The Download: inside the Vitalism movement, and why AI's "memory" is a privacy problem Meet the Vitalists: the hardcore longevity enthusiasts who believe death is "wrong" Last April, an excited crowd gathered at a compound in Berkeley, California, for a three-day event called the Vitalist Bay Summit. It was part of a longer, two-month residency that hosted various events to explore tools--from drug regulation to cryonics--that might be deployed in the fight against death. One of the main goals, though, was to spread the word of Vitalism, a somewhat radical movement established by Nathan Cheng and his colleague Adam Gries a few years ago. Consider it longevity for the most hardcore adherents--a sweeping mission to which nothing short of total devotion will do. Although interest in longevity has certainly taken off in recent years, not everyone in the broader longevity space shares Vitalists' commitment to actually making death obsolete. And the Vitalists feel that momentum is building, not just for the science of aging and the development of lifespan-extending therapies, but for the acceptance of their philosophy that .

FinTech refers to any idea or innovation that improves or optimizes the way individuals or companies conduct financial activities. Early FinTech concentrated on developing add-on products to complement existing financial services. This combination of finance and technology has spawned a slew of valuable goods and services that redefine financial services and make them more accessible to the general public. Some of these products and services include insurance aggregators, mobile wallets, AI investment management advisers, peer-to-peer (P2P) lending and crowdfunding tools, and platforms for trading financial assets. The cutting-edge solutions that contributed to such technologies include Blockchain, Deep Learning, and Artificial Intelligence (AI).

It is no secret that privacy issues and concerns sit at the forefront of business actions, online activity, and government decisions. For example, artificial intelligence (AI) is now used in several of the applications that drive the digital workplace. This is why many enterprise managers are starting to question the implications and ramifications this will have for privacy. And this is not merely speculation; privacy issues stemming from the use of AI raise serious concerns for businesses and consumers. Privacy issues also arise in response to the data breaches, scandals, as well as personal data leaks that have considerably eroded confidence in information technology and information systems.

Both traditional automakers and Internet companies have long been involved in the development of automated driving technology and have achieved certain results. In 2017, GM equipped the Super Cruise automatic driving function on the Cadillac CT6. In April of the same year, Baidu released the Apollo self-driving vehicles platform. In July, Audi officially released the Audi A8, and its automated driving system Traffic Jam Pilot reached Level 3. In October, Waymo completed the first social road test of Level 4 self-driving vehicles for the first time. In April 2018, Baidu launched the test ride of Level 4 Baidu driverless bus "Apolon," and announced the automated driving bus entered the mass production phase in July. The rapid development of automated driving technology has also led to a lot of discussions - most of which are concerned about the widespread use of automated driving technology.

The stunning successes of artificial intelligence would not have happened without the availability of massive amounts of data, whether it's smart speakers in the home or personalized book recommendations. And the spread of AI into new areas of the economy, such as AI-driven marketing and self-driving vehicles, has been driving the collection of ever more data. These large databases are amassing a wide variety of information, some of it sensitive and personally identifiable. All that data in one place makes such databases tempting targets, ratcheting up the risk of privacy breaches. The general public is largely wary of AI's data-hungry ways.

As cybersecurity and privacy researchers, we believe that the relationship between AI and data privacy is more nuanced. The spread of AI raises a number of privacy concerns, most of which people may not even be aware. But in a twist, AI can also help mitigate many of these privacy problems. Privacy risks from AI stem not just from the mass collection of personal data, but from the deep neural network models that power most of today's artificial intelligence. Data isn't vulnerable just from database breaches, but from "leaks" in the models that reveal the data on which they were trained. Deep neural networks – which are a collection of algorithms designed to spot patterns in data – consist of many layers.

The stunning successes of artificial intelligence would not have happened without the availability of massive amounts of data, whether its smart speakers in the home or personalized book recommendations. And the spread of AI into new areas of the economy, such as AI-driven marketing and self driving vehicles, has been driving the collection of ever more data. These large databases are amassing a wide variety of information, some of it sensitive and personally identifiable. All that data in one place makes such databases tempting targets, ratcheting up the risk of privacy breaches. The general public is largely wary of AI's data-hungry ways.

Artificial intelligence – more specifically, the machine learning (ML) subset of AI – has a number of privacy problems. Not only does ML require vast amounts of data for the training process, but the derived system is also provided with access to even greater volumes of data as part of the inference processing while in operation. These AI systems need to access and "consume" huge amounts of data in order to exist and, in many use cases, the data involved is private: faces, medical records, financial data, location information, biometrics, personal records, and communications. Preserving privacy and security in these systems is a great challenge. The problem grows in sensitivity as the public becomes more aware of the consequences of their privacy being violated and misused.

But privacy remains an unresolved challenge in the industry, particularly with regard to compliance and regulation https://lnkd.in/d7vE5cs. We at Centre Of Excellence For Protection Of Human Rights In Cyberspace (CEPHRC) and TeleLaw Project Of PTLB can help in formulating a #technolegal policy for AI and related fields for interested stakeholders. Collaborate with us in 2020 and let us together create a wonderful world.

Artificial intelligence promises to transform -- and indeed, has already transformed -- entire industries, from civic planning and health care to cybersecurity. But privacy remains an unsolved challenge in the industry, particularly where compliance and regulation are concerned. Recent controversies put the problem into sharp relief. The Royal Free London NHS Foundation Trust, a division of the U.K.'s National Health Service based in London, provided Alphabet's DeepMind with data on 1.6 million patients without their consent. Google -- whose health data-sharing partnership with Ascension became the subject of scrutiny in November -- abandoned plans to publish scans of chest X-rays over concerns that they contained personally identifiable information.