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Surveillance

AI Powered State Surveillance On Rise, COVID-19 Used as Scapegoat

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As governments around the world come to terms with the impact of COVID-19, citizens are facing new draconian restrictions which include travel bans and forced quarantine. These are the types of restrictions which are normally associated with totalitarian and/or communist states. The merits of the efficacy of these efforts deserves debate, but there is no debate to be had. Rules are in place, and citizens must obey.

What is more concerning than this complete temporary loss of freedom, are the new laws, and privacy-blocking regulations which are being implemented. We are talking about a complete loss of privacy, and an erosion of basic human rights.

Previously, country wide monitoring using using facing recognition technology via a camera network was used to identify and track the movements of people on terrorist watchlists. That same technology is now being applied to anyone who is deemed infected or has previously traveled within the past two weeks.

Below we highlight some of the Governments that have taken advantage of the current pandemic, to implement systems designed for long-term surveillance.

Russia

Russia has taken advantage of COVID-19 to fast-forward their plans to blanket the country with a massive facial recognition system. The system was rolled out earlier this year with major public backlash. Privacy advocates were filing lawsuits to attempt to reduce the amount of potential government surveillance.

With fear at its side President Vladimir Putin was able to curb this backlash. Russia argued that public safety was the number one concern and that Facial Recognition does not invade peoples privacy. These proceedings took place on the pretext of the COVID-19 outbreak.

Today the surveillance network has been activated. The 170,000 camera-wide system is now programmed to track the movements of anyone who leaves quarantine or self-isolation. Now that the system is in place, the odds of rolling back the technology are next to none.

With this technology is place, Russia which has a proven history of tracking and killing uncooperative journalists, has increased their arsenal to monitor the movements of anyone who makes a disparaging comment regarding President Vladimir Putin or the state.

Russia has solidified their totalitarianism control, other countries are following in these footsteps.

Israel

Less than two weeks ago the Israeli government approved tracking the movement of people who are deemed ‘suspected’ of infection of COVID-19. Tracking would be via the data on your mobile phone. Most people carry their mobile phones on them at all times, which enables governments to be informed of your current location.

What is especially concerning is that this bill was passed in an overnight sitting of the cabinet, bypassing parliamentary approval.

This enables the full-time tracking of all Israeli citizens. The Association for Civil Rights in Israel labeled this as a “dangerous precedent and a slippery slope”. These were also powers which were previously only enabled for counter-terrorism operations.

While currently implemented to track ‘suspected’ COVID-19 patients. With this new law in place, future implementations of the technology are up to interpretation.

Hong Kong

What if you were forced to wear a wristband to continuously update the government to your whereabouts? While previously this was used on criminals., March 19th, is when Hong Kong enacted regulations to use it on non-criminals, used precisely to track passengers and to place them in forced quarantine.

The wristbands are connected to a smartphone app and will be used to make sure people actually stay at home. Refusal to wear the wristband or leaving the confines of your home can result in a six month prison sentence.

The long-term precedent of a government entity being able to control the movement and tracking the locations of its citizens should be of concern to anyone who believes in privacy. This is especially true in the case of Hong Kong which has long fought China to retain its independence.

United States

The United States was slow to wake up to COVID-19, due to the initial outrageous claim by the Trump administration that the outbreak was a liberal hoax. The tide has since shifted with the federal government proposing and then reversing from a quarantine of New York state, among other measures to fight the outbreak.

A recent development which should concern us is the United States communicating with tech giants such as Facebook, Google, Twitter, Uber, Apple, and IBM to share data about all of its users. Currently the data is to remain anonymous, but once location tracking is in place, it’s only several lines of code to disable user privacy and anonymity.

The current use case is using machine learning to decipher the location of future hotspots in order to better prepare healthcare workers. Monitoring of this big data should be implemented by a non-profit entity in order to ensure that the data is used specifically to track outbreaks.

This conglomerate of companies has the potential to enable unfiltered access to every single facet of a persons life. From all social media communication, to geo-location tracking. It remains to be seen if democratic values will hold with more government surveillance requests. In the meantime there is cause for concern.

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Antoine Tardif is a Futurist who is passionate about the future of AI and robotics. He is the CEO of BlockVentures.com, and has invested in over 50 AI & blockchain projects. He is also the Co-Founder of Securities.io a news website focusing on digital securities, and is a founding partner of unite.ai

Autonomous Vehicles

Supply Chains after Covid-19: How Autonomous Solutions are Changing the Game

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Early measures by the material handling industry to curb the coronavirus pandemic saw border and plant closures all over the world. While for machine and vehicle manufacturers in eastern Europe and China production is in full swing again, the rest of Europe, North America and other western countries are struggling to get back to their pre-Covid-19 production strength.

Restrictions in freight transport across Europe are still very noticeable and are causing bottlenecks in supply chains. The strict stay-at-home-orders imposed in most European countries to contain the pandemic have had and are having a major impact on industrial production as the personnel are simply missing on site.

Security measures like keeping minimum distance or wearing masks are proving to be an organizational challenge for many production facilities around the world. In order to be able to comply with the safety requirements, in many premises only half of the workforce is allowed on-site, or the production line is divided into shifts. This in turn is restricting the flow of goods. Even when components exist, they stockpile, and cannot be integrated due to a lack of staff or time for those on reduced activity.

After the crisis, the industry will face new challenges. There is already speculation about a trend moving away from globalization towards regionalization. It is not necessarily the sourcing of production that could be affected by a possible regionalization, but rather warehouse management. Regardless of restricted supply chains, access to material inventory is essential for every production line. As a lesson-learned from the Covid-19 crisis, we could see a move from large central warehouses to smaller regional warehouses.

The automotive industry, for instance, was hit hard by supply shortages due to restrictions stemming from the pandemic. Automotive OEMs and their suppliers have long and complex supply chains with many steps in the production process. After the experienced bottlenecks, their follow-up measures might include a diversification of suppliers, as well as the decentralization of inventories in order to maintain agility in case of a crisis.

This presupposes digitalization of warehouse management: if existing stockpiling data is used rationally, transparency in the entire supply chain can easily be created. This would mean everyone involved could use existing data to optimize their processes. This requires intelligent warehouse management systems (WMS) and intelligent solutions for material handling to work hand-in-hand.

Automated guided vehicles (AGVs) are not a novelty in in-house material handling processes but their evolution could hold the key to the industry’s future. Since their introduction, technologies in autonomous vehicles have developed rapidly, enabling the transport of people in complex environments. Bringing this level of intelligence to industrial vehicles hails the next era of logistics automation: new AGV generations accessing complex outdoor environments are a real game changer and could potentially become more attractive after the Covid-19 crisis. As these vehicles become increasingly deployed in dynamic environments without infrastructure, these technologies have quickly migrated from manufacturing applications to supporting warehousing for manufacturing and distribution.

The process automation in supply chains – part of the so-called Industry 4.0 – will play a significant role. It could allow companies to keep or even reduce overall logistics operational costs, and eventually maintain a minimal operational flow even in times of crisis.

Rethinking the industrial supply chain: intelligence is key

The autonomous tow tractor TractEasy by autonomous technology leader EasyMile is a perfect example of this new generation. It masters the automation of outdoor and intralogistics processes on factory premises, logistics centers and airports. The company is currently demonstrating the maturity of these autonomous tow tractors at automotive supplier Peugeot Société Anonyme (PSA)’s manufacturing plant in Sochaux, France. Operated by GEODIS, PSA is using the tractor to find opportunities to optimize costs in the flows on its site.

The impact of the ongoing crisis has revealed the fragility of existing supply chains. Companies are reassessing large and complex procurement networks. Ultimately, the Covid -19 pandemic is putting supply chains to the test, but global supply chains should be prepared for crises as part of risk management anyway. The sheer number of natural disasters in recent years has meant that the international supply chains have been repeatedly overhauled. From this point of view, the Covid-19 crisis is an example of unpredictability that supply chains have to adapt to in order to develop.

What is certain is that the industry is on an upward trend toward more sustainable and stable industrial ecosystems. Automation is a concept that will play a major role in these future considerations, from manufacturers to logistic operators across the globe.

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COVID-19

Stefano Pacifico, and David Heeger, Co-Founders of Epistemic AI – Interview Series

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Epistemic AI employs state-of-the-art Natural Language Processing (NLP), machine learning and deep learning algorithms to map relations among a growing body of biomedical knowledge, from multiple public and private sources, including text documents and databases. Through a process of Knowledge Mapping, users’ work interactively with the platform to map and understand subsets of biomedical knowledge, which reveals concepts and relationships and that are otherwise missed with traditional search.

We interviewed both Co-Founders of Epistemic AI to discuss these latest advances.

Stefano Pacifico comes from 10+ years in applied AI and NLP development. Formerly at Bloomberg, where he spent 7 years, and was at Elemental Cognition before starting Epistemic.

David Heeger is a Silver Professor of data science and neuroscience at NYU, and has spent his career bridging computer science, AI and bioscience. He is a member of the National Academy of Sciences. As founders they bring together the expertise of building applied large-scale AI and NLP systems for understanding large collections of knowledge, with expertise in computational biology and biomedical science from years of research in the area.

What is it that introduced and attracted you to AI and Natural Language Processing (NLP)?

Stefano Pacifico: When I was in college in Rome, and AI was not popular at all (in fact it was very fringe), I asked my then advisor what specialization I should have taken among those available. He said: “If you want to make money, Software Engineering and Databases, but if you want to be weird but very advanced, then choose Artificial Intelligence”. I was sold at “weird”. I then started working on knowledge representation and reasoning to study how autonomous agents could play soccer or rescue people. Then two realizations made me fall in love with NLP: first, autonomous agents might have to communicate with natural language among themselves! Second, building formal knowledge bases by hand is hard, while natural language (in text) already provides the largest knowledge base of all. I know today these might seem obvious observations, but they were not as mainstream before.

What was the inspiration behind launching Epistemic AI?

Stefano Pacifico: I am going to make a bold claim. Nobody today has adequate tooling to understand and connect the knowledge present in large, ever-growing collections of documents and data. I had previously worked on that problem in the world of finance. Think of news, financial statements, pricing data, corporate actions, filings etc. I found that problem intoxicating. And of course, it’s a difficult problem; and an important one!  When I met my co-founder, Dr. David Heeger, we spent quite a bit of time evaluating startup opportunities in the biomedical industry. When we realized the sheer volume of information generated in this field, it’s as if everything fell in its right place. Biomedical ​researchers struggle with information overload, while attempting to grapple with the vast and rapidly expanding base of biomedical knowledge, including documents (e.g., papers, patents, clinical trials) and databases (e.g., genes, proteins, pathways, drugs, diseases, medical terms). This is a major pain point for researchers and, with no appropriate solution available, they are forced to use basic search tools (PubMed and Google Scholar) and explore manually-curated databases. These tools are suitable for finding documents matching keywords (e.g., a single gene or a published journal paper), but not for acquiring comprehensive knowledge about a topic area or subdomain (e.g., COVID-19), or for interpreting the results of high throughput biology experiments, such as gene sequencing, protein expression, or screening chemical compounds. We started Epistemic AI with the idea to address this problem with a platform that allows them to iteratively:

  1. Shorten the time to gather information and build comprehensive knowledge maps
  2. Surface cross-disciplinary information​ that can be otherwise difficult to find (real discoveries often come from looking into the white space between disciplines);
  3. Identify causal hypotheses by finding paths and missing links in your knowledge map​.

What are some of both the public and private sources that are used to map these relations?

Stefano Pacifico: At this time, we are ingesting all the publicly available sources that we can get our hands on, including Pubmed and clinicaltrials.gov. We ingest databases of genes, drugs, diseases and their interactions. We also include private data sources for select clients, but we are not at liberty to disclose any details yet.

What type of machine learning technologies are used for the knowledge mapping?

Stefano Pacifico: One of the deeply held beliefs at Epistemic AI is that zealotry is not helpful for building products. Building an architecture integrating several machine learning techniques was a decision made early on, and those range from Knowledge Representation to Transformer models, through graph embeddings, but include also simpler models like regressions and random forests. Each component is as simple as it needs to be, but no simpler. While we believe to have already built NLP components that are state-of-the-art for certain tasks, we don’t shy away from simpler baseline models when possible.

Can you name some of the companies, non-profits, or academic institutions that are using the Epistemic platform?

Stefano Pacifico: While I’d love to, we have not agreed with our users to do so. I can say that we had people signing up from very high-profile institutions in all three segments (companies, non-profits, and academic institutions). Additionally, we intend to keep the platform free for academic/non-profit purposes.

How does Epistemic assist researchers in Identifying central nervous system (CNS) and other disease-specific biomarkers?

Dr. David Heeger: Neuroscience is a very highly interdisciplinary field including molecular and cellular biology and genomics, but also psychology, chemistry, and principles of physics, engineering, and mathematics. It’s so broad that nobody can be an expert at all of it. Researchers at academic institutions and pharma/biotech companies are forced to specialize. But we know that the important insights are interdisciplinary, combining knowledge from the sub-specialties. The AI-powered software platform that we’re building enables everyone to be much more interdisciplinary, to see the connections between their individual subarea of expertise and other topics, and to identify new hypotheses. This is especially important in neuroscience because it is such a highly interdisciplinary field to begin with. The function and dysfunction of the human brain is the most difficult problem that science has ever faced. We are on a mission to change the way that biomedical scientists work and even how they think.

Epistemic also enables the discovery of genetic mechanisms of CNS disorders. Can you walk us through how this works?

Dr. David Heeger: Most neurological diseases, psychiatric illnesses, and developmental disorders do not have a simple explanation in terms of genetic differences. There are a handful of syndromic disorders for which a specific mutation is known to cause the disorder. But that’s not typically the case. There are hundreds of genetic differences, for example, that have been associated with autism spectrum disorders (ASD). There is some understanding for some of these genes about the functions they serve in terms of basic biology. For example, some of the genes associated with ASD hold synapses together in the brain (note, however, that the same genes typically perform different functions in other organ systems in the body). But there’s very little understanding about how these genetic differences can explain the complex suite of behavioral differences exhibited by individuals with ASD. To make matters worse, two individuals with the same genetic difference may have completely different outcomes, one diagnosed with ASD and the other, not. And two individuals with completely different genetic profiles may have the same outcome with very similar behavioral deficits. To understand all this requires making the connection from genomics and molecular biology to cellular neuroscience (how do the genetic differences cause individual neurons to function differently) and then to systems neuroscience (how do those differences in cellular function cause networks of large numbers of interconnected neurons to function differently) and then to psychology (how do those differences in neural network function cause differences in cognition, emotion, and behavior). And all of this needs to be understood from a developmental perspective. A genetic difference may cause a deficit in a particular aspect of neural function. But the brain doesn’t just sit there and take it. Brains are highly adaptive. If there’s a missing or broken mechanism then the brain will develop differently to compensate as much as possible. This compensation might be molecular, for example, upregulating another synaptic receptor to replace the function of a broken synaptic receptor. Or the compensation might be behavioral. The end result depends not only on the initial genetic difference but also on the various attempts to compensate relying on other molecular, cellular, circuit, systems, and behavioral mechanisms.

No individual has the knowledge to understand all this. We all need help. The AI-powered software platform that we’re building enables everyone to collect and link all the relevant biomedical knowledge, to see the connections and to identify new hypotheses.

How are biopharma and academic institutions using Epistemic to tackle the COVID-19 challenge?

Stefano Pacifico: We have released a public version of our platform that includes COVID specific datasets and is freely accessible to anyone doing research on COVID-19. It is available at https://covid.epistemic.ai

What are some of the other diseases or genetic issues that Epistemic have been used for?

Stefano Pacifico: We have collaborated with autism researchers and are most recently putting together a new research effort for Cystic Fibrosis. But we are happy to collaborate with any other researchers or institutions that might need help with their research.

Is there anything else that you would like to share about Epistemic?

Stefano Pacifico: We are building a movement of people that want to change the way biomedical researchers work and think. We sincerely hope that many of your readers will want to join us!

Thank you both for taking the time to answer our questions. Readers who wish to learn more should visit Epistemic AI.

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Artificial Neural Networks

AI Models Struggle To Predict People’s Irregular Behavior During Covid-19 Pandemic

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Retail and service companies around the world make use of AI algorithms to predict customer behaviors, take stock of inventory, estimate marketing impacts, and detect possible instances of fraud. The machine learning models used to make these predictions are trained on patterns derived from the normal, everyday activity of people. Unfortunately, our day-to-day activity has changed during the coronavirus pandemic, and as MIT Technology Review reported current machine learning models are being thrown off as a result. The severity of the problem differs from company to company, but many models have been negatively impacted by the sudden change in people’s behavior over the course of the past few weeks.

When the coronavirus pandemic occurred, the purchasing habits of people shifted dramatically. Prior to the onset of the pandemic, the most commonly purchased objects were things like phone cases, phone chargers, headphones, kitchenware. After the start of the pandemic, Amazon’s top 10 search terms became things like Clorox wipes, Lysol spray, paper towels, hand sanitizer, face masks, and toilet paper. Over the course of the last week of February, the top Amazon searches all became related to products people required to shelter themselves from Covid-19. The correlation of Covid-19 related product searches/purchases and the spread of the disease is so reliable that it can be used to track the spread of the pandemic across different geographical regions. Yet machine learning models break down when the model’s input data is too different from the data used to train the model.

The volatility of the situation has made automation of supply chains and inventories difficult. Rael Cline, the CEO of London-based consultancy Nozzle,  explained that companies are trying to optimize for the demand of toiler paper one week ago, while “this week everyone wants to buy puzzles or gym equipment.”

Other companies have their own share of problems. One company provides investment recommendations based on the sentiment of various news articles, but because the sentiment of news articles at the moment is often more pessimistic than usual, the investing advice could be heavily skewed toward the negative. Meanwhile, a streaming video company utilized recommendation algorithms to suggest content to viewers, but as many people suddenly subscribed to the service their recommendations started to fall from the mark. Yet another company responsible for supplying retailers in India with condiments and sauces discovered bulk orders broke their predictive models.

Different companies are handling the problems caused by pandemic behavior patterns in different ways. Some companies are simply revising their estimates downward. People still continue to subscribe to Netflix and purchase products on Amazon, but they have cut back on luxury spending, postponing purchases on big-ticket items. In a sense, people’s spending behaviors can be conceived of as a contraction of their usual behavior.

Other companies have had to get more hand-on with their models and have engineers make important tweaks to the model and it’s training data. For example, Phrasee is an AI firm that utilizes natural language processing and generation models to create copy and advertisements for a variety of clients. Phrasee always has engineers check what text the model generates, and the company has begun manually filtering out certain phrases in its copy. Phrasee has decided to ban the generation of phrases that might encourage dangerous activities during a time of social distancing, phrases like “party wear”. They have also decided to restrict terms that could lead to anxiety, like “brace yourself”, “buckle up”, or “stock up”.

The Covid-19 crisis has demonstrated that freak events can throw off even highly-trained models that are typically reliable, as things can get much worse than the worst-case scenarios that are typically included within training data. Rajeev Sharma, CEO of AI consultancy Pactera Edge, explained to MIT Technology Review that machine learning models could be made more reliable by being trained on freak events like the Covid-19 pandemic and the Great Depression, in addition to the usual upwards and downwards fluctuations.

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