookie" and other similar technologies (such as, pixels and web beacons), which are small data files that are transferred to your computer when you use our Website and Services. These technologies automatically identify your browser whenever you interact with our Website and Services.

We use cookies and other tracking technologies to:

There are different types of cookies and other technologies used our Website, notably:

JD Supra Cookies. We place our own cookies on your computer to track certain information about you while you are using our Website and Services. For example, we place a session cookie on your computer each time you visit our Website. We use these cookies to allow you to log-in to your subscriber account. In addition, through these cookies we are able to collect information about how you use the Website, including what browser you may be using, your IP address, and the URL address you came from upon visiting our Website and the URL you next visit (even if those URLs are not on our Website). We also utilize email web beacons to monitor whether our emails are being delivered and read. We also use these tools to help deliver reader analytics to our authors to give them insight into their readership and help them to improve their content, so that it is most useful for our users.

Analytics/Performance Cookies. JD Supra also uses the following analytic tools to help us analyze the performance of our Website and Services as well as how visitors use our Website and Services:

Facebook, Twitter and other Social Network Cookies. Our content pages allow you to share content appearing on our Website and Services to your social media accounts through the "Like," "Tweet," or similar buttons displayed on such pages. To accomplish this Service, we embed code that such third party social networks provide and that we do not control. These buttons know that you are logged in to your social network account and therefore such social networks could also know that you are viewing the JD Supra Website.

If you would like to change how a browser uses cookies, including blocking or deleting cookies from the JD Supra Website and Services you can do so by changing the settings in your web browser. To control cookies, most browsers allow you to either accept or reject all cookies, only accept certain types of cookies, or prompt you every time a site wishes to save a cookie. It's also easy to delete cookies that are already saved on your device by a browser.

The processes for controlling and deleting cookies vary depending on which browser you use. To find out how to do so with a particular browser, you can use your browser's "Help" function or alternatively, you can visit http://www.aboutcookies.org which explains, step-by-step, how to control and delete cookies in most browsers.

We may update this cookie policy and our Privacy Policy from time-to-time, particularly as technology changes. You can always check this page for the latest version. We may also notify you of changes to our privacy policy by email.

If you have any questions about how we use cookies and other tracking technologies, please contact us at: privacy@jdsupra.com.

Read the original here:
Using artificial intelligence against the spread of COVID-19 - JD Supra

For the last couple of years, Artificial Intelligence (AI) has been changing many fields and increasing efficiency by using improved datasets. One of those areas where AI has accelerated evolution is the robo-advisory, which is a field having extensive financial big data to analyze.

Robo-advisors are the systems that use algorithms to automatically perform investment decisions or tasks which are mostly done by human advisors. Robo advisors are a potential solution to the complexities of financial decision making, said Jill E. Fisch, a law professor at the University of Pennsylvania at a conference of Pension Research Council.

In the main scheme, robo-advisors are merging customers information such as their financial goals, risk tolerances, timeframes, with the right asset allocation that qualifies customers needs. While making this merge, they use many algorithms including machine learning models to create the best fit for the customer. In the process of timeframe, they take lots of actions as well such as rebalancing the portfolio or performing tax-loss harvesting. This automatically increases efficiency while taking decisions at the right time for the portfolio.

AI usage in enterprises

Numerous enterprises have started to use AI in the robo-advisory field. Betterment is one of these robo-advisor enterprises that uses AI to reduce taxes on transactions where machine learning algorithms select the specific tax consequences of the transactions.Similar to Betterment, SigFig also uses its AI engine automatically to allocate assets and determines which investments will result in minimum taxes.

Another enterprise that uses AI is Wealthfront. Former CEO Adam Nash says, Were firm believers that artificial intelligence applied to your actual behavior will provide far more powerful advice than what traditional advisors offer today.

Also, Fidelity has already started its robo-advisory service in 2016 as Fidelity Go and as the beginning of 2019, Fidelity Go took top ranking as the best overall robo-advisor in the 2019 winter edition of The Robo Ranking report from Backend Benchmarking.

Efficiency side

The biggest impact of AI might be the time-saving base for human advisors. With AIs deep learning capabilities which relieve advisors from having to perform much of the rote or mundane monitoring and administrative tasks that currently allocate a significant portion of their time. When allocations fall outside of certain parameters for the specific clients, an AI-based system can trigger it into the monitor of the human advisor.

To increase efficiency, AI requires vast amounts of data to give more accurate results. Analysis of vast quantities of historical and financial data will uncover alpha opportunities that traditional analysis would otherwise overlook and give robo-advisors an edge over passive strategies and AI can process big data swiftly, allowing robo-advisors to adapt to changing market conditions and consumer behaviors much quicker in order to make better investment decisions. Time saved is key here, says John Zhang, founder of a robo-advisory startup WealthGap which explores AI in hedge funds-like portfolios.

Enterprises that offer robo-advisory services may not abandon the human component completely, but it seems the adoption of artificial intelligence is enhancing the platforms and they will be more able to give clients the big picture in the course of time.

See original here:
How AI Is Expanding The Applications Of Robo Advisory - Forbes

Updated: May 25, 2018:

JD Supra is a legal publishing service that connects experts and their content with broader audiences of professionals, journalists and associations.

This Privacy Policy describes how JD Supra, LLC ("JD Supra" or "we," "us," or "our") collects, uses and shares personal data collected from visitors to our website (located at http://www.jdsupra.com) (our "Website") who view only publicly-available content as well as subscribers to our services (such as our email digests or author tools)(our "Services"). By using our Website and registering for one of our Services, you are agreeing to the terms of this Privacy Policy.

Please note that if you subscribe to one of our Services, you can make choices about how we collect, use and share your information through our Privacy Center under the "My Account" dashboard (available if you are logged into your JD Supra account).

Registration Information. When you register with JD Supra for our Website and Services, either as an author or as a subscriber, you will be asked to provide identifying information to create your JD Supra account ("Registration Data"), such as your:

Other Information: We also collect other information you may voluntarily provide. This may include content you provide for publication. We may also receive your communications with others through our Website and Services (such as contacting an author through our Website) or communications directly with us (such as through email, feedback or other forms or social media). If you are a subscribed user, we will also collect your user preferences, such as the types of articles you would like to read.

Information from third parties (such as, from your employer or LinkedIn): We may also receive information about you from third party sources. For example, your employer may provide your information to us, such as in connection with an article submitted by your employer for publication. If you choose to use LinkedIn to subscribe to our Website and Services, we also collect information related to your LinkedIn account and profile.

Your interactions with our Website and Services: As is true of most websites, we gather certain information automatically. This information includes IP addresses, browser type, Internet service provider (ISP), referring/exit pages, operating system, date/time stamp and clickstream data. We use this information to analyze trends, to administer the Website and our Services, to improve the content and performance of our Website and Services, and to track users' movements around the site. We may also link this automatically-collected data to personal information, for example, to inform authors about who has read their articles. Some of this data is collected through information sent by your web browser. We also use cookies and other tracking technologies to collect this information. To learn more about cookies and other tracking technologies that JD Supra may use on our Website and Services please see our "Cookies Guide" page.

We use the information and data we collect principally in order to provide our Website and Services. More specifically, we may use your personal information to:

JD Supra takes reasonable and appropriate precautions to insure that user information is protected from loss, misuse and unauthorized access, disclosure, alteration and destruction. We restrict access to user information to those individuals who reasonably need access to perform their job functions, such as our third party email service, customer service personnel and technical staff. You should keep in mind that no Internet transmission is ever 100% secure or error-free. Where you use log-in credentials (usernames, passwords) on our Website, please remember that it is your responsibility to safeguard them. If you believe that your log-in credentials have been compromised, please contact us at privacy@jdsupra.com.

Our Website and Services are not directed at children under the age of 16 and we do not knowingly collect personal information from children under the age of 16 through our Website and/or Services. If you have reason to believe that a child under the age of 16 has provided personal information to us, please contact us, and we will endeavor to delete that information from our databases.

Our Website and Services may contain links to other websites. The operators of such other websites may collect information about you, including through cookies or other technologies. If you are using our Website or Services and click a link to another site, you will leave our Website and this Policy will not apply to your use of and activity on those other sites. We encourage you to read the legal notices posted on those sites, including their privacy policies. We are not responsible for the data collection and use practices of such other sites. This Policy applies solely to the information collected in connection with your use of our Website and Services and does not apply to any practices conducted offline or in connection with any other websites.

JD Supra's principal place of business is in the United States. By subscribing to our website, you expressly consent to your information being processed in the United States.

You can make a request to exercise any of these rights by emailing us at privacy@jdsupra.com or by writing to us at:

You can also manage your profile and subscriptions through our Privacy Center under the "My Account" dashboard.

We will make all practical efforts to respect your wishes. There may be times, however, where we are not able to fulfill your request, for example, if applicable law prohibits our compliance. Please note that JD Supra does not use "automatic decision making" or "profiling" as those terms are defined in the GDPR.

Pursuant to Section 1798.83 of the California Civil Code, our customers who are California residents have the right to request certain information regarding our disclosure of personal information to third parties for their direct marketing purposes.

You can make a request for this information by emailing us at privacy@jdsupra.com or by writing to us at:

Some browsers have incorporated a Do Not Track (DNT) feature. These features, when turned on, send a signal that you prefer that the website you are visiting not collect and use data regarding your online searching and browsing activities. As there is not yet a common understanding on how to interpret the DNT signal, we currently do not respond to DNT signals on our site.

For non-EU/Swiss residents, if you would like to know what personal information we have about you, you can send an e-mail to privacy@jdsupra.com. We will be in contact with you (by mail or otherwise) to verify your identity and provide you the information you request. We will respond within 30 days to your request for access to your personal information. In some cases, we may not be able to remove your personal information, in which case we will let you know if we are unable to do so and why. If you would like to correct or update your personal information, you can manage your profile and subscriptions through our Privacy Center under the "My Account" dashboard. If you would like to delete your account or remove your information from our Website and Services, send an e-mail to privacy@jdsupra.com.

We reserve the right to change this Privacy Policy at any time. Please refer to the date at the top of this page to determine when this Policy was last revised. Any changes to our Privacy Policy will become effective upon posting of the revised policy on the Website. By continuing to use our Website and Services following such changes, you will be deemed to have agreed to such changes.

If you have any questions about this Privacy Policy, the practices of this site, your dealings with our Website or Services, or if you would like to change any of the information you have provided to us, please contact us at: privacy@jdsupra.com.

As with many websites, JD Supra's website (located at http://www.jdsupra.com) (our "Website") and our services (such as our email article digests)(our "Services") use a standard technology called a "cookie" and other similar technologies (such as, pixels and web beacons), which are small data files that are transferred to your computer when you use our Website and Services. These technologies automatically identify your browser whenever you interact with our Website and Services.

We use cookies and other tracking technologies to:

There are different types of cookies and other technologies used our Website, notably:

JD Supra Cookies. We place our own cookies on your computer to track certain information about you while you are using our Website and Services. For example, we place a session cookie on your computer each time you visit our Website. We use these cookies to allow you to log-in to your subscriber account. In addition, through these cookies we are able to collect information about how you use the Website, including what browser you may be using, your IP address, and the URL address you came from upon visiting our Website and the URL you next visit (even if those URLs are not on our Website). We also utilize email web beacons to monitor whether our emails are being delivered and read. We also use these tools to help deliver reader analytics to our authors to give them insight into their readership and help them to improve their content, so that it is most useful for our users.

Analytics/Performance Cookies. JD Supra also uses the following analytic tools to help us analyze the performance of our Website and Services as well as how visitors use our Website and Services:

Facebook, Twitter and other Social Network Cookies. Our content pages allow you to share content appearing on our Website and Services to your social media accounts through the "Like," "Tweet," or similar buttons displayed on such pages. To accomplish this Service, we embed code that such third party social networks provide and that we do not control. These buttons know that you are logged in to your social network account and therefore such social networks could also know that you are viewing the JD Supra Website.

If you would like to change how a browser uses cookies, including blocking or deleting cookies from the JD Supra Website and Services you can do so by changing the settings in your web browser. To control cookies, most browsers allow you to either accept or reject all cookies, only accept certain types of cookies, or prompt you every time a site wishes to save a cookie. It's also easy to delete cookies that are already saved on your device by a browser.

The processes for controlling and deleting cookies vary depending on which browser you use. To find out how to do so with a particular browser, you can use your browser's "Help" function or alternatively, you can visit http://www.aboutcookies.org which explains, step-by-step, how to control and delete cookies in most browsers.

We may update this cookie policy and our Privacy Policy from time-to-time, particularly as technology changes. You can always check this page for the latest version. We may also notify you of changes to our privacy policy by email.

If you have any questions about how we use cookies and other tracking technologies, please contact us at: privacy@jdsupra.com.

Original post:
Navigating Artificial Intelligence and Consumer Protection Laws In Wake of the COVID-19 Pandemic - JD Supra

The governments Integrated Review comes at a time of considerable technological change. The UK has entered a Fourth Industrial Revolution (4IR), which promises to fundamentally alter the way we live, work, and relate to one another. This new era will be characterised by scientific breakthroughs in fields such as the Internet of Things, Blockchain, quantum computing, fifth-generation wireless technologies (5G), robotics, and artificial intelligence (AI), which together are expected to deliver transformational changes across almost every sector of the economy.

Of particular note are recent developments in AI, specifically advances in the sub-field of machine learning. Progress over the last decade has been driven by an exponential growth in computing power, coupled with increased availability of vast datasets with which to train machine learning algorithms. While machine learning technology can be traced back to at least the 1950s, investment has increased substantially in recent years, and as a result AI is rapidly becoming ubiquitous across the economy.

AI is often described as a general purpose technology its potential applications are manifold, ranging from mundane administrative tasks through to complex individual-level behavioural analysis, for instance to forecast consumer demand based on purchasing history, or to recommend music and films based on users personal interests. The ability of machine learning algorithms to rapidly derive insights from previously unexamined data has far-reaching ethical and societal implications, which are particularly pertinent in high risk contexts such as healthcare, law enforcement or defence.

There are countless ways in which the UKs defence and security sector could conceivably seek to deploy AI. Given its diverse applications, it will be essential to strategically prioritise the areas where AI is expected to provide the most immediate benefits, while being realistic about areas where its utility remains unproven. This strategic prioritisation process should be guided by the following three principles.

There is a natural tendency to overestimate the effects of new technology in the short term while underestimating the long-term impacts; the phenomenon is known as Amaras Law. While AI is likely to have a transformative impact on defence and security in the long term, any specific forecasts looking beyond the next decade are likely to be highly speculative. There is a risk that policy decisions are guided by hypothetical future uses and hyperbolic worst-case scenario outcomes, rather than focusing on realistic near-term applications. In reality, the immediate short-term benefits of AI will be an incremental augmentation of existing processes, rather than the creation of novel, futuristic capabilities. This will need to be appropriately reflected in development and procurement strategies.

Moreover, AI investment is often hampered by a lack of technical understanding, and customers are all too easily seduced by media hype and marketing buzzwords. Rates of predictive accuracy are often misinterpreted or misrepresented, making it difficult for the buyer to assess a tools real-world benefits. A focus on statistical accuracy may also distract from fundamental questions regarding the operational value of AI products when deployed in the field. In many cases, a non-AI solution may be more appropriate to the task at hand, and there will be situations in which use of AI will be undesirable or counterproductive.

Poor data quality or data availability can also pose major challenges. Developing effective machine learning systems requires access to large, well-curated datasets. Lack of access to clean, operationally relevant data can lead to frustration and delay during software development, particularly in sensitive contexts such as defence and security, where datasets often require additional protections and restrictions. Data requires substantial preparation, cleaning and pre-processing before it is suitable for machine analysis, which will need to be taken into account in the resourcing of government AI projects.

For these reasons, it is essential to ensure a sufficient degree of data analytics literacy among senior decision-makers responsible for AI procurement. The UK government should adopt a cautious and sceptical approach to the procurement of commercial AI technology, and refrain from committing to long-term contractual agreements before assessing a products real-world benefits. The importance of data quality and testing should not be underestimated: many products will fail to deliver as advertised when deployed in the field, and AI applications require iterative trialling, evaluation, verification and validation to maintain their efficacy.

AI is often characterised in terms of the ability to perform tasks normally requiring human intelligence. With organisations under increasing pressure to do more with less, AI can be viewed as an attractive option to minimise the human resources required to deliver certain business functions. But there are limits to the human processes that can be effectively automated. Existing AI is most useful when applied to narrowly defined, repetitive tasks. The more abstract the problem, the less useful AI becomes.

For this reason, the most immediate benefit from AI will be the ability to automate organisational, administrative and data-management processes, freeing up staff time to focus on more complex or abstract cognitive tasks. There are countless more innovative, experimental uses which will be of interest to the defence and security sector, but in many cases these remain at an early stage of development and their potential benefits are yet to be proven. Moreover, mundane uses of AI to automate repetitive administrative processes will typically not give rise to the same complex ethical challenges associated with more innovative applications.

In the short term, the main focus for AI investment should be the automation of organisational, administrative and data-management processes. Alongside this, efforts should focus on repurposing existing AI technology that is already widely used in other sectors (such as audiovisual analysis and natural language processing). To support innovation in the medium to long term, research funding should be made available for technology providers and academic institutions to co-develop proofs of concept and pilot projects for the more experimental, cutting-edge capabilities which are yet to be evaluated operationally.

Human expertise is the single most important component of any AI project. Cultivating technical expertise and developing a workforce of data-literate practitioners must therefore be a core objective of any future AI development strategy.

The UK government should invest in developing a core cell of data-science expertise to lead the development and deployment of new AI applications in the defence and security sector. This should be achieved by recruiting new talent, retraining current practitioners and partnering with academic institutions. Many of the AI capabilities the defence and security sector may wish to implement could be developed in-house without reliance on third-party providers, minimising costs and enabling a more agile approach to testing, evaluation and validation. The initial investment of developing this in-house technical expertise will therefore be more than recuperated by the cost savings made in the long term. At the same time, the skills required may often be more readily available outside the public sector, and there is a need to develop more agile models of strategic collaboration with external stakeholders to take full advantage of this expertise.

In addition to this core cell of technical expertise, it is essential to ensure a high level of data literacy among practitioners across the defence and security sector. When AI is integrated into a decision-making process overseen by a human operator, the user must sufficiently understand the limitations inherent in the system to be able to use the output in conjunction with their professional judgement. This is important not just to ensure accountable decision-making, but also to build trust between human operators and AI systems. Senior decision-makers must also have a foundational understanding of the benefits and shortcomings of different AI systems in order to maintain accountability at all levels of the decision-making chain.

Finally, any future AI development will need to be governed by a clear ethical and regulatory framework. Public discourse is increasingly focused on the governance and regulation of data analytics, and there are high expectations of transparency in how new technologies are developed and deployed. Despite an abundance of high-level data ethics principles, it remains unclear how these should be operationalised in different contexts. Additional sector-specific guidance should be developed to ensure ethical and proportionate use of AI for defence and security, including mechanisms for independent scrutiny and ethical oversight.

The views expressed in this Commentary are the author's, and do not represent those of RUSI or any other institution.

BANNER IMAGE: Representation of an artificial brain. Public domain

Read this article:
Artificial Intelligence and the Integrated Review: The Need for Strategic Prioritisation - RUSI Analysis

The Pentagon is shopping around for ideas from industry regarding how it might better test and evaluate future artificial intelligence products to ensure they are safe and effective.

In a request for information this week, the Pentagons Joint Artificial Intelligence Center, or JAIC, seeks input on cutting-edge testing and evaluation capabilities to support the full spectrum of the Defense Departments emerging AI technologies, including machine learning, deep learning and neural networks.

According to the solicitation, the Pentagon wants to augment the JAICs Test and Evaluation office, which develops standards and conducts algorithm testing, system testing and operational testing on the militarys many AI initiatives.

The Pentagon stood up the JAIC in 2018 to centralize coordination and accelerate the adoption of AI and has been building out its ranks in recent months, hiring an official to implement its new AI ethical principles for warfare.

The JAIC is requesting testing tools and expertise in planning, data management, and analysis of inputs and outputs associated with those tools. The introduction of AI-enabled systems is bringing changes to the process, metrics, data, and skills necessary to produce the level of testing the military needs, and that is why the JAIC is requesting information, Dr. Jane Pinelis, Chief, Test, Evaluation and Assessment at the JAIC, said in a statement. Testing and Evaluation provides knowledge of system capabilities and limitations to the acquisition community and to the warfighter. The JAIC's T&E team will make rigorous and objective assessments of systems under operational conditions and against realistic threats, so that our warfighters ultimately trust the systems they are operating and that the risks associated with operating these systems are well-known to military acquisition decision-makers."

The solicitation indicates it plans to use feedback from the solicitation to guide how it further builds out its capabilities. Specifically, the Pentagon is interested in tech testing tools that focus on:

In addition, the Pentagon wants feedback regarding evaluation services in five mission areas: dataset curation, test harness development, model output analysis, test reporting and testing services. Lastly, it seeks other technologies it may not be aware of that may be beneficial to testing and evaluation efforts.

Responses to the RFI are due May 10.

Original post:
Pentagon Needs Tools to Test the Limits of Its Artificial Intelligence Projects - Nextgov

In Rodgers v. Christie, a recent non-precedential decision, the United States Court of Appeals for the Third Circuit examined whether traditional strict products liability doctrines apply to artificial intelligence-based software. 2020 WL 1079233 (3d Cir. Mar. 6, 2020). There, plaintiffs asserted claims under the New Jersey Products Liability Act (PLA), arising from the States Public Safety Assessment (PSA). Id. at *1. The PSA is a data-based risk assessment algorithm which provides quantitative scores and a decision-making framework to assist courts in assess[ing] the risk that [a] criminal defendant will fail to appear for future court appearances or commit additional crimes and/or violent crimes if released. See Roders v. Laura and John Arnold Foundation, 2019 WL 2429574, at *1 (D.N.J. June 11, 2019), affd sub nom. Roders v. Christie, 2020 WL 1079233. Plaintiffs strict products liability claims put the PSA at issue, claiming the algorithm had assigned an erroneously low score to a convicted felon, who allegedly murdered their son three days after he was released from detention on non-monetary conditions. 2020 WL 1079233, at *1.

The trial court granted defendants motion to dismiss those claims on the basis that an algorithm, such as the PSA, cannot be considered a product subject to the PLA. In so holding, the trial court looked to the Restatement (Third) of Torts, which articulates two categories of products: (1) tangible personal property distributed commercially; and (2) [o]ther items, such as property and electricity . . . when the context of their distribution and use is sufficiently analogous to . . . tangible personal property. Id. citing Restatement (Third) of Torts 19. Thus, the court reasoned, because the PSA does not fit into either of these categories, it is not a product subject to the PLA, and plaintiffs claims could not proceed. 2019 WL 2429574, at *2-3.

On appeal, the Third Circuit upheld the dismissal of plaintiffs claims, holding that the PSA does not fit the definition of a product for purposes of the PLA for two reasons. First, the PSA, as a tool designed to assist courts, is not distributed commercially, and second, because information, guidance, ideas, and recommendations are not products under the Third Restatement, both as a definitional matter and because extending strict liability to the distribution of ideas would raise serious First Amendment concerns. 2020 WL 1079233, at *2 (internal quotations omitted). Importantly, the Court did not adopt a bright line rule barring the application of strict products liability claims for all artificial intelligence-based software only those which do not fit the Restatements definition.

While the Third Circuits decision Rodgers is non-precedential, it addresses a question many have flagged as central to the development of legal norms around emerging artificial intelligence-based products: whether artificial intelligence software is a product at all? As the Court astutely noted, this is a thorny question, which implicates concerns, such as the First Amendment, far beyond standard tort claims. All manner of commercial and consumer products are incorporating artificial intelligence, and courts around the country will be forced to answer this same question to determine how laws can appropriately address injuries arising from such products.

Visit link:
Third Circuit Weighs In On Strict Products Liability for Artificial Intelligence - Lexology

Now that were a month into lockdown, youve probably spent a considerable amount of your social life (read: all) on video messaging platforms. While its admittedly a great way to stay connected with friends when youre most likely cooped up in a cramped London flatshare, or enjoying a second wave of teenage angst at your parents house, its also led to some pretty raunchy gatherings: introducing the virtual sex party.

According to Rolling Stone, Zoom the popular teleconferencing app has become an unlikely gathering place for COVID-19 era millennials wanting to partake in play parties (AKA virtual chats where you can jerk off in the company of other socially-distanced people).

In short, Zooms not happy about it, and its using machine learning to identify accounts in violation of its policies, which strictly prohibit displays of nudity, violence, pornography, sexuality explicit material, or criminal activity.

We encourage users to report suspected violations of our policies, and we use a mix of tools, including machine learning, to proactively identify accounts that may be in violation, a spokesperson for Zoom told Rolling Stone.

While the platform hasnt specified what sort of machine-learning tools its using, or how the technology alerts the platform to pornographic content, a spokesperson said that itll take a number of actions against those caught in the act.

Meanwhile, rival video platform Houseparty is offering $1 millionfor info on an alleged smear campaign, which claims users have been getting their accounts hacked and personal information stolen. Basically, the internets reverted into the Wild West, and we love it.

Read the original:
Zoom is cracking down on virtual sex parties with artificial intelligence - Dazed

The crisis is an wake-up call to developing countries to speed up the digitalisation of their economies

In this article, I will refer to current efforts to harness Artificial Intelligence (AI) against COVID-19, note its promises, limitations, and potential pitfalls, and identify priorities for developing countries. Artificial Intelligence (AI) is the use of algorithms, data, and statistics to teach computers to recognize patterns and predict outcomes. Pattern recognition and prediction are what underlies Machine Learning (ML), Natural Language Processing (NLP), and Computer Vision, the main applications of modern AI.

Since the outbreak of the pandemic in December 2019, there has been a rush to harness AI in the fight. I document these in a recent companion article in Towards Data Science on Medium. AI can help track and predict the spread of the infection, it can help make diagnoses and prognoses, and it can search for treatments and a vaccine. It can also be used for social control for instance, to help isolate those that are infected and monitor and enforce compliance with lockdown measures.

Unfortunately, AI is currently not up to the job to rigorously track and predict the infection. It cannot yet provide reliable assistance in diagnoses. And while its most promising use is to search for a vaccine and treatments, these will take a long time. The main reason for this somewhat pessimistic conclusion is inadequate data. The problem in the current crisis is that there is, on the one hand, not suitable enough (that is, unbiased and sufficient) data to train AI models to predict and diagnose COVID-19. Most of the studies that have trained AI models to diagnose COVID-19 from CT scans or X-rays have made use of small, biased, and unrepresentative samples from China. Many of these studies are not (yet) published in peer-reviewed journals.

On the other hand, the global impact and focus on the pandemic have resulted in too much data. There is too much noisy social media data associated with COVID-19, which, as the failure of Google Flu Trends, illustrated more than five years ago. This failure is dissected by Lazer and colleagues in a 2014 paper in Science, in which they identified the noisy social media data as upending big data hubris and algorithm dynamics. These factors currently also bedevil efforts to track COVID-19 using big data from social media. Furthermore, and perhaps more importantly, the systemic shock which the outbreak has caused has led to a deluge of outlier data. In essence, COVID-19 is a massive unique event. This sudden deluge of new data is invalidating almost all prediction models in economics, finance, and business. The consequence is that many industries are going to be pulling the humans back into the forecasting chair that had been taken from them by the models.

So, while we will not likely see AI in prediction and diagnoses during the current COVID-19 pandemic, we are likely to see the growing use of AI for social control. In contrast to AIs limitations in prediction and diagnoses due to data problems, no such problems exist in using surveillance technology. The use of mass surveillance to enforce lockdown and isolation measures in China, including infrared cameras to identify potentially infected persons in public, has been well documented. These have not been limited to China, but are being adopted by many western democracies, including the USA, UK, Germany, and Spain. Here, it is not so much public infrared cameras that are used but rather personal mobile phone data that are being requested by governments.

Moreover, many developing economies are following suit. OneZero has compiled a list of at least 25 countries that by mid-April 2020 had resorted to surveillance technologies to track compliance and enforce social distancing measures. Many of these violate data privacy norms. These include developing countries such as Argentina, Brazil, Ecuador, India, Indonesia, Iran, Kenya, Pakistan, Russia, South Africa, and Thailand. In the case of South Africa, the country is reported to have contracted a Singapore-based AI company to implement a real-time contact tracing and communication system. Singapore is using an app called TraceTogether, which sends out warnings if social distancing limits are breached.

In addition to social control and compliance measuring, AI systems via apps and mobile devices can also help health authorities to manage. According to Petropoulos, these can enable patients to receive real-time waiting-time information from their medical providers, to provide people with advice and updates about their medical condition without them having to visit a hospital in person, and to notify individuals of potential infection hotspots in real-time so those areas can be avoided.

Social control, and the public information that can be spread via mobile devices, can be beneficial so long as we do not have a vaccine against the virus causing COVID-19. Without a vaccine, governments are left to resort to flatten the epidemiological curve, so as to help the healthcare industry not to be overwhelmed by a sudden increase in patients. And while lockdowns and social distancing measures can be effective to reduce the speed at which the virus spread, they come at an exorbitant economic cost and, therefore, at some time, will have to be phased out.

To limit the danger that there will be a rebound in infections once restrictions are lifted, it may be necessary for large scale diagnostic testing to identify those still infected and keep them in quarantine. In this approach, AI surveillance tools can be valuable. Large scale diagnostic testing is also necessary to fill in the data-gap that characterizes knowledge on the extent and fatality of the coronavirus. It is not known accurately how many people are in fact infected and how many are asymptomatic. A study in Science suggested that up to 86 percent of all infections may be undocumented. If this is accurate, then there are two important implications, one bad and one good news. One, the pandemic may easily rebound once lockdowns are lifted. Two, the virus may not be as lethal as is thought. In this regard, The Economist points out, If millions of people were infected weeks ago without dying, the virus must be less deadly than official data suggest.

The contribution of surveillance technology comes with one substantial risk: that once the outbreak is over, that erosion of data privacy would not be reversed, and that governments would continue to keep intrusive tabs ontheir populations. They can even potentially use the data obtained in the fight against COVID-19 for other purposes.

This risk of using AI in the fight against COVID-19 is perhaps reflective of the general risk in using AI. AI has both positive and negative impacts. There will always be trade-offs. For instance, if we consider the Sustainable Development Goals (SDGs) broadly, a recent survey published in Nature Communicationsemphasized that AI can enable the accomplishment of 134 targets across all the goals, but it may also inhibit 59 targets. AI can do good, but it can also do bad.

Take two more examples of how AI can do both good and bad at the same time. While NLP algorithms may warn against the possible outbreak of an epidemic by mining written reports on social media and online news, a recent study found that to train a standard NLP model to do this using Graphics Processing Unit (GPU) hardware, emits 626,155 pounds of CO2. This is five times more than an average car emits in its lifetime (120,000 lbs.). Another example is that AI-driven automation may raise productivity and firm efficiency, but may increase unemployment and poor-quality jobs (gigs), with higher poverty and inequality as outcomes.

Hence, the authors in Nature Communications recommend that the fast development of AI needs to be supported by the necessary regulatory insight and oversight for AI-based technologies to enable sustainable development. Failure to do so could result in gaps in transparency, safety, and ethical standards.

The key point is that we need to limit the potential adverse consequences of AI, and we need to do so through adequate governance of AI.

Having identified current efforts to harness AI against COVID-19, and having noted their promises, limitations, and potential pitfalls, it remains to identify the priorities for developing countries.

Developing countries are already having to deal with the economic fallout of the pandemic. As Hausmann argues, with revenues, trade, and investments dropping, developing countries would need to increase their indebtedness massively if they are to implement basic healthcare support and social distancing measures against the disease. They are losing policy space precisely when they need it the most. Therefore, prioritization of resources is vital.

Developing countries should prioritize their scarce resources on propping up their health sectors and providing social security to their citizens. In essence, they should not be investing their resources in AI in the hope of improving hospital efficiencies, or in finding a vaccine.

Although AI can be helpful in finding a vaccine, developing countries, and particularly those in Africa, are largely lagging in terms of AI research and development capability. As I document elsewhere, around 30 companies in three regions, North America, the EU, and China, perform the vast bulk of research, patenting (93%), as well as receives the bulk (more than 90 percent) of venture capital funding for AI.

This is not to say that developing countries have no interest in harnessing AI to find a vaccine they do, and this illustrates that such a vaccine is a global public good. Scott Barret has put forth the concept of a single-best effort public good, which can be applied to the search for a vaccine for COVID-19. In the case of a single-best effort public good, it can be produced by one or a few countries for the benefit of all countries. Thus, while developing countries should not be spending resources on finding pharmaceutical solutions to the crisis through AI, they should be part of a global coalition to harness the AI capabilities of high-income economies and China in this respect. What should be avoided is an uncoordinated response, an AI arms race between countries and regions, and uncertainty about the distribution of and access to such a vaccine.

Developing countries should not be spending resources on trying to find pharmaceutical solutions to the crisis, but should be part of a global coalition to harness the AI capabilities of high-income economies and China to find a vaccine and treatments

Developing countries should also partake in the gathering and building of large public databases on which to train AI. The costs of doing so are small, and the potential benefits, given the need for unbiased and representative data on the pandemic is high. It should be seen as an investment against future pandemics.

Finally, in terms of surveillance, AI, in combination with testing, may help developing countries to ease restrictions and lockdowns earlier. But as was discussed, this will come at the risk of compromised data privacy a price that may have to be paid for public health and the re-opening of economies.

How developing countries go about their AI-based surveillance and testing will be crucial. Developing country governments and the global community need to ensure adherence to the highest ethical standards and transparency. If they do not, then they may face the prospect that people will lose what little trust they had in government, which will, as Ienca and Vayena pointed out, make people less likely to follow public-health advice or recommendations and more likely to have poorer health outcomes.

For the developing countries of Africa, this makes it imperative that they ratify the African Unions Convention on Cyber Security and Personal Data Protection the Malabo Convention as soon as possible. On two countries have so far done this. Consistent with this convention, they should stop limiting internet access, internet censorship, and trying to restrict digital information flows.

Developing countries still face a substantial digital divide, and the worlds poorest region, Sub-Saharan Africa, face a particularly daunting challenge it currently contributes less than 1% of worlds digital knowledge production.The COVID-19 crisis, and its likely long-term consequences in terms of accelerating automation, online trade, reshoring as well as increasing the market power of large incumbent digital platforms, should spur on these countries to see the current crisis as an opportunity to speed up their digitalization, and to leverage from domestic and international sources the funding to invest in the long-run upgrading of data infrastructures and skills.

Note from the editors: Towards Data Science is a Medium publication primarily based on the study of data science and machine learning. We are not health professionals or epidemiologists, and the opinions of this article should not be interpreted as professional advice. To learn more about the coronavirus pandemic, you can click here.

Excerpt from:
Artificial Intelligence, COVID-19, and Developing Countries: Priorities and Trade-Offs - Elemental