Tag: big data

Ageism, Wageism, and Transhumanism – Part II – Article by Zach Richardson

Ageism, Wageism, and Transhumanism – Part II – Article by Zach Richardson

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Zach Richardson

“I bargained with Life for a penny,
And Life would pay no more,
However I begged at evening
When I counted my scanty store;

For Life is just an employer,
He gives you what you ask,
But once you have set the wages,
Why, you must bear the task.

I worked for a menial’s hire,
Only to learn, dismayed,
That any wage I had asked of Life,
Life would have paid.”

– Jessie B. Rittenhouse

Author’s Note: There is an acronym thrown around on the legal advice subreddit that has the unfortunate name IANAL, which stands for “I am not a lawyer.” That acronym applies here. Nothing I say in these essays should be considered as anything other than the speculations of a layman.

~ Zach Richardson, Director of Publication, United States Transhumanist Party, December 2021

The “Larry” character from this series’ introductory essay is based on a personal anecdotal work experience. Larry’s function in that organization was “order entry”. He was a steady worker and could reliably enter 20-25 orders per hour into our management system. Larry was not quite as fast as Roger, but he could keep up nicely and did his share… at first. Each year passed, and he got a little bit slower at clicking around, at keeping track of the volume of data needed for each entry and at maintaining accuracy.

Roger, on the other hand, was a speed demon. 40 orders per hour was not uncommon, and he seemed laser-focused at work. He was the better of the two, and everyone knew it. Both he and Larry had been with the company since the beginning.

COVID-19 rolled around, and our industry suffered a massive drop in revenue; our clients were manufacturing plants who were all temporarily ceasing operations. It was time for layoffs, and Larry was let go due to lack of performance. Everyone knew it was because he was getting slow and forgetful. Everyone knew it was because he was too old.

The company was lucky because they had a good standard by which they could measure the two on, and clearly demonstrate a decline in performance. They had metrics on employee workflow going back several years.

The fact that these metrics had been around since the beginning and were unchanging was key in the company’s realization that it was legally defensible to lay off Larry.

They could show a decline in performance and say:

“Age had nothing to do with it! We don’t know why he was getting worse at his job, but he just was, and our metrics showed it, so we were unfortunately forced to let him go.”

Metrics, metrics, metrics.

The key defense against any claim of ageism was enabled this last decade by how ridiculously easy it has become to collect and store data. Metrics can be set as targets that are just out of reach or are only being met by 70% of your workforce, and then managers can fire those they don’t like, while retaining those they do.

Consider speed limits on our highways; probably half of all drivers break the limits, but only a handful of them are pulled over and ticketed.

Metrics allowed large companies to fire disparately since the mid-2000s, and the ease of data collection via company-owned devices is enabling small companies to do the same.

This is not good, but it is also not bad. It is not fair, but it is also not unfair. It is just business.

So, how does a company proceed with removing an unwanted elderly worker without getting tied up in a lawsuit?

Well, there’s an art to it, and in the next few essays I’ll explain a few of the key concrete particulars by looking at successfully and unsuccessfully litigated examples in case law.

** I am not a lawyer, and nothing in this series of essays should be taken as legal advice**

Zach Richardson is Director of Publication for the U.S. Transhumanist Party. 

State of AI 2020 – Article by Pavel Ilin

State of AI 2020 – Article by Pavel Ilin

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Pavel Ilin

This summary is prepared based on the State of AI Report 2020, which was crafted by Nathan Benaich and Ian Hogarth.

The AI industry is very diverse in its application, and it’s going through a transformation from the magical-wand stage to the plateau of adequate development. Let’s take a look at what is happening in the AI industry.

Research

We haven’t come up with new super-smart algorithms. Progress in model performance keeps being driven by big computational budgets and huge data sets. Training of the GPT-3 language model, with its 175 billion parameters, cost approximately $10 million. At the same time larger models require less data to achieve the same level of performance. With a deep-learning approach we are getting close to the point when the cost of training will grow outrageous with incrementally smaller improvements of the model.

An important fact is that the code base of most artificial intelligence systems remains closed. Only 15% of papers publish their code. This raises a lot of concerns about reproducibility and AI safety. AI explainability remains a critical issue for AI safety research; there are promising avenues of exploration such as Asymmetric Shapley Values, but so far it’s unknown how AI systems make decisions. 

Natural language processing (NLP) models successfully simulate common scenes and linguistics, but they fail dramatically with understanding problems and context and forming knowledge. 

Talent

Talented people with skills in math and computer science are the drivers of the progress in the AI field. More and more US professors are being recruited by tech companies. This affects the quality of education that US universities can provide. We already can see a decline in the level of entrepreneurship among recent graduates. At the same time Universities are creating AI-related degree programs.

The US keeps its position as the main attractor of talented individuals. For example China contributes to the talent pool of AI developers, but after publication of their first results, talented people are most likely to move to the US. 90% of international PhD graduates stay and work in US universities and corporations. Demand for AI talent remains much higher than supply, even despite COVID-19’s impact on market growth.  

Industry

AI keeps progressing not only on a theoretical and research level. Many real world applications are already in use, and they are affecting the industries in various ways.

New drugs are being designed by AI, and they are already in clinical trials. For example AI-designed drugs for OCD treatment are out for testing in Japan. AI drug-discovery startups keep raising funds. Also big pharma is teaming up with startups around preserving privacy during drug discovery. For example OpenMined uses federated learning to preserve privacy with medical data. Viz.ai presented the first product which was approved by the Centers for Medicare and Medicaid Services in the US. Their product analyzes tomography scans and alerts specialists who can treat patients before they receive damage that leads to the long-term disability. 

Progress in self-driving cars stays limited. Only 3 companies in California have permission to conduct testing of self-driving cars without a safety driver. Self-driving mileage remains microscopic compared to human drivers (2,874,950 miles for self-driving cars versus 390,313,739,000 miles for humans). The research and development process for self-driving cars remains very expensive. The major companies in this field raised around $7 billion since July 2019. Tesla chose to approach gradually adding self-driving features to its cars, but human drivers still remain in the loop. Recent approaches such as supervised learning do not perform well enough. To make dramatic breakthroughs, new approaches are required.

Computer vision unlocks faster accident and disaster recovery intervention. It also reduces the amount of human hours spent using a microscope, which could lead to acceleration of development processes and reduction of product costs.

AI drives sales and at the same time reduces costs in supply chains and manufacturing. Robotic process automation and computer vision are the most commonly deployed techniques in the enterprise. Speech, natural language generation, and physical robots are the least common. Recently IBM partnered with health insurance company Humana. IBM implemented natural language understanding (NLU) software which is already live and handles calls. It not only redirects calls to the different queues; it’s able to answer basic questions, such as “How much will the copay be to visit a specific specialist?” without human intervention.

Modern AI, in order to perform well, requires a lot of computing resources. Specialized AI hardware keeps progressing, and companies are now presenting second generations of their products. Graphcore M2000 offers faster training time to drop the cost of state-of-the-art models. Google’s new TPU v4 delivers up to a 3.7x training speedup over their TPU v3. NVIDIA will not rest either; it has achieved up to 2.5x training speedups with the new A100 GPU vs V100. Increasing interest towards machine learning devOps is a signal that the industry shifting its focus from how to build models to how to run them.

Despite the COVID-19 pandemic, investments keep coming into the industry. Private funding rounds of greater than $15 million for the AI-first companies remain strong.

Politics

Usage of AI for facial recognition tasks is extremely common around the world. Around half of the world allows facial recognition. This has become a recognizable political and ethical problem, especially when use of this technology leads to the wrong arrests. There were two highly publicized cases of wrong arrest in the US (which is probably just a tip of the iceberg). In May 2019, Detroit police arrested Michael Oliver who was wrongly accused of a felony for supposedly reaching into a teacher’s vehicle, grabbing a cellphone and throwing it, cracking the screen, and breaking the case. In January 2020, Detroit police arrested Robert Williams as a shoplifter who allegedly stole five watches from Midtown’s trendy Shinola store in October 2018. In both cases charges were dismissed but harm was done. 

Industry took a more thoughtful approach as a reaction to the AI mistakes. Microsoft deleted its database of 10 million faces, Amazon announced a one-year pause on letting the police use its facial recognition tool Rekognition. IBM announced it would sunset its general purpose facial recognition products. Washington State in the US introduced requirements to acquire warrants to run facial recognition scans. The ImageNet, a popular image database, is making an effort toward reduction of the biases in its image collections.

As Deep Fake technology produces more and more realistic media, it becomes illegal to use in certain states in the US. California passed a law, AB 730, aimed at deep fakes, which criminalizes distributing audio or video that gives a false, damaging impression of a politician’s words or action. Many other US state bills have been passed, addressing different risks. For example Virginia law amends current criminal law on revenge porn to include computer-generated pornography.

The US government keeps pursuing implementation of the military AI systems. DARPA organised a virtual dogfighting tournament where various AI systems would compete with each other and a human fighter pilot from the US military.

AI nationalism is on the rise. Countries tend to pursue protectionist policies to scrutinize acquisitions of AI companies by the players from other countries.

Every year AI plays a more and more noticeable part in our lives. It becomes cheaper, and you learn how to do new things. But we have to remember that at the moment AI is still a tool. And there are some philosophical and methodological difficulties which we have to overcome before it will be possible to deliberate about the potential sentience of the AI. It’s very important for the policy makers to make informed decisions based on how technology actually works and not on magical understanding formed based on popular sci-fi.

Pavel Ilin is Secretary of the United States Transhumanist Party. 

 

A Transhumanist Opinion on Privacy

A Transhumanist Opinion on Privacy

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Ryan Starr

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Privacy is a favorite topic of mine. Maintaining individual privacy is a crucial element in free society. Yet there are many who want to invade it for personal or political gain. As our digital fingerprint becomes a part of our notion of self, how do we maintain our personal privacy on an inherently impersonal network of data? Where do we draw that line on what is private, and how do we enforce it? These are questions that are difficult to answer when looking at a short-term perspective. However, if we look further into the probable future, we can create a plan that helps protect the privacy of citizens today and for generations to come. By taking into account the almost certain physical merger of human biology and technology, the answer becomes clear. Our electronic data should be treated as part of our bodily autonomy.

The explosive success of social media has shown that we already view ourselves as partly digital entities. Where we go, what we eat, and who we are with is proudly displayed in cyberspace for eternity. But beyond that we store unique data about ourselves “securely” on the internet. Bank accounts, tax returns, even medical information are filed away on a server somewhere and specifically identified as us. It’s no longer solely what we chose to let people see. We are physical and digital beings, and it is time we view these two sides as one before we take the next step into enhanced humanity.

Subdermal storage of electronic data is here, and its storage capabilities will expand rapidly. Soon we will be able to store a lot more than just access codes for our doors. It is hard to speculate exactly what people will chose to keep stored this way, and there may even come a time when what we see and hear is automatically stored this way. But before we go too far into what will be stored, we must understand how this information is accessed in present time. These implants are currently based in NFC technology. Near-Field Communication is a method of storing and transmitting data wirelessly within a very short distance. Yes, “wireless” is the key word. It means that if I can connect my NFC tag to my smart phone by just waiving my hand close to it (usually within an inch or so), then technically someone else can, too. While current antenna limitations and the discreetness of where a person’s tag is implanted create a highly secure method of storage, advances in technology will eventually make it easier to access the individual. This is why it is urgent we develop a streamlined policy for privacy.

The current Transhumanist position is that personally collected intellectual property, whether stored digitally or organically, is the property of the individual. As such, it should be protected from unauthorized search and download. The current platform also states that each individual has the freedom to enhance their own body as they like so long as it doesn’t negatively impact others. However, it does not specify what qualifies as a negative impact or how to prevent it. Morphological freedom is a double-edged sword. A person can a person enhance their ability to access information on themselves, but they can also use it to access others. It is entirely feasible enhancements will be created that allow a person to hack another. And collecting personal data isn’t the only risk with that. What if the hacking victim has an artificial heart or an implanted insulin pump? The hacker could potentially access the code the medical device is operating with and change or delete it, ultimately leading to death. Another scenario might be hacking into someone’s enhanced sensory abilities. Much like in the novel Ender’s Game, a person can access another to see what they see. This ability can be abused countless ways ranging from government surveillance to sexual voyeurism. While this is still firmly within the realm of science fiction, a transhuman society will need to create laws to protect against these person-to-person invasions of privacy.

Now let’s consider mass data collection. Proximity beacons could easily and cheaply be scattered across stores and cities to function as passive collection points much like overhead cameras are today. Retail stands to gain significantly from this technology, especially if they are allowed access to intimate knowledge about customers. Government intelligence gathering also stands to benefit from this capability. Levels of adrenaline, dopamine, and oxytocin stored for personal health analysis could be taken and paired with location data to put together an invasive picture of how people are feeling in a certain situation. Far more can be learned and exploited when discreetly collected biodata is merged with publicly observable activity.

In my mind, these are concerns that should be addressed sooner than later. If we take the appropriate steps to preserve personal privacy in all domains, we can make a positive impact that will last into the 22nd century.

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Ryan Starr is the leader of the Transhumanist Party of Colorado. This article was originally published on his blog, and has been republished here with his permission.