Realistic AI Clones are Here

May 14, 2021

Is this the future of our now ubiquitous Zoom meetings? PetaPixel tells us that “AI Can Now Turn You Into a Fully Digital, Realistic Talking Clone.” Startup Hour One’ technology can create one’s digital clone and put words in its mouth. The article shares a clip of an example avatar, that of YouTube personality Taryn Southern. Southern only had to speak and sing for about seven minutes in front of a green screen to establish the AI clone, which was then fed a script by Hour One. The result is convincing. We do note it seems that, at least so far, the clone must sit still and squarely face the “camera.” Curious readers can see more examples on the company’s website. We see how this could be useful, if only to stay in pajamas while one’s clone took virtual meetings, but what could bad actors do with the tech? Reporter Jaron Schneider writes:

“Hundreds of videos can be generated in a matter of minutes just by submitting text to the platform. A creator would not need to record any audio at all. On the plus side, it doesn’t look like it would be possible to create an AI person without this studio time, but it also means that it would theoretically be possible to obtain the AI version of Southern and input any texts into the program which the AI would read as though it were her. The ramifications of that are daunting. Still, Hour One argues that the benefits of its technologies outweigh the possible downsides. The company claims that with this technology, content creators will see a drastic reduction in the time and cost of video production to a matter of minutes. Additionally, a video can be created without a time-intensive routine to look presentable for the camera (AI Taryn jokes that she can now create new YouTube videos ‘without the real Taryn having to shower or leave her bed.’). Additionally, any AI clone can speak multiple languages which allow for greater distribution of content to more people around the world.”

The company admits the approach will work for some formats, like news broadcasts, better than for others, like stand-up comedy. Schneider cautions readers not to confuse this tool with deep fakes, which overlay someone’s face over existing footage. Hour One’s tech goes beyond that to create completely new content. Founded in 2019, the company is based in Tel Aviv, Israel.

Cynthia Murrell, May 14, 2021

Microsoft Partners Up for Smarter Security

May 13, 2021

I noted “Microsoft Partners with Darktrace to Help Customers Combat Cyber Threats with AI.” You may know that Microsoft has been the subject of some attention. No, I am not talking about Windows 10 updates which cause printers to become doorstops. Nope. I am not talking about the fate of a leaner, meaner version of Windows. Yep, I am making a reference to the SolarWinds’ misstep and the alleged manipulation of Microsoft Exchange Server to create a reprise of “waiting on line for fuel.” This was a popular side show in the Washington, DC, area in the mid-1970s.

How does Microsoft address its security PR challenge? There are white papers from Microsoft threat experts. There are meetings in DC ostensibly about JEDI but which may — just by happenstance — bring up the issue of security. No big deal, of course. And Microsoft forms new security-centric partnerships.

The partner mentioned in the write up is Darktrace. The company relies on technology somewhat related to the systems and methods packaged in the Autonomy content processing system. That technology included Bayesian methods, was at one time owned by Cambridge Neurodynamics, and licensed to Autonomy. (A summary of Autonomy is available at this link. The write up points out that Bayesian methods are centuries old and often criticized because humans have to set thresholds for some applications of the numerical recipes. Thus, outputs are not “objective” and can vary as the method iterates.) Darktrace’s origins are in Cambridge and some of the firm’s funding came from Michael Lynch-affiliated Invoke Capital. The firm’s Web page states:

Founded by celebrated technologist and entrepreneur, Dr Mike Lynch OBE, Invoke Capital founds, invests in and advises fast-growing fundamental technology companies in Europe. With deep expertise in identifying and commercializing artificial intelligence research and a close relationship with the University of Cambridge, Invoke exists to realize the commercial possibilities of Britain’s extraordinary science and deep technology base. Since 2012, Invoke has been instrumental in founding, creating and developing prominent technologies, and then finding the right teams to scale them into global businesses. Invoke’s companies include Darktrace, a world-leading cyber AI company that employs more than 1,500 people globally, Luminance, an award-winning machine learning platform for the legal industry, and AI fraud-detection engine, Featurespace. Invoke exited data-driven medicine experts, Sophia Genetics, in 2020.

{The Register provides a run down of some of the legal activity associated with Mr. Lynch at this link. )

The item presenting the tie up of Microsoft and Darktrace states:

Microsoft announced today a new partnership with Darktrace, a UK-based cyber security AI firm that works with customers to address threats using what it describes as “self-learning artificial intelligence”. Darktrace’s threat response system is designed to counter insider threats, espionage, supply chain attacks, phishing, and ransomware. The partnership between Microsoft and Darktrace is meant to give organizations an automated way of investigating threats across multiple platforms. Darktrace’s system works by learning the data within a specific environment as well as how users behave. The goal is to tell which activity is benign or malicious.

For more information about Darktrace, one can consult the firm’s Web site. For a different view, an entity with the handle OneWithCommonSense provides his/her assessment of the system. You can find that document (verified online on May 13, 2021) at this link.

Why is this interesting?

  1. The use of a system and method which may be related to how the Autonomy system operates may be an example how one mathematical method can be extended to a different suite of use cases; specifically, cyber security.
  2. The Darktrace disclosures about its technology make it clear that the technology is in the category of “artificial intelligence” or what I call smart software. Systems and methods which are more efficient, economical, and more effective are reasons why smart software is an important product category to watch.
  3. Darktrace (to my knowledge) may have the capability to recognize and issue an alert about SolarWinds-type incursions. Other cyber security firms’ smart software dropped the ball and many were blindsided by the subsequent Microsoft Exchange Server and shell exploits.

As a side note, Microsoft acquired the Fast Search & Transfer company after there were legal inquiries into the company. That was a company based in Norway. With the Darktrace deal, Microsoft is again looking offshore for solution to what on the surface seems to be the Achilles’ heel of the company’s product portfolio: Its operating system and related services.

Will Darktrace’s technology address the debilitating foot injury Microsoft has suffered? Worth watching because bad actors are having a field day with free ice cream as a result of the revelations related to Microsoft’s security engineering. Windows Defender may get an injection of a technology that caught Dr. Lynch’s eye. Quick is better in my opinion.

Stephen E Arnold, May 13, 2021

Believe It or Not: The First AI to Surpass Humans

May 12, 2021

Say what you will about other aspects of Google, you have to hand it to the company’s research arm. Developers at Google Brain, DeepMind, and the University of Toronto have developed the reinforcement-learning AI DreamerV2. According to Analytics India Magazine, “Now DeepMind’s New AI Agent Outperforms Humans” as measured by the Atari benchmark. The tech evolved from last year’s Dreamer agent created by the same team. It uses a world model, an approach that is more adept at forming generalizations than traditional trial-and-error machine learning processes. World models have not been as accurate as many other algorithms, however. Until now. Reporter Ambika Choudhury writes:

“Dreamer learns a world model from the past experience and efficiently learns far-sighted behaviors in its latent space by backpropagating value estimates back through imagined trajectories. DreamerV2 is the successor of the Dreamer agent. … This new agent works by learning a world model and uses it to train actor-critic behaviors purely from predicted trajectories. It is built upon the Recurrent State-Space Model (RSSM) — a latent dynamics model with both deterministic and stochastic components — allowing to predict a variety of possible futures as needed for robust planning, while remembering information over many time steps. The RSSM uses a Gated Recurrent Unit (GRU) to compute the deterministic recurrent states. DreamerV2 introduced two new techniques to RSSM. According to the researchers, these two techniques lead to a substantially more accurate world model for learning successful policies: [a] The first technique is to represent each image with multiple categorical variables instead of the Gaussian variables used by world models; [b] *The second new technique is KL balancing. This technique lets the predictions move faster toward the representations than vice versa.”

See the write-up for a chart of DreamerV2’s performance compared to previous world models. And all this on a single GPU. Curious readers can check out the team’s paper here. We believe.

Cynthia Murrell, May 12, 2021

Why Are AI Wizards Fessing Up?

May 10, 2021

I asked myself, “What’s up with the wizards explaining some of the information about the limitations of today’s artificial intelligence systems and methods?”


I noticed several write ups which are different from the greed infused marketing presentations about smart software.

The first article is an apologia. This term means, “a defense especially of one’s opinions, position, or actions,” as Merriam Webster asserts.”Fighting Algorithmic Bias in Artificial Intelligence” allows the title to indicate that algorithmic bias is indeed an issue. The algorithms are not narrowed to machine learning. Instead the title pops up to the umbrella term. Interesting. Here’s a passage which caught my attention:

From Black individuals being mislabeled as gorillas or a Google search for “Black girls” or “Latina girls” leading to adult content to medical devices working poorly for people with darker skin, it is evident that algorithms can be inherently discriminatory…

Okay, reasonably convincing. But what went wrong in the university courses providing the intellectual underpinnings for smart software? That’s a question that the write up emphasizes in a pull quote:

It’s not just that we need to change the algorithms or the systems; we need to change institutions and social structures. — Joy Lisi Rankin

How quickly do institutions and social structures change? Not too quickly where tenure and student employment goals are intertwined with judgment, ethics, and accountability I surmise.

The second article I noted contains the musings of an AI pioneer (Andrew Ng) as related to an IEEE writer. “Andrew Ng X Rays the Hype” seems to assert that “machine learning may work on test sets, but that’s a long way from real world use.” We’re not talking about AI. Andrew Ng is focusing on machine learning, the go to method for the Google-type company. The truth is presented this way:

“Those of us in machine learning are really good at doing well on a test set,” says machine learning pioneer Andrew Ng, “but unfortunately deploying a system takes more than doing well on a test set.”

The point is that a test is just that, an experiment. MBAs engage in spreadsheet fever behavior in order to generate numbers which flow or deliver what’s needed to get a bonus. The ML crowd gets a test set working and then, it seems, leaps into the real world of marketing and fund raising. With cash, those test sets become enshrined and provide the company’s secret sauce. What if the sauce is poisoned? Yeah, ethics, right?

The third write up is appears in an online information service which has done its share of AI cheerleading. “What I Learned from 25 Years of Machine Learning” is a life lessons-type write up. What did the TechTarget Data Science Central article learn?

“Learn” is not the word I would use to characterize a listicle. There are 11 “pieces of advice.” Okay, these must be the lessons. Please, navigate to the source document to review the Full Monty. I want to highlight three “learnings” expressed as “advice.”

The first gem I will highlight is “be friend with the IT department.” Maybe be friendly or be a friend of the IT department. The learning I gleaned from this “piece of advice” is use Grammarly or find an English major to proofread. Let’s consider the advice “be a friend of the IT department” and ask “Why?” The answer is that smart software can be computationally expensive, tough to set up, and a drain on existing on premises or cloud computing resources. The IT departments with which I am familiar are not friendly to non IT people who want to take time away from keeping the VP of sales’ laptop working. Data wizards are outsiders and the IT department may practice passive aggression to cause the smart software initiative to move slowly or not at all.

The second advice I want to flag is document. Yeah. The way the world of mathy things works is to try stuff. Try more stuff. Then try stuff suggested by a blogger. Once the process or numerical recipe works, the focus is not on documenting a journey. The laser beam of attention goes to hitting a deadline and hopefully getting a bonus, a promotion, or one of those Also Participated ribbons popular in the 1980s’ middle schools. As one of my long time tech wizards said, “Document? You wish.”

The third “module” of these learnings is “get precise metrics”. Okay but precision requires specific information. Who has specific information about the errors, gaps, timeliness, and statistical validity of the data one must use? Yep, good luck with that. Quick example: Due to my research for my National Cyber Crime Conference lectures, Google is now displaying ads for weapons, female bikini hauls, fried chicken sandwiches, and mega yachts. Why? Google’s method of determining what data to use from my online queries struggles because we were using one computer to research cyber crime (weapons), pornography stars on social media sites and the Dark Web, lunch (hence the chicken fetish), and money laundering. I mean how many mega yachts does one honest business person need with one new wife and handful of former spetsnaz professionals? Yeah, data and precise metrics. If the Google can’t do it, what are your chances, gentle reader.

Now back to the question: Why are AI wizards confessing their digital sins?” My answer:

  1. The increased scrutiny of Amazon, Apple, Facebook, et al bodes ill for these firms and their use of smart software to generate money. This is a variant of the MBAs’ spreadsheet fever.
  2. High profile AI “experts” want to put some space between them and the improvised roadside Congressional investigations. Bias is a heck of a lot easier to tie to math particularly when high profile ethics issues are making headlines in the Sioux Falls Argus Leader.
  3. The wizards want to be in the group of wizards who can say, “Look. I explained what should be done. Not my personal problem.”

Net net: AI has bought the mid tier consultant-speak, frothy financial confections, and behavior of a smart person who is one step ahead of an ATM user.

Stephen E Arnold, May 10. 2021

Xoogler Meredith Whittaker Explains How to Be Google Grade

May 7, 2021

I read the interview called “Ex-Googler Meredith Whittaker on Political Power in Tech, the Flaws of ‘The Social Dilemma,’ and More.” Very Silicon Valley. You will need to work through the transcript yourself. Here are the points I circled as checkpoints for being Google Grade. The phrase in my lingo means “How to keep your job at the GOOG.” I identified six behaviors; your mileage may vary.

  1. Be a white male.
  2. Float above the concerns of non-Google grade type people.
  3. Emulate senior Google leaders; for example, the affable, other directed Jeff Dean.
  4. Ethics. Ho ho ho. Embrace phenomenological existentialism within the Google context.
  5. Respond like a Pavlovian dog or pigeon when money and power are the payoff.
  6. Fight the impulse to be a contrarian.

And the interview ends on an interesting note. The Xoogler allegedly said:

It’s going to be really hard to repurpose that toward democratic, people-driven ends, given the consolidation of power that is right now dominating those infrastructures and given the neoliberal capitalist incentives that are driving those who dominate those infrastructures.

Maybe not hard, just too late.

Stephen E Arnold, May 7, 2021

Privacy Challenges with Android Contact Tracing App

May 6, 2021

Why are we not surprised? We learn from The Markup that “Google Promised its Contact Tracing App Was Completely Private—But it Wasn’t.” The COVID contact-tracing framework, developed in a unique partnership with Apple, was used by several government agencies in their official apps. Millions of citizens took CEOs Sundar Pichai (Google) and Tim Cook (Apple) at their word that personal data would be kept private and downloaded the apps.

To trace contacts, enabled devices exchange anonymized Bluetooth signals with each other whenever people with the app are within 6 feet for 10 minutes or more. To make it harder to identify users, those symbols are changed every 15 minutes and are created from a key that changes every 24 hours. On Android (Google) devices, the exchanged signals are saved to the system logs where they are securely buried unless the user tests positive and chooses to share that information. At least, that’s the idea. Reporter Alfred Ng cites AppCensus forensics lead Joel Reardon as he writes:

“The issue, Reardon said, is that hundreds of preinstalled apps like Samsung Browser and Motorola’s MotoCare on Android devices have access to potentially sensitive information that the contact tracing apps store in system logs—a byproduct of how the preinstalled apps receive information about user analytics and crash reports. … Studies have found that more than 400 preinstalled apps on phones built by Samsung, Motorola, Huawei, and other companies have permission to read system logs for crash reports and analytic purposes. In the case of contact tracing apps, Reardon found that the system logs included data on whether a person was in contact with someone who tested positive for COVID-19 and could contain identifying information such as a device’s name, MAC address, and advertising ID from other apps. In theory, that information could be swept up by preinstalled apps and sent back to their company’s servers. He has not found that any apps have actually gathered that data, but there’s nothing preventing them from doing so.”

Ah, third-party preinstalled apps. Perhaps Google could be forgiven for overlooking that vulnerability if they had taken it seriously when it was brought to their attention. This past February, AppCensus researchers hired by the Department of Homeland Security found the problem and alerted Google. (They found no similar problems with the iPhone version.) Alas, Google has not fixed what Reardon calls a “one-line thing.”  Instead the company has issued vague promises of rolling out an update of some sort at some time. Very reassuring.

Cynthia Murrell, May 6, 2021

An Algorithm to Pinpoint Human Traffickers

May 4, 2021

We love applications of machine learning that actually benefit society. Here is one that may soon be “Taking Down Human Traffickers Through Online Ads,” reports the Eurasia Review. The algorithm began as a way to spot anomalies (like typos) in data but has evolved into something more. Now dubbed InfoShield, it was tweaked by researchers at Carnegie Mellon University and McGill University. The team presented a paper on its findings at the most recent IEEE International Conference on Data Engineering. We learn:

“The algorithm scans and clusters similarities in text and could help law enforcement direct their investigations and better identify human traffickers and their victims, said Christos Faloutsos, the Fredkin Professor in Artificial Intelligence at CMU’s School of Computer Science, who led the team. ‘Our algorithm can put the millions of advertisements together and highlight the common parts,’ Faloutsos said. ‘If they have a lot of things in common, it’s not guaranteed, but it’s highly likely that it is something suspicious.’”

According to the International Labor Organization, ads for four or more escorts penned by the same writer indicate the sort of organized activity associated with human trafficking. The similarities detected by InfoShield can pinpoint such common authorship. The organization also states that 55% of the estimated 24.9 million people trapped in forced labor are women and girls trafficked in the commercial sex industry. Online ads are the main way their captors attract customers. The write-up continues:

“To test InfoShield, the team ran it on a set of escort listings in which experts had already identified trafficking ads. The team found that InfoShield outperformed other algorithms at identifying the trafficking ads, flagging them with 85% precision.”

The researchers ran into a snag when it came to having peers verify their results. Due to the sensitive nature of their subject, they could neither share their data nor publish examples of the similarities InfoShield identified. Happily, they found a substitute data sample—tweets posted by Twitter bots presented a similar pattern of organized activity. We’re told:

“Among tweets, InfoShield outperformed other state-of-the-art algorithms at detecting bots. Vajiac said this finding was a surprise, given that other algorithms take into account Twitter-specific metrics such as the number of followers, retweets and likes, and InfoShield did not. The algorithm instead relied solely on the text of the tweets to determine bot or not.”

That does sound promising. We hope authorities can use InfoShield to find and prosecute many, many human traffickers and free their victims.

Cynthia Murrell, May 4, 2021

Content Bot Employs Powerful OpenAI Tech for Marketing Content

May 4, 2021

Great news. Now companies can launch spam fiestas, no humans required, for as little as $29 per month. Content Bot offers tools to generate persuasive marketing copy, powerful slogans, smooth landing pages, improved blog posts, and even something it calls “automated inspiration.” The site promises “human-like text.” If that sounds familiar, it should—the site’s developers must have scored one of the coveted OpenAI beta API slots, as its FAQ reveals:

“We make use of a variety of AI models, with the main model being GPT-3 by OpenAI. GPT-3, or Generative Pre-trained Transformer 3 is an autoregressive language model which uses deep learning to produce human-like text. It’s a game changer for content creators.”

Indeed it is. This is exactly the sort of thing we expected to see when OpenAI began releasing its API to a select few last year. Well, one of the things—the fake news will likely be less publicized. Content Bot’s FAQ also specifies:

“95% of the content generated by the AI is unique and original. We also provide a uniqueness score for longer form content generated so you can have peace of mind to know that the content you have received is unique.”

So one must trust their metric to verify their promise of uniqueness. Interesting. We also learn the platform is relying on Google Translate as a stopgap measure:

“We currently support all languages supported by Google Translate. We understand that although Google Translate may not be the best translation for your needs, we are currently exploring other options such as IBM Watson and OpenAI to provide better, or multiple translations at once.”

Will the price go up if they find a better translation option? The service currently costs $29 per month for the basic version, $79 for the one geared toward agencies—the latter generates three times as many blog posts and supplies a “paraphrase rewriter.” There is a free trial available, and non-profits are invited to write in for a discount. It was no surprise to learn Content Bot workers are fully remote, but the company maintains licenses and operating addresses in Florida and in South Africa. Who will be next to launch a product based on GPT-3?

Cynthia Murrell, May 4, 2021

AI Algorithms: Dealing Straight?

May 4, 2021

Humans are easily influenced and it is not rocket science either. In order to influence humans, all it takes is a working understanding of psychology, human behavior, and appealing to their emotions. Con artists are master manipulators, but they are about to be one upped by AI algorithms. The Next Web shares how easy it is to influence human behavior in the article, “Study Shows How Dangerously Simple It Is To Manipulate Voters (And Daters) With AI.”

Researchers at the Spanish Universidad de Deusto published a study on how AI can easily influence humans:

“Up front: The basic takeaway from the work is that people tend to do what the algorithm says. Whether they’re being influenced to vote for a specific candidate based on an algorithmic recommendation or being funneled toward the perfect date on an app, we’re dangerously easy to influence with basic psychology and rudimentary AI.

The big deal: We like to think we’re agents of order making informed decisions in a somewhat chaotic universe. But, as Neural’s Thomas Maucalay recently pointed out…we’re unintentional cyborgs.”

Apparently humans are no longer homo sapiens—the literal translation of the human scientific name is “wise man.” Due to our larger brain capacity, humans reasoned their way through evolution to become the dominant species. The research argues that due to our dependence on computers to do our thinking, we have changed our evolutionary status.

The researchers used fake personality tests situated around political candidates and dating apps to determine how participants were influenced by algorithms. The tests showed that participants were easily manipulated by choices AI algorithms offered them. The concept is similar to how magicians lure their audiences to a specific outcome with a “magical force.”

The problem is that we are uneducated about AI algorithm’s power. Companies use them for advertising to earn bigger profits, politicians use them to manipulate votes, and bad actors can use them to take advantage of unsuspecting marks. Bad actors, companies, politicians (although they do not all fall into the same ethics category) work faster than academics can test new algorithmic science. While humans are smart enough to beat some AI algorithms, they need to be educated about them first and it will be a long time before AI manipulation tactics make their way into Google type public service announcements.

Whitney Grace, May 4, 2021

Great Moments in Censorship: Beethoven Bust

May 3, 2021

I noted a YouTube video called “Five Years on YouTube.” Well, not any longer. A highly suspect individual who has the gall to teach piano was deemed unacceptable. Was this a bikini haul by YouTube influencer/sensation Soph Mosca, who recently pointed out that reading a book was so, well, you know, ummm hard. Was it a pointer to stolen software like this outstanding creators’ contributions who seem little troubled by YouTube’s smart software monitoring system:


Nope, the obviously questionable piano teacher with 29,000 people who want to improve their piano skills is a copyright criminal type.

Watch the video. Notice the shifty eyes. Notice the prison complexion. Notice the poor grammar, enunciation, and use of bad actor argot.

Did you hear these vile words:

  • Beethoven
  • Upsetting.

And the music? I think Beethoven is active on Facebook, Instagram, Twitter, and other social media channels. He is protected by the stalwarts at Amazon, Apple, and Google. Did he really tweet: “Persecute piano teachers”?

What’s he have to say about this nefarious person’s use of notes from the Moonlight Sonata?

Asking Beethoven is similar to asking Alexa or Siri something. The truth will be acted upon.

I think smart software makes perfect decisions even though accuracy ranges from 30 percent to 90 percent for most well crafted and fiddled models.

Close enough for horse shoes. And piano teachers! Ban them. Lock them up. Destroy their pianos.

Furthermore the perpetrator of this crime against humanity ifs If you want to help her, please, contact her. Beyond Search remembers piano teachers, an evil brood. Ban them all, including Tiffany Poon and that equally despicable Dame Mitsuko Uchida who has brazenly performed Mozart’s Piano Concerto K. 271.

Cleanse the world of these spawn of Naamah.

Stephen E Arnold, May 3, 2021

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