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Will the future of work be ethical? Perspectives from MIT Technology Review




In June, TechCrunch Ethicist in Residence Greg M. Epstein attended EmTech Next, a conference organized by the MIT Technology Review. The conference, which took place at MIT’s famous Media Lab, examined how AI and robotics are changing the future of work.

Greg’s essay, Will the Future of Work Be Ethical? reflects on his experiences at the conference, which produced what he calls “a religious crisis, despite the fact that I am not just a confirmed atheist but a professional one as well.” In it, Greg explores themes of inequality, inclusion and what it means to work in technology ethically, within a capitalist system and market economy.

Accompanying the story for Extra Crunch are a series of in-depth interviews Greg conducted around the conference, with scholars, journalists, founders and attendees.

Below he speaks to two key organizers: Gideon Lichfield, the editor in chief of the MIT Technology Review, and Karen Hao, its artificial intelligence reporter. Lichfield led the creative process of choosing speakers and framing panels and discussions at the EmTech Next conference, and both Lichfield and Hao spoke and moderated key discussions.

Gideon Lichfield is the editor in chief at MIT Technology Review. Image via MIT Technology Review

Greg Epstein: I want to first understand how you see your job — what impact are you really looking to have?

Gideon Lichfield: I frame this as an aspiration. Most of the tech journalism, most of the tech media industry that exists, is born in some way of the era just before the dot-com boom. When there was a lot of optimism about technology. And so I saw its role as being to talk about everything that technology makes possible. Sometimes in a very negative sense. More often in a positive sense. You know, all the wonderful ways in which tech will change our lives. So there was a lot of cheerleading in those days.

In more recent years, there has been a lot of backlash, a lot of fear, a lot of dystopia, a lot of all of the ways in which tech is threatening us. The way I’ve formulated the mission for Tech Review would be to say, technology is a human activity. It’s not good or bad inherently. It’s what we make of it.

The way that we get technology that has fewer toxic effects and more beneficial ones is for the people who build it, use it, and regulate it to make well informed decisions about it, and for them to understand each other better. And I said the role of a tech publication like Tech Review, one that is under a university like MIT, probably uniquely among tech publications, we’re positioned to make that our job. To try to influence those people by informing them better and instigating conversations among them. And that’s part of the reason we do events like this. So that ultimately better decisions get taken and technology has more beneficial effects. So that’s like the high level aspiration. How do we measure that day to day? That’s an ongoing question. But that’s the goal.

Yeah, I mean, I would imagine you measure it qualitatively. In the sense that… What I see when I look at a conference like this is, I see an editorial vision, right? I mean that I’m imagining that you and your staff have a lot of sort of editorial meetings where you set, you know, what are the key themes that we really need to explore. What do we need to inform people about, right?


What do you want people to take away from this conference then?

A lot of the people in the audience work at medium and large companies. And they’re thinking about…what effect does automation and AI going to have in their companies? How should it affect their workplace culture? How should it affect their high end decisions? How should it affect their technology investments? And I think the goal for me is, or for us is, that they come away from this conference with a rounded picture of the different factors that can play a role.

There are no clear answers. But they ought to be able to think in an informed and in a nuanced way. If we’re talking about automating some processes, or contracting out more of what we do to a gig work style platform, or different ways we might train people on our workforce or help them adapt to new job opportunities, or if we’re thinking about laying people off versus retraining them. All of the different implications that that has, and all the decisions you can take around that, we want them to think about that in a useful way so that they can take those decisions well.

You’re already speaking, as you said, to a lot of the people who are winning, and who are here getting themselves more educated and therefore more likely to just continue to win. How do you weigh where to push them to fundamentally change the way they do things, versus getting them to incrementally change?

That’s an interesting question. I don’t know that we can push people to fundamentally change. We’re not a labor movement. What we can do is put people from labor movements in front of them and have those people speak to them and say, “Hey, this is the consequences that the decisions you’re taking are having on the people we represent.” Part of the difficulty with this conversation has been that it has been taking place, up till now, mainly among the people who understand the technology and its consequences. Which with was the people building it and then a small group of scholars studying it. Over the last two or three years I’ve gone to conferences like ours and other people’s, where issues of technology ethics are being discussed. Initially it really was only the tech people and the business people who were there. And now you’re starting to see more representation. From labor, from community organizations, from minority groups. But it’s taken a while, I think, for the understanding of those issues to percolate and then people in those organizations to take on the cause and say, yeah, this is something we have to care about.

In some ways this is a tech ethics conference. If you labeled it as such, would that dramatically affect the attendance? Would you get fewer of the actual business people to come to a tech ethics conference rather than a conference that’s about tech but that happened to take on ethical issues?

Yeah, because I think they would say it’s not for them.


Business people want to know, what are the risks to me? What are the opportunities for me? What are the things I need to think about to stay ahead of the game? The case we can make is [about the] ethical considerations are part of that calculus. You have to think about what are the risks going to be to you of, you know, getting rid of all your workforce and relying on contract workers. What does that do to those workers and how does that play back in terms of a risk to you?

Yes, you’ve got Mary Gray, Charles Isbell, and others here with serious ethical messages.

What about the idea of giving back versus taking less? There was an L.A. Times op ed recently, by Joseph Menn, about how it’s time for tech to give back. It talked about how 20% of Harvard Law grads go into public service after their graduation but if you look at engineering graduates, the percentage is smaller than that. But even going beyond that perspective, Anand Giridharadas, popular author and critic of contemporary capitalism, might say that while we like to talk about “giving back,” what is really important is for big tech to take less. In other words: pay more taxes. Break up their companies so they’re not monopolies. To maybe pay taxes on robots, that sort of thing. What’s your perspective?

I don’t have a view on either of those things. I think the interesting question is really, what can motivate tech companies, what can motivate anybody who’s winning a lot in this economy, to either give back or take less? It’s about what causes people who are benefiting from the current situation to feel they need to also ensure other people are benefiting.

Maybe one way to talk about this is to raise a question I’ve seen you raise: what the hell is tech ethics anyway? I would say there isn’t a tech ethics. Not in the philosophy sense your background is from. There is a movement. There is a set of questions around it, around what should technology companies’ responsibility be? And there’s a movement to try to answer those questions.

A bunch of the technologies that have emerged in the last couple of decades were thought of as being good, as being beneficial. Mainly because they were thought of as being democratizing. And there was this very naïve Western viewpoint that said if we put technology and power in the hands of the people they will necessarily do wise and good things with it. And that will benefit everybody.

And these technologies, including the web, social media, smart phones, you could include digital cameras, you could include consumer genetic testing, all things that put a lot more power in the hands of the people, have turned out to be capable of having toxic effects as well.

That took everybody by surprise. And the reason that has raised a conversation around tech ethics is that it also happens that a lot of those technologies are ones in which the nature of the technology favors the emergence of a dominant player. Because of network effects or because they require lots of data. And so the conversation has been, what is the responsibility of that dominant player to design the technology in such a way that it has fewer of these harmful effects? And that again is partly because the forces that in the past might have constrained those effects, or imposed rules, are not moving fast enough. It’s the tech makers who understand this stuff. Policy makers, and civil society have been slower to catch up to what the effects are. They’re starting to now.

This is what you are seeing now in the election campaign: a lot of the leading candidates have platforms that are about the use of technology and about breaking up big tech. That would have been unthinkable a year or two ago.

So the discussion about tech ethics is essentially saying these companies grew too fast, too quickly. What is their responsibility to slow themselves down before everybody else catches up?

Another piece that interests me is how sometimes the “giving back,” the generosity of big tech companies or tech billionaires, or whatever it is, can end up being a smokescreen. A way to ultimately persuade people not to regulate. Not to take their own power back as a people. Is there a level of tech generosity that is actually harmful in that sense?

I suppose. It depends on the context. If all that’s happening is corporate social responsibility drives that involve dropping money into different places, but there isn’t any consideration of the consequences of the technology itself those companies are building and their other actions, then sure, it’s a problem. But it’s also hard to say giving billions of dollars to a particular cause is bad, unless what is happening is that then the government is shirking its responsibility to fund those causes because it’s coming out of the private sector. I can certainly see the U.S. being particularly susceptible to this dynamic, where government sheds responsibility. But I don’t think we’re necessarily there yet.

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Man paralyzed from neck down uses AI brain implants to write out text messages




Video A combination of brain implants and a neural network helped a 65-year-old man paralyzed from the neck down type out text messages on a computer at 90 characters per minute, faster than any other known brain-machine interface.

The patient, referred to as T5 in a research paper published [preprint] in Nature on Wednesday, is the first person to test the technology, which was developed by a team of researchers led by America’s Stanford University.

Two widgets were attached to the surface of T5’s brain; the devices featured hundreds of fine electrodes that penetrated about a millimetre into the patient’s gray matter. The test subject was then asked to imagine writing out 572 sentences over the course of three days. These text passages contained all the letters of the alphabet as well as punctuation marks. T5 was asked to represent spaces in between words using the greater than symbol, >.

Signals from the electrodes were then given to a recurrent neural network as input. The model was trained to map each specific reading from T5’s brain to the corresponding character as output. The brain wave patterns recorded from thinking about handwriting the letter ‘a’, for example, were distinct from the ones produced when imagining writing the letter ‘b’. Thus, the software could be trained to associate the signals for ‘a’ with the letter ‘a’, and so on, so that as the patient thought about writing each character in a sentence, the neural net would decode the train of brain signals into the desired characters.

With a data set of 31,472 characters, the machine learning algorithm was able to learn how to decode T5’s brain signals to each character he was trying to write correctly about 94 per cent of the time. The characters were then displayed so he was able to communicate.

Here’s a gentle video explaining the experiment.

Youtube Video

Unfortunately, there’s no delete button in this system; T5 had to push on even if he had made a mistake, such as imagining transcribing the wrong letter or punctuation mark. The character error rate was reduced from six per cent to 3.4 per cent by implementing an auto-correct feature. It’s about as accurate as today’s state-of-the-art speech-to-text systems, the researchers claimed.

It should be noted that the character error rate for free typing, when T5 was not transcribing text given by the researchers, was higher at 8.54 per cent and reduced to 2.25 per cent when an auto-correcting language model was used.

“Together, these results suggest that, even years after paralysis, the neural representation of handwriting in the motor cortex is probably strong enough to be useful for a BCI,” the team wrote, referring to a brain-computer interface. T5 was paralyzed due to a spinal cord injury, but the part of his brain that controls movement is still intact.

John Ngai, director of the US National Institutes of Health’s BRAIN Initiative, who was not directly involved in the research, called the study “an important milestone” for BCIs and machine learning algorithms. “This knowledge is providing a critical foundation for improving the lives of others with neurological injuries and disorders,” he said in a statement. The NIH, a government organization, helped fund the research.

Not a fit for all

Although the study seems promising, the team admitted there are a lot of challenges to overcome before this kind of technology can be commercialized or otherwise used by many more people. First of all, it has only been demonstrated on one person so far. The team will have to, as the tech stands today, retrain their model for each individual’s brain signals, and the performance may not be consistent from patient to patient.

“Why performance varies from person to person is still an unknown question,” Frank Willett, lead author of the study and a research scientist at Stanford’s Neural Prosthetics Translational Laboratory, told The Register.

“One cause is likely that the sensors sometimes record from different numbers of neurons – so sometimes when the sensor is placed into a person’s brain, it is particularly ‘hot’ and records a lot of neurons, while other times it does not. This is an open question in the field, and designing sensors that can always record many neurons is an important goal that others are working on.”

The academics also continuously retrained the system on T5’s brain signals to calibrate the software before they conducted experiments. Willett said that a system used in the real-world would have to work on minimal training data and that users shouldn’t have to retrain the machines every day.

“To translate the technology into a real product, it needs to be streamlined – the user should be able to use the BCI without needing to take too much time to train it,” he said.

“So we need to improve the algorithms so that they can work well with only a little bit of training data. In addition, it should be smart enough to automatically track how neural activity changes over time, so that the user does not have to pause to retrain the system each day.”

To translate the technology into a real product, it needs to be streamlined

The invasive nature of the electrodes is also an ssue; they have to stay implanted in a patient’s brain and will have to be made out of a material that is durable and safe. “Finally, the microelectrode device should be wireless and fully implanted,” Willett added. The software must also be able to run on a desktop computer or smartphone: it’s no good having to lug around heavy custom equipment.

“It is important to recognize that the current system is a proof of concept that a high-performance handwriting BCI is possible (in a single participant); it is not yet a complete, clinically viable system,” the paper concluded.

“More work is needed to demonstrate high performance in additional people, expand the character set (for example, capital letters), enable text editing and deletion, and maintain robustness to changes in neural activity without interrupting the user for decoder retraining. More broadly, intracortical microelectrode array technology is still maturing, and requires further demonstrations of longevity, safety and efficacy before widespread clinical adoption.” ®

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A Swiss Blockchain-Based Analytical Platform for the Cryptocurrency Market: Meet Dohrnii

A Swiss Blockchain-Based Analytical Platform for the Cryptocurrency Market: Meet Dohrnii

A Swiss Blockchain-Based Analytical Platform for the Cryptocurrency Market: Meet DohrniiA new project has long started exploring the use cases of AI for trading and is set on a mission to become a pioneer in bringing them to the cryptocurrency market. Today, we will take a closer look at how trading has evolved over the years and where the cryptocurrency industry stands less than a

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A new project has long started exploring the use cases of AI for trading and is set on a mission to become a pioneer in bringing them to the cryptocurrency market. Today, we will take a closer look at how trading has evolved over the years and where the cryptocurrency industry stands less than a decade since its inception.

The Development of Trading Tech Over the Years

If you think back to how the cradle of trading Wall Street was operating on the 80s – with DOS-based computers with green numbers and black screens and phones being the pinnacles of technology at the time – it is mindblowing what tools are available today even to those who do not trade professionally. Globalization has shaken the trading industry at its core – since the 1970s, computational algorithms and simulations such as the Monte Carlo method have been evolving, with the new century marking a drastic revolution in their capabilities and application scopes.

New and Emerging Investing Trends

In 1971, the first electronic stock market was launched by NASDAQ. It was a revolutionary concept that was regarded as a major step towards the future of the investing sector. Shortly after in 1980, online trading followed, allowing brokers to communicate with their clients digitally and to facilitate buy and sell orders directly. Then, the internet emerged, allowing everyone to conduct thorough research on companies and new investing opportunities easily accessible at their fingertips.

Parallel to these advancements, trading technology focused on the analysis of the markets was rapidly evolving. Algorithmic trading, which uses programmatic rules to analyze the markets, ultimately giving traders the power to execute orders exponentially quicker and with less bias than human operators are able to, bridged the gap between informational technology and investing, forming a never ending duo (Source: Stacker). More recently, companies like Wealthfront and Betterment introduced the first robo advisors, which allowed for a humanless financial planning and investing and laid out the foundations for a computer-driven future of the trading sector. AI, blockchain and cryptocurrencies followed, bringing us to where we stand today.

However, as a novel sector, the cryptocurrency market is still trailing behind in terms of analytical technology that is available to the traders. The analysis tools used traditionally in trading are rarely applicable to crypto due to the fundamental differences to the stock market and the inherent volatility of the industry. Many old school traders believe it is impossible to come up with reliable models that can be applied for cryptocurrency trading.  Surprisingly, recent research states otherwise – the truth is that data is the fundament that can enable the creation of statistically reliable models – even in cryptocurrency trading. That is, if you had a close to unlimited capabilities of gathering and analyzing a variety of market data. While you might think this is unlikely, technology has come a long way – particularly in the areas of Artificial Intelligence and its application to trading. Big companies such as BlackRock and their portfolio management software Aladdin have long started to stretch the boundaries of the potential technology can bring within the trading ecosystem. Such software is developed over a prolonged period of time by a large team of experts and is perfected continuously to become reliable. As such, the access to such software is greatly limited to the average investor, presenting the trading scene with asymmetries and one-sided power in favor of the wealthiest.

Dohrnii Takes the Initiative

The Dohrnii ecosystem combines a digital crypto academy, an analytical trading platform and a trading module, forming a comprehensive trading environment for crypto traders who wish to get into cryptocurrency trading or to bring their skillset to a new level. Each trader is delivered a personalized experience along their journey – from the starting onboarding process, the skill of traders is evaluated and a custom educational program is compiled for their profile. As they progress and start trading, their preferences and performance are also analyzed, allowing for Dohrnii to design personalized investment advice such as portfolio adjustments and deliver them to the traders through the robo advisor. What is more, the traders have access to a wide variety of tools that are unique to the cryptocurrency trading scene – from advanced market analysis to trading signals and price predictions, Dohrnii introduces features that were once reserved to the biggest investors on the stock markets to the average crypto trader.

The technology that is turning the wheels of the Dohrnii ecosystem is where the magic happens. By using the latest advancements in Artificial Intelligence and blockchain, Dohrnii is making tools that used to be available only to the biggest investment companies and hedge funds accessible to the average trader, thereby democratizing fintech technology and bringing the market into a natural equilibrium. This equilibrium is of utmost importance, as it will dissolve the current situation of a partial monopoly caused by the discrepancies in the access to advanced trading technology, which translates in much better advantage for several key players.

The Dohrnii Foundation is a non-profit organization based in Zug, Switzerland. It was founded in 2020 by a team of professionals with longstanding experience in multiple areas, all of whom with one common goal – to transform the world of cryptocurrency trading from a black box to an understandable discipline everyone has the ability to comprehend. The experts behind the Dohrnii Foundation have a diversified skill set, ranging from finance, trading, fintech, technology and blockchain, forming the fundamental backbone required for the creation of the Dohrnii ecosystem.

If you are interested in learning more about the Dohrnii project, the tools the ecosystem is offering to the traders and the innovative technology behind it, visit

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AI-powered identity access management platform Authomize raises $16M




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Cloud-based authorization startup Authomize today announced that it raised $16 million in series A funding led by Innovation Endeavors, bringing the startup’s total raised to $22 million to date. CEO and cofounder Dotan Bar Noy says that the capital will be used to support Authomize’s R&D and hiring efforts this year, as expansion ramps up.

One study found that companies consider implementing adequate identity governance and administration (IGA) practices to be among the least urgent tasks when it comes to securing the cloud. That’s despite the fact that, according to LastPass, 82% of IT professionals at small and mid-size businesses say identity challenges and poor practices pose risks to their employers.

Authomize, which emerged from stealth in June 2020, aims to address IGA challenges by delivering a complete view of apps across cloud environments. The company’s platform is designed to reduce the burden on IT teams by providing prescriptive, corrective suggestions and securing identities, revealing the right level of permissions and managing risk to ensure compliance.

“As security has evolved from endpoints and networks, attention has increasingly moved to identity and access management, and specifically the authorization space. Many of the CISOs and CIOs we spoke with expressed the need for a system that would secure and manage permissions from a single platform. They took access decisions based on hunches, not data, and when they tried to take data-driven decisions, they found out that the data was outdated. Additionally, most, if not all, of the process has been manually managed, making the IT and security teams the bottleneck for growth,” Noy told VentureBeat in an interview via email.

Authomize’s secret sauce is a technology called Smart Groups that aggregates data from enterprise systems in real time and infers the right-sized permissions. Using this data in tandem with graph neural networksunsupervised learning methods, evolutionary systems, and quantum-inspired algorithms, the platform offers action and process automation recommendations.

AI-powered recommendations

Using AI, Authomize detects relationships between identities and company assets throughout an organization’s clouds. The platform offers an inventory of access policies, blocking unintended access with guardrails and alerting on anomalies and risks. In practice, Authomize constructs a set of policies for each identity-asset relationship. It performs continuous access modeling, self-correcting as it incorporates new inputs like actual usage, activities, and decisions.

Of course, Authomize isn’t the only company in the market claiming to automate away IGA. ForgeRock, for instance, recently raised $93.5 million to further develop its products that tap AI and machine learning to streamline activities like approving access requests, performing certifications, and predicting what access should be provisioned to users.

But Authomize has the backing of notable investor M12 (Microsoft’s venture fund), Entrée Capital, and Blumberg Capital, along with acting and former CIOs, CISOs, and advisers from Okta, Splunk, ServiceNow, Fidelity, and Rubrik. Several undisclosed partners use the company’s product in production, Authomize claims — including an organization with 5,000 employees that tapped Smart Groups to cut its roughly 50,000 Microsoft Office 365 entitlements by 95%. And annual recurring revenue growth is expected to hit 600% during 2021.

Authomize recently launched an integration with the Microsoft Graph API to provide explainable, prescriptive recommendations for Microsoft services permissions. Via the API, Authomize can evaluate customers’ organization structure and authorization details, including role assignments, group security settings, SharePoint sites, OneDrive files access details, calendar sharing information, applications, and service principal access scopes and settings.

“Our technology is allowing teams to make authorization decisions based on accurate and updated data, and we also automate day-to-day processes to reduce IT burden … Authomize currently secures more than 7 million identities and hundreds of millions of assets, and our solution is deployed across dozens of customers,” Noy said. “Using our proprietary [platform], organizations can now strike a balance between security and IT, ensuring human and machine identity have only the permission they need. Our technology is built to connect easily to the entire organization stack and help solve the increasing complexity security, and IT teams face while reducing the overall operational burden.”

Authomize, which is based in Tel Aviv, Israel, has 22 full-time employees. It expects to have more than 55 by the end of the year as it expands its R&D teams to develop new entitlement eligibility engine and automation capabilities and increases its sales and marketing operations in North America.


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10 cool tech events you shouldn’t miss out on this June




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