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Copyright: © Richard Jacobs
Niger Little-Poole, Director of Engineering at Sigma Ratings, delves into the thorny problem of financial security and explains how his company is working to deliver technology-based solutions to assess risk.
Sigma Ratings is an international compliance and regulatory risk rating agency in New York City, New York whose focus is on the design and architecture of a powerful AI-based rating platform. Their mission is to properly assess risk utilizing domain expertise and computer science, to rate and generate risk evaluations and scores for thousands of companies across the globe.
As our global economy has brought many financial companies and banks together for trading and services, security and trust are more important than ever. Niger Little-Poole explains how Sigma Ratings' software allows financial entities to get a better understanding of who they are working with and partnering within the business. With financial crimes, money laundering, and fraud, etc. on the rise, Sigma Ratings' proprietary software provides some comfort in turbulent times. And beyond searching for links to general crimes and infractions, Sigma Ratings' software can also detect links a bank may have to potentially nefarious groups or individuals, from oligarchs on a US government watch list to terrorists.
Little-Poole states that Sigma also seeks to provide full transparency on banks, and eventually financial companies, by exposing who they are actually owned by, in cases where shell companies or holding companies may be the true owners. As connections are important, it is critical to know who or what owns a bank or company to ensure that all entities in the chain are reputable and operating legally.
The director of engineering provides an overview of basic AML (anti-money laundering) and KYC (know your customer) initiatives in regard to compliance and regulation. Little-Poole states that regulations and protocols may be in place in 80% of a particular country's financial institutions while another country may have only 5% of their institutions under similar scrutiny.
Little-Poole discusses the complex issue of cybersecurity. Cybersecurity, simply defined, is the protection of computer systems and related hardware, software and electronic data from potential damage, corruption, misdirection of services, or theft. Little-Poole explains how hackers can utilize their hack to infiltrate a bank's system, offering the hackers a direct access line to pull money from accounts and then push it through other banking institutions and illegally launder it.
As financial crimes are rampant, the Sigma Ratings' director states that the solution must be technology-based, structured with machine learning, otherwise many financial crimes will continue to thrive, unseen by the watchful eye of regulators.
Joshua Scigala is the co-founder of the first real-time bitcoin/gold trading engine on the market.
Vaultoro is backing bitcoin with physical gold that is vaulted for the customers in top-tier Swiss gold Vaults. The technology behind Vaultoro makes it the most transparent exchange on the market, both publicly and privately audited. Vaultoro enables billions of people to start using the bitcoin payment network without the volatility risk.
Opu Labs aims to become the leading digital solutions provider for skincare measurement, management, and monitoring. They are building an advanced ecosystem that combines a machine learning AI, a clinic, a marketplace, and a community. Underpinning all of these features is blockchain technology. Its use allows patient data to be decentralized and freely accessed at scale.
It also incentivizes those involved to play an active role in our ecosystem: by rewarding them with our native ERC-223 tokens, OPU Coins. Opu Labs wants to give consumers better access to skincare expertise, provide skincare professionals with the technology needed to expand their capabilities, ease patient load, and reward those who make valuable contributions.
Dr. Joost Huizinga, a Ph.D. from the Evolving Artificial Intelligence Laboratory at the University of Wyoming, provides an informative overview of the advances and challenges in artificial intelligence (AI) learning and training.
Dr. Huizinga's interest and expertise are primarily focused in the areas of evolutionary robotics and algorithms, neural networks, learning algorithms and other elements of artificial intelligence. A portion of Huizinga's work involves neural networks and the challenge to improve them and make them more similar to the human brain. As neural networks are trained to classify images according to different categories, and learning algorithms then take command, the information about the images is still, unfortunately, scattered all throughout the neural networks. In contrast, the human brain has specific areas for various functions such as the visual cortex and motor cortex, etc., which provides a solid structural organization.
According to Dr. Huizinga, to have a fully connected neural network within the human brain, every neuron would need to be connected to every other neuron, which would necessitate a larger brain than normal in order to handle the vast network. Additionally, every connection would need the essential proteins that are required to build these types of connections. The Ph.D. explains one of his team's goals during their experimentation process—to have an evolutionary algorithm build the network itself, optimizing for not only performance, but also for a reduction in length, and number, of the connections within the network.
As a result, these artificial neural networks approach a more modular state and begin to functionally specialize. Huizinga explains that this structured specialization allows for higher efficiency in learning and thus more closely approximates a human's neural network organization. In regard to these applications as they are applied to robotics, Dr. Huizinga hopes to push neural networks to create a more advanced hierarchical structure for improved robotic functionality.
The AI Ph.D. provides an interesting comparative discussion on the similarities and differences between human brain processing and AI network processing, including how each response to external stimuli, processes the information and reacts to it. As Huizinga explains, when humans need to react quickly to external stimuli or an event or action, the human brain often accesses learned information from past events, which allows for more immediate, and appropriate, responses.
Doctor Huizinga states that the future of AI will be heavily based in teaching the AI to develop its own structures and essentially acquire the information and processes it needs to teach itself how to learn. Further, Huizinga stresses the importance of using AI to influence and train networks to exercise caution before making decisions, as human brains do, before rushing confidently into an action. This subtle training will allow for greater safety as autonomous functional products and robotic entities become mainstream.
Tracy Leparulo Founder and CEO at Untraceable, is an early crypto investor and Blockchain Futurist, she founded Untraceable in 2013 to help build and grow the crypto community.
Untraceable is the first event management and marketing agency within the blockchain and cryptocurrency community in Canada. We provide a full suite of services for events including logistics, marketing, and sponsorship. Our specialty is designing cutting-edge events by seamlessly integrating crypto ticketing, event apps, token creation, and innovative tech solutions to fit your needs.
Over the past 5 years, Untraceable has organized hundreds of events from the first Bitcoin Expo in Canada to the first Ethereum hackathon to ETHWaterloo (the world's largest Ethereum hackathon). Whether you're launching a blockchain company or planning a corporate conference, the Untraceable team will work with you to create an immersive and unforgettable event.
Blockmason is a growing team of accomplished developers and professionals from their respective industries. We leverage both specialists and generalists – including a deep bench of multi- lingual world travelers, diversified investors, technology mavens, competitive athletes, history buffs, chess enthusiasts – and worst of all, far too many overachievers with undergrad and graduate credentials from Harvard and Yale.
Lndr is a debt tracking system for friends and family were your debts are logged on the blockchain.
Dr. David Benrimoh, CEO and CMO of Aifred Health (aifred health.com), a mental health AI company, provides an interesting overview of the complexities within the mental health branch of medicine and the challenge to improve mental health diagnoses. Dr. Benrimoh is a physician currently training to be a psychiatrist. He is engaged in neuroimaging research at McGill, where he is completing his residency. Dr. Benrimoh is also pursuing a masters in neuroscience at University College, London.
Dr. Benrimoh's company, Aifred Health, is comprised of a diverse team of scientists and physicians, from neuroscientists, to computer scientists, biochemists and specialized physicians who work together to solve problems in the field of mental health. Aifred Health's primary objective is to solve the complex problem of mental health treatment selection.
Dr. Benrimoh details some of the current issues in mental health. The physician explains that while the demand for mental health services has grown exponentially, service providers, mental health professionals, and mental health clinics are at or nearing capacity, which may deprive those in need of the care they are seeking. But beyond the problem of meeting needs, Dr. Benrimoh states that the most critical problem is the simple fact that people do not recover or find relief from treatment fast enough.
As many sufferers can find relief when matched with the proper treatment, whether that is psychotherapy or drug therapy or a combination thereof, it is Benrimoh's mission to aid in the process of matching people with the appropriate treatment sooner, so they can recover. He expresses the importance of matching people with treatment right away because in some cases, especially regarding depression, waiting to receive treatment simply allows the problem to take hold. Benrimoh's goal is to match sufferers with the proper treatment 75% of the time, the first time they reach out for help.
The health CEO discusses the many problems that arise in the diagnosis of specific mental health disorders. Unfortunately, the range of symptoms is often so broad that some sufferers are offered improper treatment that may not provide them with the most efficient solution. Traditional questionnaires have long provided the basic information on sufferers to aid in the diagnosis of their mental health challenges. However, Dr. Benrimoh's team at Aifred Health seeks to use deep machine learning and AI to dig deeper, and pull out more significant data on sufferers to enable proper treatment to be assigned sooner, and hopefully bring a quicker end to the suffering.
By utilizing data from the NIMH (National Institutes of Mental Health) and other sources, Aifred Health's technological approach using AI is able to predict suicidal ideations with a 70% accuracy, from mental health population surveys. Additionally, their approach is able to consider data in regard to 4 different types of drugs and make assessments with an 80% accuracy as to whether the particular drug will be effective with a specific sufferer. Alarmingly, depression is the number one cause of disability worldwide and is the biggest disease on the planet—bar none. At any given time, 322,000,000 people are depressed, thus Dr. Benrimoh's primary focus is on the improvement of efficiency and efficacy of depression treatments.
Louis-Pierre Gravelle, a lawyer, engineer, and patent agent at Robic (robic.com) gives a thorough overview of the many issues that relate to patents and property rights in the ever-expanding field of artificial intelligence (AI).
Robic is a boutique IP firm based in Canada that specializes in the protection and commercialization of intellectual property (IP) rights and other intangible assets, and of course any necessary litigation and/or transaction support. Founded in 1892 by engineers, the Marion brothers, the firm established its longstanding reputation of excellence under the direction of its eponymous principal, Raymond A. Robic. Since its establishment 125 years ago, Robic has grown expansively and is now home to over 180 employees.
Gravelle's background in engineering and his particular interest in 3D printing has made him well suited for the countless AI issues and cases that are arising with hurried frequency in the advancing technology world. Gravelle states that AI patent applications are becoming commonplace, with the largest tech companies such as Microsoft, Google, and Amazon leading the way.
As much of the work in AI of years past was generated in academia, a preponderance of core information and many basic algorithms are currently part of the public domain. And this makes for an interesting set of circumstances as new products are developed that could be tied, in part, to public domain data and key information. Gravelle discusses the difficulties that can arise in the determination of whose patented process a particular AI product follows. Thus potential infringement of rights is a complex area that requires intensive research in order to establish a clear resolution of rights.
The law partner provides an overview of granted claims and infringement issues regarding IP and AI and how decisions are sometimes made in the process. For example, in the field of medical diagnostics with competitive companies that use AI engines to detect specific disease through in vivo or other diagnostics, it is often challenging to find the line where research crosses into infringement. And often times these are the areas where patent issues become lawsuits.
In regard to 3D printing, Gravelle discusses how materials such as circuits, metals, and powders that go into the finish and solidity of the products are becoming an area of great interest in the IP legal arena as well. Additionally, he explains how issues in the legal tech field are expanding rapidly. Legal tech, in basic terms, would be classified as any computer-aided tool that assists an individual working in the legal arena, such as tools used for natural language processing, etc.
As IP issues concerning AI and other intangible assets are increasing, Gravelle states that the complex issues that arise in regard to potential infringement are of great importance to rights holders.
John Weaver, an attorney at McLane Middleton who focuses heavily on emerging tech, including artificial intelligence (AI), self-driving vehicles, and drone technology, leads an informative discussion on the legal issues that will arise as emerging technologies infiltrate the marketplace.
Mr. Weaver works with clients from varying fields and assists them with best practices, helps them prepare and respond to legal issues, and assists with regulation compliance—especially privacy and data security regulations. Weaver explains how these compliance issues are particularly important with emerging tech such as AI and autonomous technology because personal data is the figurative fuel that fires the engine for these technologies. Thus, companies need to have a solid understanding of what they are legally allowed to do with the volume of personal data connected to various technologies.
The telecommunications and technology attorney lays out some of the legal issues that companies will face as the coming years bring more data via streaming, high-speed internet, and sophisticated 5G networks. Weaver gives an overview of blockchain transactions and the data privacy issues that are facing players in that industry. As blockchain involves lists of permanent records secured by cryptography, bundled data may sometimes be in violation of privacy laws.
As the use of blockchain expands, attorneys such as Weaver will be needed to help clients walk carefully through the minefield of prickly data privacy regulations that are required by various governments and organizations. Additionally, Weaver will delve into the current legal inadequacies within the confluence of intellectual property (IP) and AI laws. Specifically, he'll explain how advancing technologies will expose further limitations in IP as it relates to protected data versus public domain.
The technology attorney details how limited legal personhood, as is sometimes used to establish legal structures regarding corporations, will be used for autonomous vehicles as they begin to populate our roads and highways in higher numbers. Once established, manufacturers and users can worry less about liability issues and more about perfecting and using the technology that most current data predicts will ultimately make our roads safer.
David Fragale - Co-Founder and CPO at Atonomi joins Future Tech Podcast.
Atonomi - The Secure Ledger of Things Enabling Secure, Blockchain-based Cryptosecurity for IoT Transactions The Atonomi Network is a blockchain-based security solution to protect the Internet of Things and enable secure device-to-device autonomous transactions. The Atonomi Security Protocol validates device identity, tracks device reputation, and provides immutable blockchain ledgering of all transactions.
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