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The photo on the left was what Sophia Jones fed the AI service. It generated the two images on the right. Sophia Jones hide caption

The photo on the left was what Sophia Jones fed the AI service. It generated the two images on the right.

Sophia Jones is juggling a lot right now. She just graduated from her master's program, started her first full-time job with SpaceX and recently got engaged. But thanks to technology, one thing isn't on her to-do list: getting professional headshots taken.

Jones is one of a growing number of young professionals who are relying not on photographers to take headshots, but on generative artificial intelligence.

The process is simple enough: Users send in up to a dozen images of themselves to a website or app. Then they pick from sample photos with a style or aesthetic they want to copy, and the computer does the rest. More than a dozen of these services are available online and in app stores.

For Jones, the use of AI-generated headshots is a matter of convenience, because she can tweak images she already has and use them in a professional setting. She found out about AI-generated headshots on TikTok, where they went viral recently, and has since used them in everything from her LinkedIn profile to graduation pamphlets, and in her workplace.

So far no one has noticed.

"I think you would have to do some serious investigating and zooming in to realize that it might not truly be me," Jones told NPR.

Still, many of these headshot services are far from perfect. Some of the generated photos give users extra hands or arms, and they have consistent issues around perfecting teeth and ears.

These issues are likely a result of the data sets that the apps and services are trained on, according to Jordan Harrod, a Ph.D. candidate who is popular on YouTube for explaining how AI technology works.

Harrod said some AI technology being used now is different in that it learns what styles a user is looking for and applies them "almost like a filter" to the images. To learn these styles, the technology combs through massive data sets for patterns, which means the results are based on the things it's learning from.

"Most of it just comes from how much training data represents things like hands and ears and hair in various different configurations that you'd see in real life," Harrod said. And when the data sets underrepresent some configurations, some users are left behind or bias creeps in.

Rona Wang is a postgraduate student in a joint MIT-Harvard computer science program. When she used an AI service, she noticed that some of the features it added made her look completely different.

"It made my skin kind of paler and took out the yellow undertones," Wang said, adding that it also gave her big blue eyes when her eyes are brown.

Others who have tried AI headshots have pointed out similar errors, noticing that some websites make women look curvier than they are and that they can wash out complexions and have trouble accurately depicting Black hairstyles.

"When it comes to AI and AI bias, it's important for us to be thinking about who's included and who's not included," Wang said.

For many, the decision may come down to cost and accessibility.

Grace White, a law student at the University of Arkansas, was an early adopter of AI headshots, posting about her experience on TikTok and attracting more than 50 million views.

The close-up photo on the right was one of 10 real images that Grace White submitted to an AI service, which generated the two images on the left. Grace White hide caption

Ultimately, White didn't use the generated images and opted for a professional photographer to take her photo, but she said she recognizes that not everyone has the same budget flexibility.

"I do understand people who may have a lower income, and they don't have the budget for a photographer," White said. "I do understand them maybe looking for the AI route just to have a cheaper option for professional headshots."

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Young professionals are turning to AI to create headshots. But there ... - NPR

On Friday, Sept. 8, the second annual Day for Data symposium will gather industry leaders and academia together for a practical exploration of business analytics. The event is scheduled from 8 a.m. to 4 p.m. EDT in Virginia Tech's Owens Ballroom.

Virginia Tech is a leader in advanced analytics programs and capabilities, said Jay Winkeler, executive director of the Center for Business Analytics. Building off the success from last year, Day for Data will be bigger and bolder, with a focus on the AI [artificial intelligence] revolution happening all around us.

The conference, hosted by the Pamplin College of Businesss Center for Business Analytics, is an opportunity for shared learning and thought leadership in the field of business analytics. Corporate leaders and university faculty converge to fill a robust agenda with expertise in a wide range of topics including generative AI and large language models, advanced data analytics, digital privacy, business leadership and intelligence, and more.

Beyond the rich learning component, Day for Data also lends itself to opportunities for professional advancement. With a strong turnout expected from both academia and industry, the event offers students a chance to see the real-world applications of their studies and companies an opportunity to scout for emerging talent.

The interaction between students, faculty, and corporations is critical to harnessing the power of analytics and showing how skilled professionals translate analytics into meaningful business decisions, said Winkeler. For industry professionals, it is a chance to tell their success stories and gain critical exposure to a talented student and faculty population.

The symposium will begin with opening remarks by Saonee Sarker, Richard E. Sorensen Dean for the Pamplin College of Business, followed by a keynote address from Andrew Allwine, senior director of data optimization for Norfolk Southern. During the session, Allwine will share his strategies for aggregating and translating complex datasets into actionable insights and tangible return on investment for organizational decision-makers.

Key contributions by faculty working within Pamplin include a session led by Voices of Privacy, an initiative spearheaded by Professors France Blanger and Donna Wertalik that seeks to prepare society to manage their information privacy amid the challenging modern digital landscape, as well as a research poster session highlighting the latest research in the field.

After a lunch and networking break, Keith Johnson, director of solutions architecture for partner systems integrators at Amazon Web Services, will deliver a presentation and live demonstration of Amazons latest innovations with generative AI and large language models. Tracy Jones, data strategy and management executive for Guidehouse, will follow with a session on the opportunities and threats of artificial intelligence implementation, including case studies of organizations that neglected ethical principles and suffered consequences.

Both experts will return to join Kevin Davis, chief growth officer for MarathonTS, and Cayce Myers, director of graduate studies for the School of Communication at Virginia Tech, for a panel discussion and interactive conversation on artificial intelligence, including ethical, legal, and technical considerations. Day for Data will conclude with a networking reception.

Day for Data 2023 is sponsored by Norfolk Southern, Guidehouse, MarathonTS, Ernst & Young, and Amazon Web Services.

For more information on Day for Data and to register, please visit the event page.

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Generative AI and data analytics on the agenda for Pamplin's Day ... - Virginia Tech

Imagine you want to carry a large, heavy box up a flight of stairs. You might spread your fingers out and lift that box with both hands, then hold it on top of your forearms and balance it against your chest, using your whole body to manipulate the box.

Humans are generally good at whole-body manipulation, but robots struggle with such tasks. To the robot, each spot where the box could touch any point on the carriers fingers, arms, and torso represents a contact event that it must reason about. With billions of potential contact events, planning for this task quickly becomes intractable.

Now MIT researchers found a way to simplify this process, known as contact-rich manipulation planning. They use an AI technique called smoothing, which summarizes many contact events into a smaller number of decisions, to enable even a simple algorithm to quickly identify an effective manipulation plan for the robot.

While still in its early days, this method could potentially enable factories to use smaller, mobile robots that can manipulate objects with their entire arms or bodies, rather than large robotic arms that can only grasp using fingertips. This may help reduce energy consumption and drive down costs. In addition, this technique could be useful in robots sent on exploration missions to Mars or other solar system bodies, since they could adapt to the environment quickly using only an onboard computer.

Rather than thinking about this as a black-box system, if we can leverage the structure of these kinds of robotic systems using models, there is an opportunity to accelerate the whole procedure of trying to make these decisions and come up with contact-rich plans, says H.J. Terry Suh, an electrical engineering and computer science (EECS) graduate student and co-lead author of a paper on this technique.

Joining Suh on the paper are co-lead author Tao Pang PhD 23, a roboticist at Boston Dynamics AI Institute; Lujie Yang, an EECS graduate student; and senior author Russ Tedrake, the Toyota Professor of EECS, Aeronautics and Astronautics, and Mechanical Engineering, and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL). The research appears this week in IEEE Transactions on Robotics.

Learning about learning

Reinforcement learning is a machine-learning technique where an agent, like a robot, learns to complete a task through trial and error with a reward for getting closer to a goal. Researchers say this type of learning takes a black-box approach because the system must learn everything about the world through trial and error.

It has been used effectively for contact-rich manipulation planning, where the robot seeks to learn the best way to move an object in a specified manner.

But because there may be billions of potential contact points that a robot must reason about when determining how to use its fingers, hands, arms, and body to interact with an object, this trial-and-error approach requires a great deal of computation.

Reinforcement learning may need to go through millions of years in simulation time to actually be able to learn a policy, Suh adds.

On the other hand, if researchers specifically design a physics-based model using their knowledge of the system and the task they want the robot to accomplish, that model incorporates structure about this world that makes it more efficient.

Yet physics-based approaches arent as effective as reinforcement learning when it comes to contact-rich manipulation planning Suh and Pang wondered why.

They conducted a detailed analysis and found that a technique known as smoothing enables reinforcement learning to perform so well.

Many of the decisions a robot could make when determining how to manipulate an object arent important in the grand scheme of things. For instance, each infinitesimal adjustment of one finger, whether or not it results in contact with the object, doesnt matter very much. Smoothing averages away many of those unimportant, intermediate decisions, leaving a few important ones.

Reinforcement learning performs smoothing implicitly by trying many contact points and then computing a weighted average of the results. Drawing on this insight, the MIT researchers designed a simple model that performs a similar type of smoothing, enabling it to focus on core robot-object interactions and predict long-term behavior. They showed that this approach could be just as effective as reinforcement learning at generating complex plans.

If you know a bit more about your problem, you can design more efficient algorithms, Pang says.

A winning combination

Even though smoothing greatly simplifies the decisions, searching through the remaining decisions can still be a difficult problem. So, the researchers combined their model with an algorithm that can rapidly and efficiently search through all possible decisions the robot could make.

With this combination, the computation time was cut down to about a minute on a standard laptop.

They first tested their approach in simulations where robotic hands were given tasks like moving a pen to a desired configuration, opening a door, or picking up a plate. In each instance, their model-based approach achieved the same performance as reinforcement learning, but in a fraction of the time. They saw similar results when they tested their model in hardware on real robotic arms.

The same ideas that enable whole-body manipulation also work for planning with dexterous, human-like hands. Previously, most researchers said that reinforcement learning was the only approach that scaled to dexterous hands, but Terry and Tao showed that by taking this key idea of (randomized) smoothing from reinforcement learning, they can make more traditional planning methods work extremely well, too, Tedrake says.

However, the model they developed relies on a simpler approximation of the real world, so it cannot handle very dynamic motions, such as objects falling. While effective for slower manipulation tasks, their approach cannot create a plan that would enable a robot to toss a can into a trash bin, for instance. In the future, the researchers plan to enhance their technique so it could tackle these highly dynamic motions.

If you study your models carefully and really understand the problem you are trying to solve, there are definitely some gains you can achieve. There are benefits to doing things that are beyond the black box, Suh says.

This work is funded, in part, by Amazon, MIT Lincoln Laboratory, the National Science Foundation, and the Ocado Group.

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AI helps robots manipulate objects with their whole bodies - MIT News

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Enterprises have quickly recognized the power of generative AI to uncover new ideas and increase both developer and non-developer productivity. But pushing sensitive and proprietary data into publicly hosted large language models (LLMs) creates significant risks in security, privacy and governance. Businesses need to address these risks before they can start to see any benefit from these powerful new technologies.

As IDC notes, enterprises have legitimate concerns that LLMs may learn from their prompts and disclose proprietary information to other businesses that enter similar prompts. Businesses also worry that any sensitive data they share could be stored online and exposed to hackers or accidentally made public.

That makes feeding data and prompts into publicly hosted LLMs a nonstarter for most enterprises, especially those operating in regulated spaces. So, how can companies extract value from LLMs while sufficiently mitigating the risks?

Instead of sending your data out to an LLM, bring the LLM to your data. This is the model most enterprises will use to balance the need for innovation with the importance of keeping customer PII and other sensitive data secure. Most large businesses already maintain a strong security and governance boundary around their data, and they should host and deploy LLMs within that protected environment. This allows data teams to further develop and customize the LLM and employees to interact with it, all within the organizations existing security perimeter.

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A strong AI strategy requires a strong data strategy to begin with. That means eliminating silos and establishing simple, consistent policies that allow teams to access the data they need within a strong security and governance posture. The end goal is to have actionable, trustworthy data that can be accessed easily to use with an LLM within a secure and governed environment.

LLMs trained on the entire web present more than just privacy challenges. Theyre prone to hallucinations and other inaccuracies and can reproduce biases and generate offensive responses that create further risk for businesses. Moreover, foundational LLMs have not been exposed to your organizations internal systems and data, meaning they cant answer questions specific to your business, your customers and possibly even your industry.

The answer is to extend and customize a model to make it smart about your own business. While hosted models like ChatGPT have gotten most of the attention, there is a long and growing list of LLMs that enterprises can download, customize, and use behind the firewall including open-source models like StarCoder from Hugging Face and StableLM from Stability AI. Tuning a foundational model on the entire web requires vast amounts of data and computing power, but as IDC notes, once a generative model is trained, it can be fine-tuned for a particular content domain with much less data.

An LLM doesnt need to be vast to be useful. Garbage in, garbage out is true for any AI model, and enterprises should customize models using internal data that they know they can trust and that will provide the insights they need. Your employees probably dont need to ask your LLM how to make a quiche or for Fathers Day gift ideas. But they may want to ask about sales in the Northwest region or the benefits a particular customers contract includes. Those answers will come from tuning the LLM on your own data in a secure and governed environment.

In addition to higher-quality results, optimizing LLMs for your organization can help reduce resource needs. Smaller models targeting specific use cases in the enterprise tend to require less compute power and smaller memory sizes than models built for general-purpose use cases or a large variety of enterprise use cases across different verticals and industries. Making LLMs more targeted for use cases in your organization will help you run LLMs in a more cost-effective, efficient way.

Tuning a model on your internal systems and data requires access to all the information that may be useful for that purpose, and much of this will be stored in formats besides text. About 80% of the worlds data is unstructured, including company data such as emails, images, contracts and training videos.

That requires technologies like natural language processing to extract information from unstructured sources and make it available to your data scientists so they can build and train multimodal AI models that can spot relationships between different types of data and surface these insights for your business.

This is a fast-moving area, and businesses must use caution with whatever approach they take to generative AI. That means reading the fine print about the models and services they use and working with reputable vendors that offer explicit guarantees about the models they provide. But its an area where companies cannot afford to stand still, and every business should be exploring how AI can disrupt its industry. Theres a balance that must be struck between risk and reward, and by bringing generative AI models close to your data and working within your existing security perimeter, youre more likely to reap the opportunities that this new technology brings.

Torsten Grabs is senior director of product management at Snowflake.

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