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Facial Recognition thinks you might be a toaster, really

(TECH NEWS) Facial Recognition is still a log way from being perfect. Ceci n’est pas une toaster. Really. Repeat it with me: I am not a toaster.

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Facial recognition failure

Using facial recognition seems pretty seamless, think of your iPhone. Yet, a human face has actually been confused with a toaster, according to a facial recognition technology expert.

If a computer, which is thought to be highly reliable, will confuse a human face for a toaster, what might that mean for facial recognition accuracy when seeking out suspects of crimes? Possibly, not so reliable.

“Obviously, the technology has immense value in promoting societal interests such as efficiency and security but it also represents a threat to some of our individual interests, particularly privacy,” Nessa Lynch, associate professor of law at Victoria University of Wellington, New Zealand. Lynch and other experts are part of a research project that will be completed in mid-2020. The researchers presented some of their findings during a panel recently held at the university.

Some of the very first images used to test data were those of convicted felons in Florida. They had abused meth and had great cheekbones. But, that presented problems when using facial recognition on actual real folk without a meth habit.

The cheekbones are very different than the average person, which can happen when you eat food. Data from such a source was not useful when training a system to recognize normal people, said Rachel Dixon, Privacy and Data Protection Deputy Commissioner at the Office of the Victorian Information Commissioner in Australia.

Companies who sell the technology products often claim they are highly reliable, but Dixon said, often they are reliable because of the environments where they are used, which may be unvarying. And, the systems are tuned for these specific environments.

“…Picking you out walking randomly down the street can be quite challenging. There’s a whole bunch of environmental factors there that go to essentially reducing the confidence level,” Dixon said in a story published on Ideasroom. “None of this is absolute. There is no one-to-one match. And by perturbing an image even a small amount you can make the machine-learning system think the person is a toaster. I’m not joking.”

If a computer recognizes a face, for example, as person of interest in a crime, it is very hard to change that perception, even if it is wrong, because humans have a hard time believing a machine can make a mistake, especially if it has said it is the correct match, Dixon explained.

In the United States, a conservative estimate is that roughly a quarter of all the 18,000 law enforcement agencies have access to facial recognition systems, particularly for the use in investigations. Yet, Georgetown Law Professor Clare Garvie said there are no laws – at the state or federal level – governing its use.

Garvie, a senior associate at the center on privacy and technology at Georgetown said, “As a result, this technology has been implemented largely without transparency to the public, without rules around auditing or public reporting, without rules around who can be subject to a search. As a result, it is not just suspects of a criminal investigation that are the subject of searches. In many jurisdictions, witnesses, victims or anybody associated with a criminal investigation can also be the subject of a search.”

Because there is little reporting and auditing of the use of the technology, it’s unclear if agencies are checking to determine if it’s being misused or if it is actually a helpful and successful tool, Garvie said. Are law enforcement officials “catching the bad guys” or is the use of the technology a waste of money, which she said she suspects it is in some jurisdictions.

Meanwhile, it may come as no surprise to some, those often caught in the crosshairs are from lower socio-economic status or marginalized populations.

In one instance, a person who was ranked 319th for being a likely match based on the algorithmic ranking, was the one police arrested. The police also failed to provide the ranking evidence to the defense lawyers.

In the United Kingdom, the technology has been used extensively and with mixed results by law enforcement and businesses in order to search for people on watch lists, according to Dr. Joe Purshouse from the School of Law at the University of East Anglia in the UK.

“The human rights implications for privacy, freedom of assembly – those are chilling, Purshouse said, adding the marginalized are caught in the middle such as, “Suspects of crime, people of lower socio-economic status who are forced to use public space and rely more heavily on public space than people who have economic advantages, perhaps.”

Mary Ann Lopez earned her MA in print journalism from the University of Colorado and has worked in print and digital media. After taking a break to give back as a Teach for America corps member and teaching science for a few years, she is back with her first love: writing. When she's not writing stories, reading five books at once, or watching The Great British Bakeoff, she is walking her dog Sadie and hanging with her cats, Bella, Bubba, and Kiki. She is one cat short of full cat lady status and plans to keep it that way.

Tech News

Defense startups are getting beaucoup bucks from the DoD

(TECH NEWS) Some tech companies are getting large venture capital because the Department of Defense is looking for new defense startups.

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military looking defense startups

While private investors remain wary of funding defense startups, they are still keeping an eye on the possible venture opportunities. Meanwhile, the Pentagon is hoping domestic investors will increase spending into these startups in order to compete with China’s strategy of creating private equity firms to invest into foreign technologies.

A major reason for the growing interest by venture capitalists is the shift in focus from traditional weapons to tools for information warfare, meaning software and tech systems. Defense startups are creating products that may have multiple benefits outside the DoD.

Changes in the defense venture landscape are slow with all three parties learning how to benefit from one another. Startups realize working with the DoD is a “mission-driven objective” as stated by Ryan Tseng, founder of Shield AI. “We went into this eyes wide open, knowing full well that to the venture community, the math doesn’t make sense.”

However, there are several big investor players already in the game. Andreessen Horowitz, a top-tier venture fund is banking on the economic sustainability of defense startups in the future. They’ve already invested in Shield AI and defense tech company Anduril Industries. Additionally, the Founders Fund, another big name venture firm led by Silicon investors Peter Thiel, Brian Singerman, and Ken Howery is investing in Anduril and goTenna after successfully backing SpaceX and Palantir Technologies.

Defense companies’ emphasis on tech could be the answer to challenges usually associated with DoD investments like competing against dominate manufacturers with steady government contracts and long procurement cycles. U.S. Code 2377 stipulates that commercially available items be considered first in procurement efforts. If defense startups can enter the market, they will also stand a chance of winning government contracts over bigger, traditional companies, thus diversifying the playing field.

But until there is a greater guarantee of a payoff, investors are likely to remain skeptical. The possibilities for this new generation of defense companies is going to needs some more wins to prove the future is in their corner.

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Tech News

Goal-based project management tool simplifies your work life

(TECH NEWS) If you are struggling to keep tasks straight then this new tool Qoals allows for a simpler and more straightforward way to accomplish goals as a team.

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Qoals pitch

We all have goals – whether they be personal, professional, financial, etc. Anyone can set a goal, all it takes is having a thought and assigning it a certain level of importance. However, not everyone completes their goals due to the oft difficulties and confusions associated with execution.

Like anything else, if there’s a will, there’s a way. A new way has been found in the form of Qoals – a simple and straightforward tool that helps you to get aligned around business goals instead of an endless wall of tasks.

The ability to complete goals is done through: setting goals, adding tasks, collecting things, and tracking progress. With this, everyone on your team has access to this information to keep tabs on what’s happening.

With setting goals, you create and prioritize your goals, letting your team members know which ones are most important at that time. Goals can be prioritized with tabs such as: long term, short term, and urgent. By adding tasks, you can add and assign tasks to set a clear path in order to complete set goals.

In collecting things, you collect resources related to your goal and keep them in one safe place (again, this is accessible to your whole team). This doesn’t require uploading files, but simply including links to resources to keep everything easily accessible. Finally, by tracking progress, everyone on the team can see where you’re at with your goals – which saves time with the follow ups of “how’s Goal X going?”

Why did Qoals develop this goal-oriented approach? “It’s about time we simplify things,” according to the official website. “Get aligned around goals and let everyone know what’s important for the business. Add goals under various projects and start adding tasks and resources to make that goal happen.”

Additionally, Qoals boasts that this provides users with a birds-eye view of what’s happening with their team, allowing them to be more human-centric. You can create unlimited projects, set and track your goals, collected everything related to said goal, keep the discussion relevant, access your tasks with one click, stay connected to your team, and see what’s going on at a glance.
Qoals is currently in beta.

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Tech News

7 ways AI will transform health care

(TECH NEWS) Instead of worrying about the singularity of AI technology, let’s shine a ray of hope, and show one of the best ways to use AI robots.

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robot AI health care

Artificial intelligence (AI) has revolutionized business practices across many industries. With 39% of health care providers investing in AI-related technologies, it’s safe to say it’s about to transform the medical industry as well. AI’s disruptive potential may increase profits in other sectors, but in health care, it can save lives.

While robots aren’t quite ready to replace doctors, they can help them accomplish their tasks with higher speed and precision. AI in the hospital is not just a thing of the future, either. All around the world, smart machines are already assisting medical staff in a variety of ways.

As this technology refines, machine learning will become an increasingly regular part of medicine. Here are seven ways AI will transform health care in the coming years.

1. Robot-Assisted Surgery

It may sound like something out of a sci-fi movie, but surgery robots are already in use. In 2017, more than 690,000 surgeries were assisted by robots in the U.S. alone. As these machines continue to demonstrate their worth, they’ll appear in more operating rooms.

Robotic surgeons like the da Vinci Surgical System offer more precise and less invasive movements than human hands and traditional tools. With AI, they can improve upon surgical methods. AI-enabled surgery bots can notice reactions in a patient invisible to the human eye and make necessary adjustments.

2. Early Diagnoses

Intelligent programs can quickly analyze vast amounts of information. This unique talent makes AI ideal for making preliminary diagnoses in patients. Smart machines can take note of patients’ symptoms and interpret them to make an early diagnosis while doctors make their rounds.

The accuracy of these diagnoses will improve as AI develops, but even at its current state, they can be useful. Doctors can use them as a starting-off point. A list of likely diagnoses can be a helpful resource to doctors when trying to diagnose patients as quickly as possible. They might also make health care professionals consider options they otherwise wouldn’t have thought of, increasing accuracy.

3. Administrative Assistance

Treating patients is not the only duty of health care professionals. Doctors and nurses have to take records of patient data, from symptoms to insurance information, so they can refer to them later or send them to other hospitals. This process can take time, and any issues along the way can create problems for patients and doctors alike.

IT usability is a critical part of health care, and AI can optimize it. Intelligent systems can find ways to streamline the information-sharing process, ensuring health workers get the data they need as soon as possible. AI can also handle administrative tasks like scheduling and logistics, allowing hospital staff to focus on more pressing concerns.

4. Health Screenings

Just as AI applications can diagnose patients, they can also make predictions about a person’s fitness for a given situation. Predictive analytics is an AI function that analyzes historical data to make predictions about future outcomes. AI systems can use predictive analytics to perform more nuanced health screenings.

AI can tell doctors is a patient would be fit for surgery or not. Similarly, it can advise people if they aren’t a suitable candidate for physically exerting activities or tests. These analytics consider a wide range of data, including things a human might overlook, leading to more accurate predictions.

5. Remote Monitoring

AI can also optimize health care outside of the hospital. Wearable technology is already prevalent with products like Fitbit, and the medical industry can use this to its advantage. With wearable health-monitoring devices, doctors can monitor their patients remotely.

Remote monitoring devices can alert patients if they need to see a doctor. Should an emergency occur, they can also alert hospital staff so they can send an ambulance. These noninvasive technologies will allow patients with conditions such as heart disease to live without fear by providing them with almost instant assistance.

6. Robot Nurses

Intelligent robots can help fix the nursing labor shortage by filling in those vital roles. Nursing robots are already working in Japanese hospitals and may soon see use in the U.S. These machines can help patients move, reduce their stress and remind them to take their medicine.

With AI, these robot nurses can adapt to each patient’s needs and desires. By analyzing how different people respond to various stimuli and situations, they can customize care. Intelligent nurse robots treat patients in a manner ideal for their health and comfort needs.

7. AI-Enabled Genomics

Compared to humans, AI is better suited for data-heavy tasks. Since DNA sequencing is a form of data analysis, it’s an ideal area to employ AI.

Using artificial intelligence in genomics has already shown impressive results. In 2019, an AI system identified new genetic mutations that contributed to autism. The system could detect patterns in DNA humans would not be able to, as well as predict how changing each gene would affect a person.

AI Is Revolutionizing Medicine

Artificial intelligence is changing the way the health care industry operates. With continued research and improvement, AI systems could save countless lives.

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