Tag: future of ai

I Am an Investor in OpenAI — Here’s What Other Investors Must Consider About The Biggest Risks and Benefits of AI | Entrepreneur

[ad_1]

Opinions expressed by Entrepreneur contributors are their own.

While AI has been in existence since the 1950s, it has only just recently become a household name with the introduction of OpenAI’s ChatGPT less than two years ago. As an investor in both OpenAI and xAI, I continuously ask myself the ethical ramifications of this new technology, how intelligent it is and whether it is the best path forward for humanity. As an experiment, I asked ChatGPT its opinion about artificial intelligence (AI) systems in today’s society, and this is what it had to say:

“As an artificial intelligence language model, I do not have personal opinions or beliefs. However, I can provide an objective analysis of AI systems in today’s society. AI has brought about numerous benefits to society, such as improved efficiency, accuracy, and decision-making processes. It has also contributed to advancements in medical research, transportation, and various other industries. However, the deployment of AI systems in today’s society also presents potential risks, such as job displacement, privacy concerns, and the possibility of biased algorithms. Therefore, it is crucial to ensure that AI is developed ethically and responsibly to mitigate these risks and ensure that the technology benefits society as a whole. In summary, AI systems have immense potential to positively impact society, but their deployment must be done with careful consideration and ethical responsibility.”

Related: The Only Drawback of AI Is Your Attitude. Here Are 4 Things You Need to Change Your Outlook — And Harness AI for Success.

While this response shows AI is still robotic and objective in its methods of “thinking,” it is also, in fact, intimately aware of the ethical concerns bearing over its use. At this critical point in history, where we sit at the precipice of mass AI adoption, we still can’t seem to decide whether AI will be our savior or our villain. Ultimately, it comes down to the applications and responsible management of AI systems.

As an investor, I am in the unique position of being able to help push these new AI companies forward. In the last two years, we have seen a “gold rush” of AI startups. Many have raised impressive rounds; many have faltered, and some have already seen acquisitions. However, the first two I have invested in OpenAI and xAI are now well-known. The former for its ability to have tackled the challenge of global mass adoption and the latter for its commitment to building a more ethical system for the AI ecoverse.

When it comes to applications in AI, it’s safe to say that humans have always strived to create tools to make our lives easier; this is not new. Historically, “mechanical muscles” and outsourcing shifted physical labor. Now, we’re at the point where “mechanical minds” will unleash a cascade of applications across industries. Automation has evolved from more than just the physical.

One major development pushing forward the AI adoption frontier is Microsoft’s continued investment in OpenAI. This partnership will likely lead to everything from optimized Excels to AI-generated PowerPoints and even more support in email management. Upper-class work is now also ripe for disruption.

In finance, AI can be leveraged in various ways. AI algorithms can identify patterns of behavior, such as unusual transactions or identity theft, allowing financial institutions to detect and prevent activities quickly. AI can also analyze market trends and make assumptions about future trends, assisting institutions in making more accurate decisions.

While this is only one sample of industry transformation, there are some areas of work that will be impacted less by AI. These industries are those operating in information asymmetry — such as early-stage venture capital. For AI to work effectively, the model must have access to data. Industries whose data is private, segregated and complex cannot train and build inferences in the same way that a model based on publicly available information can. The strength of AI is dependent on the value of its underlying data and model, which in turn is dependent on the quality of the rules set forth by the humans crafting the algorithms.

In terms of management, we must continue to maintain an element of skepticism and criticism as the growing adoption of AI increases. These tools will continue to develop, but they should not be treated as an all-knowing source of truth. Again, this is critical because these systems only know what is in their underlying systems. As people, investors and business-minded individuals, we must acknowledge the strength of these systems while also considering the fact that they must be constantly maintained. While AI employs a constantly evolving algorithm that learns from itself and experiences, we must still continue to adjust the rules and data sources supporting these AI systems.

Related: AI Will Radically Transform the Workplace — Here’s How HR Teams Can Prepare for It

Diversity of thought and perspectives is critical for those who have the power to develop these systems. A system created by humans with biases will intrinsically be biased as well. We must maintain human values in control of the systems. In today’s society, where truth seems to be subjective, belief in these systems can be both extremely advantageous and extremely detrimental. We must refrain from programming systems to tell people what they want to hear. It is important for people to understand and respect the limitations of AI.

At the same time, just because humanity is capable of doing something doesn’t always mean it should. We could likely replace most jobs with AI, but should we? Where do we draw the line? How do we ensure honesty and integrity in future systems and platforms? This is part of what xAI and OpenAI have committed to tackling and why I have believed in and supported them from the beginning.

AI will undoubtedly transform all of our lives, but this transformation will only be positive if we continue to be critical stewards of truth and information. As investors, I encourage everyone to maintain a healthy dose of skepticism when investing in AI-powered solutions. Look at the human beings who stand behind those systems because their beliefs are the ones teaching and driving the solution.

[ad_2]

Ozi Amanat

Source link

August 15, 2024
New AI Listens to Toilet Sounds to Detect Diarrhea

[ad_1]

Dec. 27, 2022 — Artificial intelligence has achieved another milestone: Discerning the sound of an unhealthy bowel movement.

A design for a “Diarrhea Detector” that could alert health officials to disease outbreaks like cholera was recently presented by engineers from the Georgia Tech Research Institute. Someday, the AI could even be used with home smart devices to monitor one’s bowel health.

A prototype accurately identified diarrhea 98% of the time in tests, the engineers told a conference of the Acoustical Society of America in Nashville. Even with background noise, it was correct 96% of the time.

Cholera infects millions of people each year, killing up to 143,000 who become dehydrated from severe diarrhea, according to the World Health Organization. Many deaths could be avoided with an oral rehydration solution if the outbreak is spotted fast enough. Cholera can be lethal within 24 hours after symptoms start.

The device could be installed in public toilets where inadequate plumbing raises the risk for a cholera outbreak.

“Cholera typically has a more watery sound to it — it can sound a lot like urination and it doesn’t have a lot of the flatulence notes in general,” says project co-lead Maia Gatlin, an aerospace engineer and PhD candidate at the Georgia Tech Research Institute. “That someone is having severe diarrhea, and that they are having a lot of it — that can be captured.”

The idea grew out of conversations about how COVID-19 can be monitored by analyzing sewage, says project co-lead Alexis Noel, PhD, a biomechanics engineering researcher at the institute.

Other researchers have considered video analysis to look for diarrhea.

“I was curious if we could detect diarrhea using sound,” Noel says, “as some folks are a little wary about having a camera pointed at their bum in the toilet.”

First, the researchers gathered 350 publicly available audio samples of bathroom sounds from YouTube and Soundsnap. Some clips had up to 10 hours of diarrhea noises.

The researchers listened to the samples to establish authenticity.

“We didn’t know these people, we didn’t know how they recorded, so we had to listen to a good bit,” Gatlin says. “There were definitely lots of fart sounds where we were like, ‘That’s not a fart, that’s someone blowing into their elbow.’”

The sounds of defecation, urination, flatulence, and diarrhea were converted into spectrogram images. A computer analyzed those images for about 10 hours using a “convolutional neural network.” The software, using trial and error, teaches itself how to identify the subtle similarities between diarrhea spectrograms and how they differ from other toilet sounds.

For example, urination has a consistent tone and defecation may have a singular tone. Diarrhea’s sound is more random.

Once the AI learning process was complete, the researchers loaded the diarrhea-decoding algorithm onto a Raspberry Pi, a computer roughly the size of a credit card that costs less than $50. Georgia Tech student Cade Tyler 3D-printed a case for the motherboard with a microphone connection, a series of lights (green for acquiring a signal, red for diarrhea, and orange for “other”), and the words “Diarrhea Detector” inscribed on the surface.

The computer takes a 10-second audio recording, which is converted to a spectrogram and fed to the algorithm. The whole process takes only seconds.

The next iteration of the device would send a report via Wi-Fi or other wireless communication signal to a database, so public health officials can monitor for disease outbreaks.

“We’re not collecting anything identifiable about people,” Gatlin says.

The researchers have not yet determined how many of these devices would be needed to cover a community, or where the ideal placement would be.

The algorithm still needs to be refined using better audio data collected in controlled conditions, from people who have provided informed consent, Gatlin says. Gatlin also hopes to train the AI to work in outdoor latrines, which are common in areas without functioning sewage systems.

[ad_2]

Source link

December 27, 2022
Could This Computer Help You Beat Cancer?

[ad_1]

Nov. 22, 2022 – The 1960s marked the arrival of computers in medicine. Expensive, cumbersome hunks of plastic and metal that could (maybe) get test results to a doctor faster. The 1980s saw the first real difference-making functions computers could offer – clinical, financial, administrative – and in 1991, the Institute of Medicine published the first manifesto on what electronic health records could (and would) be.

Since then, we’ve seen computer breakthroughs across all areas of medicine, with artificial intelligence, virtual reality, and telemedicine brought to the fore. But something else is brewing that not a lot of people know about yet: Quantum computing, a completely new type of computing that has already begun to advance everything from drug development and disease identification to the security of electronic records.

“Think of it as transitioning from getting light through fire and candles and now having electricity, and there’s a light bulb that is lighting it all,” says Lara Jehi, MD, Cleveland Clinic’s chief research information officer.

What Is Quantum Computing?

Classical computers (aka binary computers), which are the foundation of today’s devices, including artificial intelligence and machine learning, work by using information known as bits. These appear as 0 or 1 (sometimes defined as off/on or false/true).

Quantum computers, on the other hand, use quantum bits known as qubits. And yes, the definition of “quantum” – as in: very, very small – applies.

International Business Machines, more commonly known as IBM, is currently leading this new tech. A common misconception about quantum computers is that they are “a next evolution of computers that will get faster,” says Frederik Flöther, PhD, life sciences and health care lead with IBM Quantum Industry Consulting. Instead, he wants us to look at quantum computing as something completely new “because it is fundamentally a different hardware, a different software, not just an evolution of the same.”

How does it work differently from existing computers? Quantum computing deals in nature. Therefore, qubits have to be based on the natural world. What does that mean? Nobel Prize-winning physicist Richard Feynman was famously quoted as saying, “Nature isn’t classical, dammit, and if you want to make a simulation of nature, you’d better make it quantum mechanical, and by golly it’s a wonderful problem, because it doesn’t look so easy.”

Nature, says Jehi, doesn’t work in black and white or fit into boxes.

“We have to convert it to zeros and ones because that’s what computers speak,” she explains. But quantum computing uses the principles of quantum mechanics. “It’s exactly how nature works, because it is based on the fundamental unit of everything in nature, which is atomic structure.”

Very, very small indeed. And that’s why quantum computing could be game-changing tech in medicine.

“Quantum computers can be used to represent a bunch of different solutions to a problem all at the same time, and then collapse down to the optimal solution, the one that actually works,” says Tony Uttley, president and chief operating officer with Quantinuum, a collaboration between Cambridge Quantum and Honeywell Quantum Solutions that is working to drive the future of quantum computing. “And the reason it does that is because of some fabulous properties of quantum physics.”

Establishing a Quantum Computing Beachhead

Scientists around the globe are studying quantum computers and looking into how they can harness this technology to make some big gains in the medical world.

IBM has created the IBM Quantum Network and is partnering with different organizations, from startups to Fortune 500 companies, to develop and test technology in various settings. One of these partnerships with the Cleveland Clinic is set to establish the “Discovery Accelerator,” focused on advancing health care through high-performance computing on the hybrid cloud, quantum computing technologies, and artificial intelligence.

Many people around the country are now using this technology on existing computers by tapping into the cloud, but with limited qubit access. IBM has researchers in places like Germany and Japan working on quantum computers and will be installing the country’s first of IBM’s next-generation 1,000+ qubit quantum systems on the Cleveland Clinic campus, which they are planning to use to help further investigate quantum computing’s many predicted benefits.

But what are those benefits?

Drug Discovery and Development

Quantum chemistry is one main area quantum computing is poised to help.

“The immediate application of that would be in drug discovery,” says Jehi. When scientists make drugs, they sit in a lab and develop different chemical formulas for what might constitute that drug.

“But for us to really know if it’s going to work, we need to be able to imagine how that chemical composition will translate into a structure,” she says.

Even in their most powerful form, today’s supercomputers are slow in their ability to change this chemical formula on paper to a simulation of what the chemical compound will look like. And in many cases, they can’t do this type of analysis.

“So, we end up making the drugs without knowing exactly how they’re going to look, which is not really the optimal way of creating a drug you expect to work” explains Jehi. “It’s a waste of time creating compounds that aren’t going to have any effect.”

Quantum computers will allow researchers to create and see these molecular structures and know how they bind and interact with the human body. In effect, they’ll know if a potential drug will work before ever having to physically make it.

Because of its differences from classic computing, quantum computers are not limited in their ability to simulate how different compounds can appear. Being able to simulate the compounds that drugs are made of can lead to a faster discovery of medications to treat a wide range of conditions.

Disease Analysis

Eventually, this technology could assist with disease analysis, working on a molecular level to allow computers/AI to contemplate, for example, cancer molecules and gain a deeper understanding of how they function.

Jehi says quantum computing can also be used to study things like chronic illnesses. These are conditions that people must live with and manage, and how a person is feeling in this instance can vary day-to-day, based on things like what a person is eating, the weather, or medications they are taking.

“There are so many different possibilities for what could change a patient’s trajectory in one way versus another,” says Jehi.

She stresses that if we have a group of patients, and we’ve captured everything that’s happened to them along their disease journey, it’s very challenging to mimic what that group looks like, and then study the effects of these different interventions on it using traditional computing.

“It just gets way too complicated, and the computers that we have can’t keep up with analyzing the effects of the different possibilities. It gets jumbled up,” Jehi says.

But quantum computing can offer quantum machine learning, meaning you use this special quantum ability to handle different simulations and different possibilities.

Cleveland Clinic, for instance, is looking at how some patients who undergo general surgeries have heart complications after their procedures.

“It would be transformative if we could identify ahead of time who is at highest risk of having a heart attack after surgery, as so we could take care of those people better,” she says.

The clinic’s current data set includes records for 450,000 patients, and current AI/machine learning makes sifting through this very slow and complex. The clinic is using machine learning approaches to create a synthetic data set, a smaller group that is a replica of the much larger one. Quantum technology could improve and speed this analysis to produce models that better perform.

Disease Detection

“Imagine you go get a CT scan,” says Uttley. “There are already AI solutions that you can run that set of images through and ask, ‘Does this look like something that would be cancer?’” This existing technology, he explains, works well on things that are typical and have been identified before, because that’s how machine learning works. If AI has seen something 100,000 times, it can often find something else that looks like it.

But today’s classical computers aren’t equipped to identify something unfamiliar. “Those are places where quantum computers can be much better at thinking of images and being able to say, ‘I can detect rare cancers or rare conditions that you don’t have a huge library of things that look like that,’” Uttley says.

This is also where researchers can use a quantum computer to be able to figure out what things could look like.

“The beauty of quantum computing is that it is a bias formation in quantum physics, this more probabilistic design. And so you can take advantage of that probabilistic design to help them think about this,” Uttley says.

How Far Out Are We?

Uttley says we’re in an emergent era of quantum computing. Quantum computers exist and that’s a big deal, but a lot of this technology is still in fairly early stages.

“It’s a little bit like we’re at the beginning of the internet and saying, how are things going to play out,” he explains.

Right now, companies like Quantinuum are striving to perform computations on both a quantum and classic computer, compare the results, and say, “We’re getting the same answer.”

“So, this is the era where we’re able to build trust and say these quantum computers are actually working correctly,” Uttley explains.

In the future, he says, we can possibly imagine something like a quantum MRI that is able to understand your body in a way that transmits that data to a quantum computer to detect what’s wrong, and be able to tell the difference between cancerous and non-cancerous. That will allow faster treatments and tailoring them to specific patient populations.

“What we’re doing today might seem slightly less sexy than that, but is maybe even equally important,” says Uttley.

This is using quantum computers to make the best encryption keys that can be made. The medical community, which is already using quantum computing to execute this, is excited about this being a better means of keeping patient data as secure as possible.

In June, Quantinuum launched InQuanto, which is quantum computing software that is allowing computational chemists, who, until now, only had classical computers at their fingertips. The move created an opportunity to start thinking about the problems that they worked on and what they would do with a quantum computer. As quantum computers become higher-performing over the years, Uttley says the software will go from tasks like isolating one molecule to solving larger problems.

“That will happen over this next decade, where I think we’ll see the first kind of real use cases come out in the next likely 2 to 3 years,” he says. For now, this technology will likely be used in tandem with classical computers.

Uttley says that progress in the quantum world and medicine will continue to grow at a slow and steady pace, and in years to come, we’ll likely see things start to click and then eventually, this to take off “full force.”

[ad_2]

Source link

November 22, 2022
Your Next Colonoscopy Could Get an Assist From AI

[ad_1]

Nov. 11, 2022 – Artificial intelligence holds a lot of potential in medicine, helping doctors find skin cancer, flagging potential issues on a chest X-ray, and assisting in many other procedures. Screening for colorectal cancer during a colonoscopy is another prime example.

A colonoscopy — recommended for Americans at average cancer risk starting at age 45 — won’t be much different for patients with the addition of artificial intelligence, or AI. But behind the scenes, AI could be making detection of any precancerous polyps or cancerous lesions more likely.

“AI-enhanced colonoscopy effectively turbocharges the physician’s ability to find even the most subtle precancerous polyps,” says Tyler M. Berzin, MD, a gastroenterologist at the Center for Advanced Endoscopy at Beth Israel Deaconess Medical Center in Boston.

The technology is designed to flag anything the computer “sees” as suspicious, but it does not replace the training and expertise of a gastroenterologist. Even with AI, doctors remain at the patient’s side and perform the procedure.

The doctor remains in full control, says Prateek Sharma, MD, a gastroenterologist and professor of medicine at University of Kansas School of Medicine in Kansas City, KS. “AI is assisting and alerting them to colon polyps — the precancerous lesions in the colon — so that the doctor can remove them.”

Controversy Continues

Size, height, and numbers matter with polyps. Doctors generally remove or biopsy lesions 10 millimeters and larger.

But there remains less consensus about the best approach to smaller polyps.

“The clinical relevance of detecting and removing small (5 to 9 mm) or diminutive (less than 5 mm) adenomas is a subject of ongoing debate,” Berzin and co-authors wrote in a leading gastroenterology journal in May 2020.

One of the potential disadvantages to using AI polyp tools, for example, is “the risk of removing a higher number of diminutive or hyperplastic polyps, which increases cost and risk, without any benefit to the patient,” Berzin says.

“Trained gastroenterologists are experts at identifying and removing precancerous colon polyps,” Berzin says. “But a gastroenterologist working with an AI polyp detection tool has a big advantage because AI computer vision tools can simultaneously analyze every pixel of the endoscopy monitor and can do so without being distracted or fatigued for even a millisecond.”

The benefit for patients is “another pair of eyes looking for polyps and helping the doctor,” says Sharma, who is also chair of the Artificial Intelligence Task Force at the American Society for Gastrointestinal Endoscopy.

How It Works

AI is based on computer instructions called algorithms that learn the difference between worrisome and benign colonoscopy images and videos. AI gets better over item in a process called machine learning. When an AI system spots a potential area of concern, the technology calls attention to it by framing it within a box on the screen. Some systems also sound an audible alarm.

“We are seeing more interest in using these algorithms since they will standardize endoscopists’ polyp detection and, therefore, reduce the number of colon cancers missed,” says Sravanthi Parasa, MD, a gastroenterologist at the Swedish Health Services in Seattle.

“These products are slowly gaining traction. During colonoscopy scheduling, patients should ask if their endoscopist has access to augmented diagnostic tools,” she says.

The technology is not accurate 100% of the time – there can be false positives where the system flags a bubble in the colon, for example, as potentially dangerous. That’s just one reason that doctors still have the final say on whether a polyp is suspicious or not.

AI or no AI, “colonoscopy has long been our most effective tool for preventing colon cancer, detecting precancerous polyps earlier than any other screening method,” says Berzin, who is also an associate professor of medicine at Harvard Medical School.

AI Can Be Costly

AI and machine learning already play a role in “smart” technologies (smartphones, smartwatches, and smart speakers), self-driving cars, and speech recognition software. But the use of AI in medicine is comparatively new. And like a lot of novel technologies, it’s also expensive. “The AI equipment needs to be purchased and is expensive,” Sharma says.

“The cost of the algorithms currently can be prohibitive for some centers in the current health care landscape,” Parasa agrees. “The cost is likely to come down as more algorithms enter the GI market, as it is with other software solutions.”

Colorectal Cancer Is Common

Not counting some kinds of skin cancer, colorectal cancer is the fourth most common cancer in Americans. It is also the fourth leading cause of cancer-related deaths in the United States, the CDC reports. More than 150,000 Americans will be diagnosed with colorectal cancer and more than 50,000 will die in 2022, according to National Cancer Institute figures.

Future Insights

More research is needed to examine how humans and this technology interact, Berzin says. “The most interesting research in this space will not be about comparing ‘physician versus AI,’ but will be focused on understanding the nuances of ‘physician plus AI.’”

In the U.S., there are at least three FDA-approved AI algorithms for polyp detection, and more are being developed, Parasa says.

“In addition, other applications which are currently available on the European market might be available in the U.S. market in the near future, including polyp characterization.”

“As the field matures, we will likely see more AI augmentation tools that will assist us in detecting and diagnosing GI conditions in real time,” she adds. “A suite of algorithms like this will definitely improve patient care and outcomes.”

Even though AI is somewhat of a work in progress in medicine, Berzin expects the combination of doctor and AI technology “will translate into the highest possible protection from colon cancer in the long term.”

[ad_2]

Source link

November 11, 2022