Artificial Intelligence

Turning the page on 2022 will be a cause for celebration in the healthcare sector.

The past year was one of the worst financial years on record for hospitals, according to Kaufman Hall. New data from the healthcare consulting firm and the American Hospital Association indicates that 53% to 68% of the nation’s hospitals will end 2022 in the red. At the same time, hospital employment is down approximately 100,000 from pre-pandemic levels.

This is all happening amid a backdrop of growing margin pressures and an aging population.

So, what will the coming year hold for healthcare organizations and their patients? And how can businesses in the healthcare sector best position for success in 2023 and beyond?

Let’s examine the situation, assess what 2023 will look like and identify the best treatment.

High costs will dissuade people from getting the care they need

Past experience shows us that in recessions, Americans are quick to cut routine visits and medical advice that comes at a cost. Expect continued media coverage on the questionable economy, recession nerves and layoffs to keep people away from healthcare in the year ahead.

Concerns about the economy and the fact that as many as 15 million Americans could lose Medicaid access when the pandemic ends could exacerbate the trend of people putting their health on the backburner to save time and money or try to avoid stress.

Staff shortages and wage demands will pack a one-two punch

Healthcare employees are stressed as well. A recent report explains that nurses are “beyond burnout.” This problem has prompted the launch of a multimillion-dollar burnout prevention program pilot. But research suggests that turnover is highest for health aides and assistants.

High burnout keeps employers struggling to recruit and retain staff. And increasing wages make it increasingly difficult for healthcare institutions to afford the help they need and turn a profit.

One of the reasons there aren’t enough people to serve patients and generate more revenue is because there’s a lot of friction in the current model. Rather than spending time with patients, healthcare workers have to dedicate significant time to dull, inefficient administrative processes. If healthcare organizations don’t address it, this problematic pattern will continue.

A growing number of healthcare companies will automate back-office work

In a move to improve their situation and that of all healthcare stakeholders, healthcare companies in 2023 will automate accounts payable, claims processing, collections and other back-office work. At the same time, health insurance providers will automate most of the administrative work associated with processing claims. This will be especially prevalent at mid-sized companies, many of which previously felt automation technology was out of their reach.

Automation will free up employees to spend more time serving patients, which is what attracted many of these workers to healthcare to begin with. It will enable healthcare organizations to know that administrative tasks are done exactly right every time. And it will allow healthcare organizations to improve efficiency and scalability and reduce their costs.

Typically, automation has been the domain of large organizations, which have the resources to do heavy integration work and bot maintenance. But now, platforms that don’t require such integration and continually optimize bots put automation within reach of mid-sized businesses.

In-person care will take a hit as more people embrace telehealth

Expect growing adoption of telehealth in the coming year and beyond. Many Americans now understand the value and ease of telehealth, which took off amid Covid-19 stay-at-home orders and dramatic policy changes. In the first year of the pandemic alone, 44% of continuously enrolled Medicare fee-for-service beneficiaries had a telehealth visit, totaling more than 45 million visits.

Baby boomers and those in dire scenarios utilize in-person visits most often. Chronic pain cases, mental health concerns and pain points of younger people – who will look to mobile-first experiences rather than considering physical locations – will funnel into telehealth.

Meeting patients where they are, rather than requiring them to travel or overcome other barriers to get service, will help patients and every other stakeholder in the healthcare system.

Advances in AI will take wearable technology, healthcare applications to the next level

Major wearables companies like Apple and Google Fitbit have amazing proprietary data sets. Recent artificial intelligence (AI) breakthroughs will allow these major wearable companies to use their unique data and devices to unlock new and even more exciting applications.

OpenAI’s new GPT-3 chatbot, which delivers more advanced results than people expected, is one sign of where things are headed. This signals that AI models are becoming more advanced.

To date, wearable technology has primarily involved consumer applications that track how many steps you take or capture your workout history. And with recent advances in AI modeling, we’re likely to see some interesting new use cases in the healthcare and insurance arenas over the next year. But now, the major differentiator won’t be how you interface with AI but rather who has the unique training data needed to unlock new experiences and applications for end users.

Technology will move healthcare in the right direction

Running a healthcare operation and delivering quality care to patients isn’t easy, as the past year clearly demonstrated. Inefficiency and unnecessary friction are a large part of the problem. And healthcare is far more expensive than it needs to be. The U.S. spends nearly twice as much as the average OECD country yet has some of the worst outcomes.

But, with the right technology, healthcare organizations in the year ahead can become more efficient, make quality care accessible to more people, reduce their recruiting and hiring costs, prevent mistakes, and deliver better outcomes for themselves, their workers and their patients.

0 comment
0 FacebookTwitterPinterestEmail

machine learning AI

The evolution of machine learning is still in its early stages, but at the HLTH 2022 conference this week, companies shared how they are working to fine tune their approaches to AI. Those efforts included everything from improving the quality of patient data that underpin the algorithms, which has been criticized for not reflecting a diverse enough patient population, to making it easier for healthcare organizations to validate their effectiveness. Health tech companies also highlighted different approaches they are taking to how they work with providers to pilot machine learning algorithms and market them.

Dr. John Halamka, president of Mayo Clinic Platform, used his talk at HLTH to highlight an initiative to assess and reduce bias in patient data to improve the effectiveness of machine learning algorithms. Launched three years ago, Mayo Clinic Platform built an ecosystem to coordinate collaborations with health tech companies to enable innovation in healthcare.

Halamka’s talk on the “algorithmically underserved” noted that currently, when healthcare organizations use an algorithm, they often have no idea whether it performs well or not. The goal of its AI validation platform, Mayo Clinic Platform _ Validate, is to provide clinical validation for machine learning algorithms.

Mayo Clinic Platform is also partnering with other healthcare organizations to set standards and reporting for models as part of The Coalition for Health AI. In addition to Mayo, other founding members include University of California Berkeley, Duke Health, Johns Hopkins University, MITRE, Stanford Medicine, and University of California San Francisco. Industry members include Change Healthcare, Google, Microsoft, and SAS.

Halamka announced that a webinar is planned for December 7, which will offer a preview of plans for a public-private partnership to create a national register to assess the usefulness of a variety of healthcare algorithms.

“We feel we need a national set of assurance standards for algorithms,” Halamka said. The registry will host the metadata for algorithms produced in healthcare.

AI marketplace

Health tech companies are also developing marketplaces to improve the way collaboration partners, such as providers, payers and research groups, select algorithms.

“Data may be the new oil but the data needs to be refined,” said Wavemaker Three-Sixty Health General Partner Jay Goss. One of its portfolio companies, Gradient Health, partners with medical data providers around the globe (generally hospitals and imaging centers) to curate annotated medical images for AI research labs and corporations, so that they don’t have to do one-off deals with hospitals to obtain the data. Companies can search through segmented and labeled studies, or request a custom dataset, spending less time tracking down data and more time developing new tools.

AI hubs

Ferrum Health developed a program to enable health systems to assess machine learning algorithms without exposing their de-identified patient data to a cloud or otherwise forcing them to centralize that data. The company, which is part of the United Healthcare Accelerator 2022 cohort, exhibited on the accelerator’s pavilion.  Ferrum’s approach enables these tests to be done on-premises, behind a firewall, an approach David Miller, Ferrum’s vice president of sales – West, said is designed to de-risk their business for hospitals and health systems. The algorithms in its marketplace are FDA approved.

“We run a test of the algorithms using the hospital’s de-identified patient data to show how they perform for them,” said West. “We let our clients try it before they buy it.”

The company’s four AI Hubs include: oncology, orthopedics, cardiovascular, and breast care.

Reducing physician burnout

DeepScribe exhibited as part of the Plug and Play accelerator’s footprint at the conference. Its automated physician natural language processing software automatically summarizes a physician’s conversation with their patient and auto-populates those notes into EMR fields. Among the EMR companies it works with are athenahealth, dr chrono, AdvancedMD and Claimpower.

Earlier this year, DeepScribe closed a $30 million Series A round to support the company’s growth. The business is designed to negate the need for an in-person medical scribe, saving clinicians money.

The validation approach to algorithms seems like a natural progression in machine learning, similar to the rise of digital health apps followed by the need to validate them to ensure adoption by healthcare organizations skeptical of overhyped tech. It’s a natural progression balancing the interest in machine learning with the recognition that healthcare algorithms are not created equal.

Photo: Hemera Technologies, Getty Images

0 comment
0 FacebookTwitterPinterestEmail

Almost every day, there is another headline touting the latest advancement in artificial intelligence (AI).

And almost every time, the subject of the story isn’t actually AI.

While we have made huge advancements in automation and natural language processing (which is usually what those articles are about), neither of these are really AI.

Both are crucial steps on the journey towards AI, but a home speaker that turns the lights on when I ask it to isn’t exactly intelligent – artificially or otherwise.

That’s not for want of trying. With Australia’s AI spending heading for $3.6 billion, and $44 million in Government grants being made available to develop AI and digital capability centres, it’s a destination we’re determined to reach, but we have not moved as far over the past decade, as many would believe.

The challenge lies in how we define AI. In its simplest form, it should be a program that is able to draw on its own past experiences, think for itself, and come up with new answers, creations, or processes on its own.

Some call this level of advancement ‘singularity’ or ‘General AI’, but can anything less really be called artificial intelligence?   

Today’s breed of ‘AI’ are algorithms with a finite and defined purpose – these are also known as ‘weak’ or ‘narrow’ AI. They are constrained by how they have been programmed and essentially boil down to a series of ‘if x, then y’ commands.

The machine learning and automation we have today definitely make our lives easier, but it is reactionary – it needs to be told what to do and how to do it before acting.

Take shopping centre parking lots as an example.

What is currently being described as ‘AI’ are programs that monitor how many parking spaces are occupied and how many are free. Perhaps it does this through sensors in each spot; maybe this is achieved through object recognition via smart CCTV. Either way, the end result is something like the occupied spots displaying a red light above them, those that are still available showing a green light, and an LED screen tallying up the remaining free spots to display them at the car park’s entrance.

While this is an impressive feat of automation and convenient for shoppers, it is far from AI.

It still relies on an algorithm that essentially tells the program that if there is a car in a spot, show a red light; if there is not, show a green light, then count the free spots and display that number (if x, then y).

True AI in the shopping centre car park would make far more intelligent decisions without necessarily being told what it should be looking for. What might this look like?

By analysing the makes and models of each car entering the car park, it could make an estimation of the average purchasing power of shoppers, discover the trends for when different cohorts are at the shopping centre, and advise each store on optimum staffing levels.

To make this more accurate, it might track the brands on the shopping bags patrons are leaving with, drawing on that experience over time to uncover when specific stores are likely to be busier. 

On the loss prevention front, it might identify that a recently found shoplifting patron entered the car park and could notify stores to be vigilant. Perhaps it could identify when a stolen car, or a car with stolen license plates, enters the facility.

If it identifies that more parents take their children to the centre at particular times and days, this insight could be shared with centre management to make sure the big fluffy mascot makes an appearance or children’s entertainers are prioritised at that time.

With a true AI, the point is that we wouldn’t necessarily know what insights it would discover or what connections it would make between seemingly disparate data points.

A big hurdle between where we are today and where we think we are is the bottleneck of computing and storage.

For an AI to be able to take in all its past experiences and all the necessary data points and create its own thought processes, it needs truly massive amounts of data it can analyse immediately.

Until we figure out how to overcome this bottleneck, we’ll continue making better mouse traps – but they won’t be telling us anything new about mice.  

Keep up to date with our stories on LinkedInTwitterFacebook and Instagram.

0 comment
0 FacebookTwitterPinterestEmail

Ochre Bio co-founders Quin Wills, chief scientific officer (left), and Jack O’Meara, CEO (right). Photo by Business Wire.

A biotech startup developing RNA therapies for liver disease by running tests in live human livers has raised $30 million to identify a drug candidate to test in real live humans.

Ochre Bio conducts its research with donor livers. These livers aren’t suitable for organ donation, but they can still be used for drug research. These livers are kept “alive” for days at a time by machines that mimic physiological conditions. Using machine learning, the biotech analyzes the liver data in order to identify and validate targets for its drugs. The company describes its approach as “deep phenotyping.” The company is developing therapies to treat chronic liver disease.

So far, Ochre says its work so far has generated data from more than 1,000 diseased human livers. With the Series A round of financing announced Monday, Oxford, U.K-based Ochre plans to turn insights from its research into RNA drug candidates. Those drugs will be tested in livers kept alive in New York. Last year, the company opened Liver ICU, a research site at BioLabs@NYULangone, a research incubator. Ochre says results from this “human liver preclinical testing” in 2023 will inform which therapies will advance into clinical tests in 2024. The research will also be used to expand its research to more liver diseases. Longer term, the startup aims to put a dent in the need for donor organs, a growing problem as demand increasingly outstrips supply.

“Humans are the model throughout our R&D, from large scale genomic atlases to testing our therapies in donor human livers maintained on machines,” co-founder and Chief Scientific Officer Quin Wills said in a prepared statement. “Our pipeline helps us converge on therapies that regenerate poor quality donor livers, so more people have access to better quality organs, faster. In the future, we have a goal to directly regenerate organs in patients, removing the need for organ transplants altogether.”

Ochre Bio was founded in 2019. The startup is a graduate of the Y Combinator accelerator. The new funding round announced Monday follows a $9.6 million seed financing last year led by Khosla Ventures. That firm invested in the latest financing, which included participation from Hermes-Epitek, Backed VC, LifeForce Capital, Selvedge, AixThera, LifeLink. The new financing also added individual investors Alice Zhang, CEO of Verge Genomics; Kristen Fortney, CEO of BioAge; and Marty Chavez, chairman of Recursion Pharmaceuticals.

0 comment
0 FacebookTwitterPinterestEmail

provider venture capital, money

If Americans thought healthcare was costly pre-pandemic, they—and their wallets—are about to be hit with a harsh reality: the cost to treat patients is expected to increase 6.5% this year, slightly higher than pre-pandemic, according to PwC’s Health Research Institute. What should be even more concerning is that a significant portion of what the United States spends on healthcare annually is lost to fraud, waste and abuse.

The most egregious cases of fraud and abuse aren’t common, but they make an impact – and sting all who are striving to make healthcare safer and more affordable. Take for example, the case of the oncologist who prescribes chemotherapy to patients without a cancer diagnosis. Or the cardiologist who conducts stent surgeries without need. These are real fraud cases that artificial intelligence (AI) technology uncovers for healthcare payers. While basic medical coding errors and Covid care upcoding are much more common, the grim reality that in 2021 alone, the Department of Justice (DOJ) recovered more than $5 billion from civil fraud and false claims cases, just a fraction of the estimated $380 billions lost every year. This is a staggering number that’s only getting bigger.

Five key areas of concern

Executive healthcare payer leaders recently gathered at a virtual roundtable to identify how the pandemic has shifted their views about medical costs, telehealth, virtual care, and technology investments related to fraud, waste, and abuse.

Here are the valuable takeaways from their discussion:

  1. Many factors influence rising healthcare costs, but top drivers are Covid testing and treatment and staff shortages.
  2. While overall utilization of healthcare is still down about 3-4%, utilization in lab work has increased dramatically by 15%. Surging lab work must be monitored by healthcare providers because it is driving an abundance of fraud.
  3. Covid care and treatment services are being upcoded, such as ordering respiratory panels or billing for full patient evaluations when only a Covid test was necessary. For example, in May 2021, the U.S. Department of Justice announced criminal charges against 14 defendants across the United States who exploited the Covid pandemic and resulted in more than $143 million in false billings.
  4. Staff shortages during Covid have also impacted access to care, which is creating a backlog that’s expected to last for at least two to three years.
  5. With a rise in the usage of telehealth, it’s vital to monitor evaluation and management (E&M) upcoding, where providers claim to provide a higher level of service than delivered or even claim to offer services that simply aren’t possible unless the patient was physically seen by the doctor.

How technology can be part of the solution

The adoption of AI, machine learning (ML), and automation to monitor fraud, waste, and abuse in healthcare is growing. Healthcare payers, agencies, and pharmacy benefits management organizations are realizing the value proposition of these technologies because they deliver transparency across the payment spectrum and create a unified view across claims, providers and patients for proactive integrity cost containment.

Here are some of the ways that healthcare payer executives are maximizing technology in their day-to-day operations:

  • AI and ML are helping to tackle complex issues such as getting accurate billing, code auditing and finding complex areas that historically relied on manual discovery. This is a major win with the existing staffing shortages.
  • AI’s potential for early detection and automation is another reason to invest—especially when it comes to fraud related to Covid care and treatment.
  • AI and other technologies can create a more cohesive and holistic approach by enabling organizations to better communicate the value of payment integrity programs to providers and customers.

Catching those who are responsible for fraud, waste, abuse and errors is an act of social good. As we chart a new course in this post-pandemic era, healthcare leaders who join forces to explore the power of AI, ML and automation are directly helping to break through barriers that have held us back far too long. When we embrace innovation together we can transform healthcare to deliver more effective and affordable care for all.

Photo: adventtr, Getty Images

0 comment
0 FacebookTwitterPinterestEmail

There’s an ocean worth of artificial intelligence-powered medical notetaking companies out there, from Nuance to DeepScribe to Suki to Corti. But Abridge contends that it’s different and investors appear to be listening.

On Thursday, Abridge announced it had closed an oversubscribed $12.5 million Series A funding round. Wittington Ventures led the round, which also had participation from UPMC Enterprises, Union Square Ventures, Bessemer Venture Partners, Pillar Venture Capital, Whistler Capital and Turing Award winner Yoshua Bengio.

The startup, which is based in Pittsburgh, was founded in 2018 by UPMC cardiologist Shiv Rao and two Carnegie Mellon University researchers, Sandeep Konam and Florian Metze. They created the company to alleviate physician burnout by decreasing “pajama time,” which refers to the hours clinicians spend entering clinical notes after work. U.S. physicians spend an average of nearly 2 hours each day completing documentation outside work hours, according to a recent study in JAMA Internal Medicine.

Rao argued that healthcare is driven by conversations. But he said neither physicians nor patients can remember these conversations very well.

“There’s good research that shows people forget up to 80% of what we’ve heard from doctors or nurses,” Rao said. “But the challenge is also significant on the professional side. We forget so much of what we tell patients, and then we have this huge challenge at night when we’re writing notes.”

Abridge now offers technology to help both patients and physicians with this problem. The company’s first solution, rolled out in 2020, is a patient app that listens to visits and bookmarks parts of the conversation where a physician gives instructions or next steps. The tool lets patients listen to a recording of the conversation and gives them a transcript of it. It can also provide patients with more information on their physician’s recommendations, such as what a certain procedure entails or the side effects of a suggested medication. About 20,000 patients use the app.

In conjunction with the announcement of its Series A funding, Abridge also launched a new solution for physicians. The tool listens to visits and creates a near-instant summary for physicians that follows their prototypical note structure, along with a full transcript of the conversation.

For example, say a cardiologist has a 30-minute visit with a patient. About 30 seconds after the conversation ends, the physician will be able to access an accurate, easy-to-read summary of the visit through the app. This summary weeds out all the irrelevant “small talk” parts of the conversation and includes categories for the patient’s allergies, medications, social history, symptoms, treatment plan and next steps, Rao said.

Abridge’s AI was trained on a proprietary dataset derived from more than 1.5 million medical visits. More than 2,000 physicians already use the startup’s physician app, according to the company.

As Abridge works to grow the number of physicians using its technology, it will have to stand out from the competition. Rao said there are a few key ways his company does this.

All of the startup’s competitors create transcripts and then use human scribes to put together useful notes, Rao claimed. Abridge is different because its technology produces a note summary at the same time as the visit transcript. The company is also the only one to integrate with telemedicine and call centers, whereas its competitors only work with in-person conversations, Rao declared.

He also argued that Abridge publishes more peer-reviewed papers on note-related physician burnout and how its technology is working to solve it than any other competing company. Rao said this was important because all healthcare AI companies should make an effort to be more transparent.

The last advantage Rao said Abridge has is the fact that it’s always ready to do live demos with prospective customers. As a former corporate venture capitalist at UPMC Enterprises, Rao said he was always surprised that none of the companies his health system was funneling millions into would offer them a demo of any sort. 

After viewing a live demo, I could see how they could be attractive to providers. During the faux telemedicine visit we had, I got to see how quickly and accurately the tool worked. Using our less-than-stellar Zoom audio, the tool produced instant and accurate notes detailing every relevant aspect of our conversation.

UPMC is one its customers, and Rao teased the fact that Abridge will soon announce partnerships with one of the nation’s biggest health plans and one of its biggest pharmacy chains. Guess we’ll just have to wait to find out.

Photo source: Abridge

0 comment
0 FacebookTwitterPinterestEmail

heart, doctor, cardiac

Novartis’s main offerings for cardiovascular disease are drugs prescribed as a response to symptoms. However, heart problems can develop years before signs appear and the pharmaceutical giant wants to find new approaches to these disorders. A new partnership aims to develop artificial intelligence-based software that detects hidden cardiovascular conditions.

The collaboration is with Anumana, which develops algorithms that are applied to electrocardiograms (ECG). The agreement calls for Cambridge, Massachusetts-based Anumana to develop algorithms that could be applied to patients with previously undetected life-threatening heart disease. These algorithms are intended to help identify problems so physicians can intervene sooner.

“Cardiovascular disease is a widespread and multifactorial disease and, in order to mitigate its impact, we must look beyond therapeutic innovation and reimagine how we approach cardiovascular care,” Victor Bulto, president of Novartis Innovative Medicines U.S., said in a prepared statement. “Novartis is proud to collaborate with Anumana on innovative and data-driven solutions to better predict the risk of life-threatening heart disease, further driving forward our commitment to improving patient experiences and population health outcomes in this patient population.”

No financial terms of the multi-year collaboration were disclosed.

Anumana was formed by the Mayo Clinic and Nference, a company that uses unstructured electronic medical records data from medical centers to develop new diagnostics and treatments. The Anumana joint venture builds on an existing partnership between the Mayo Clinic and Nference. They launched the startup last year and backed it with a $25.7 million Series A investment. Anumana says it has since closed a $60 million Series B round of funding.

The Novartis collaboration could make the ECG, a widely used and inexpensive test, a wealth of information for cardiovascular analysis. Anumana said the alliance builds on its own efforts to develop AI-enabled software that detects signals from ECGs that humans cannot interpret. The new partnership is focused on developing software that can detect previously undiagnosed left ventricular dysfunction, which is also referred to as a weak heart pump. This condition can lead to heart failure.

In addition, Anumana said that the AI will screen for atherosclerotic cardiovascular disease, which can lead to heart attack and stroke. The research includes development of a digital point-of-care solution that can guide the use of drugs. The goal is to reduce the risks of hospitalizations and cardiovascular death. Under the Novartis partnership, Anumana will work with experts at the Mayo Clinic.

“This collaboration has the potential to transform the use of a ubiquitous inexpensive test, the ECG, with the aim of democratizing disease detection and helping medical care teams to proactively manage heart disease ahead of time and prevent some clinical events from ever happening,” Dr. Paul Friedman, chair of the department of cardiovascular medicine at Mayo Clinic and chair of Anumana’s Mayo Clinic board of advisors, said in a prepared statement.

0 comment
0 FacebookTwitterPinterestEmail

Opinions expressed by Entrepreneur contributors are their own.

To date, AI has gone through three phases of its development. Descriptive analysis to answer what happened, diagnostic analytics to answer why it happened and predictive analytics to answer what might happen next. The analytical and forecasting power of AI has increased tremendously, but it won’t stop there. 

The problem with the current generation of AI is it needs to receive data from humans. This reduces the problem-solving power of artificial intelligence in dealing with new events. There is no denying that AI has made it easier for humans to predict the future. But, dealing with future events, like another pandemic, needs a more powerful AI. 

0 comment
0 FacebookTwitterPinterestEmail
This article was translated from our Spanish edition using AI technologies. Errors may exist due to this process. Opinions expressed by Entrepreneur contributors are their own.


Innovation is essential for success. With the Mexican Grand Prix approaching soon, as it will take place on November 7, these innovations will be the center of attention, highlighting some of the most incredible feats of engineering: Formula 1 (F1). The fastest team will largely depend on your ability to innovate to win the race.

For the McLaren F1 team, that means perfecting the car to stay ahead of the competition. Every 17 minutes, McLaren creates a new part for its car. And, at the end of the season, 80% of the car will be completely different. With more than 180 Grand Prix victories and 20 championships, this strategy has served the historic McLaren team well.

But innovation is also critical to winning the race in cyberspace. To outcompete cyber attackers, organizations must invest in technologies that prioritize research and development, deploying technology to combat the growing number of increasingly sophisticated threats.

Protecting McLaren’s crown jewels

The constant evolution of the car makes McLaren’s intellectual property – that is, the car’s designs and its performance characteristics – its most prized jewel. Just as a startup’s business model or product designs are crucial to any entrepreneur, McLaren’s proprietary data is its most precious resource.

Both entrepreneurs and companies must take the necessary precautions to ensure that criminals cannot steal your ideas. Hackers don’t care about legal frameworks or patents. They will do whatever it takes to cause harm.

A successful cyber attack could cause serious problems for any organization. These attacks could lead to a deterioration in reputation, significant capital losses, or the theft of ideas that are already patented. For McLaren or any other team, the damage that a cyberattack would cause – ranging from access to intellectual property, to the competition strategy or the data from the sensors connected to the cars – could be the difference between winning or losing.

The pace is so fast in racing that F1 teams need technology and innovation to keep up. Proper cybersecurity measures are critical to ensuring intellectual property protection, ensuring equipment can function, and most importantly ensuring victory is achieved.

Cyber Impacts On The Runway: Preventing Closure

Beyond data loss, cyberattacks can have physical consequences in the racing world. A successful cyberattack could disrupt the activities of a business entirely. The closure of any activity for a period of time would be unsustainable.

But for McLaren, not taking the car out on the track is inconceivable. A shutdown attack on a race weekend is the kind of situation that keeps McLaren leaders up at night.

Organizations must understand that hackers will eventually infiltrate. More important than building perimeter defenses with legacy technologies such as firewalls, companies must focus on mitigating the spread of threats and minimizing damage to avoid a shutdown.

Flattening the tires on cyber attackers: Stopping threats on the fly

The digital environment of an entrepreneur, like that of F1, moves at high speed: multiple processes and activities happen simultaneously. For that reason, leveraging artificial intelligence (AI) -based solutions for cybersecurity defense is vital.

The McLaren team uses AI technology to ensure the defense of its digital infrastructure. These artificial intelligence-based cybersecurity solutions automatically alert security teams to threats in their digital infrastructure. These real-time alerts allow security teams to focus their attention on responding to and remediating threats.

Especially during race weekends, it is important that the entire employee base – from the CEO to the team in the box – does not waste valuable time evaluating whether an email or other communication is authentic. They need to trust AI to examine that data for them.

This autonomous responsiveness allows the team to focus on more complex security tasks, without having to worry about relying on the decisions of a single individual to protect the entire company and its infrastructure. Not only is one person at risk by clicking on a suspicious link. The entire company is at risk.

A change in the race venue implies a change in the digital environment

Cybersecurity based on artificial intelligence (AI) is the best way to secure complex and sophisticated digital environments, such as a growing startup or a Formula 1 team. Every weekend, McLaren is in a different place, on a circuit different races.

The most successful AI has the ability to learn about the regular business operations of an organization, thereby identifying abnormal behaviors for that specific environment. In that sense, AI can prevent partial or total business interruption, data theft and other negative repercussions of a cyber attack. This type of AI, called “self-learning”, can adapt as its environment changes, which in the case of F1 is very common.

McLaren learned that it was essential to adopt this type of technology in advance. As attacks and their perpetrators become more sophisticated, defensive technologies need to rely on innovation to stay ahead of threats.

Every entrepreneur and businessman should follow McLaren’s example: embrace new technologies to defend and protect their ideas and their work. McLaren took decisive steps to ensure its cyber integrity; companies should too.

0 comment
0 FacebookTwitterPinterestEmail

April 5, 2021 6 min read

Opinions expressed by Entrepreneur contributors are their own.

At a Fintech conference in put on by Fordham University in the spring of 2017, an AI expert made a bold prediction: Someday there would be a company with a market cap of one trillion dollars. He predicted that this valuation, which at the time seemed incredible, would be based on that firm’s extensive use of AI.

He was correct in at least one regard: became the world’s first trillion-dollar company a little over a year later. But what of the second part of the prediction? Was Apple’s staggering valuation due to the power of AI? Are AI and, more broadly, , the key drivers of business growth?

Apple uses data analytics and AI extensively. combines speech recognition and expert systems to give you reminders based on your location. studies your listening habits and assembles playlists accordingly. Apple Fitness+ uses data from the Apple watch to help users build health. In 2018, Apple’s head of AI, John Giannandrea, was appointed to the company’s executive team.

Yet the same press release announcing Giannandrea’s appointment offers a fundamental insight into why Apple has been so successful. It notes that Apple “leads the world in innovation” — not AI. The company has spent decades creating entirely new product arenas and pioneering new business models around music sales (), app subscriptions (the ), cloud storage (), and digital payments (Apple Pay). It’s easy to forget that just 20 years ago, computers made up nearly all of Apple’s business. Last year, Mac products contributed just 10.4% of the company’s revenue.

Human creativity is visionary in ways that AI can’t be

Apple’s new products and business models relied on creativity and the ability to see beyond the known, not or AI. Creative leaders like and could see the deficiencies in portable MP3 players, but no algorithm could have told them how to build an entirely new way to listen to music. The iPod, the , and the iPad emerged from their ability to envision ways to apply new technologies and their outstanding sense of user-centric design. Services like iTunes and the App Store stemmed from the company’s commitment to provide entirely new and valuable experiences for consumers. Innovation, not AI or analytics, generated the returns that drive Apple’s trillion-dollar valuation.

Related: Machine Learning and Artificial Intelligence to Revolutionize the …

Nor could data be expected to produce such results. Quantitative techniques, even sophisticated ones like deep learning, are backward-looking, highly constrained, and reductionist. They begin with an established dataset and seek the best answer from a limited number of pre-defined choices. The very nature of data-driven tools makes them unsuited to coming up with bold, disruptive innovations — the kind of breakthroughs that lead to something entirely new. For example, one of the most promising AI technologies at the moment is the GPT-3 “few-shot” learning model, which has shown a modest ability to do some creative tasks like generating synthetic news articles and computer code. Today, though, its domain is primarily limited to natural language processing — and even that uses a model with 175 billion parameters.

Breakthrough innovation occurs because of ideas sparked by a serendipitous conversation, an unexpected finding in a lab, or the ability to connect disparate pieces of information from very different domains into a keen insight. When Jony Ive was hired at Apple, he had designed products ranging from telephones to toilets but had done nothing in the computer industry. Yet he was at the heart of Apple’s new product successes for two decades because of his ability to envision how new technologies could build a world that did not yet exist.

Reliance on AI can actually hurt more than it helps

The danger companies today face is that an over-emphasis on AI and quantitative tools can potentially hinder breakthrough innovation. If each decision must be driven by data, how will a firm create something for which there is no relevant data? No algorithm can justify investing in and launching a breakthrough innovation.

Related: How Artificial Intelligence Is Helping Fight The COVID-19 Pandemic

Moreover, a firm’s bandwidth for business improvement can be consumed by the use of analytical tools. In such cases, incremental innovation will rule the day. Renowned computer scientist Melanie Mitchell summarizes the trap that businesses fall prey to: “The race to commercialize AI has put enormous pressure on researchers to produce systems that work ‘well enough’ on narrow tasks.” Quantitative tools are powerful and exciting, but they can come to dominate a firm, keeping it focused on narrow tasks to the detriment of breakthrough innovation.

This isn’t to say that there is no role for data analysis. Data tools are enabling technologies that can improve and extend existing products. Firms should use AI technologies within their breakthrough innovations. For example, while there is no algorithm that could have taken mobile phone data in the early 2000s and come up with the iPhone, the AI-driven assistant Siri added value to this breakthrough innovation. AI is one of a number of functional areas at Apple, where it sits alongside software, hardware engineering, design, and other business areas.

The importance of breakthrough innovation is further illustrated by the other members of the trillion-dollar club. Amazon, Alphabet, and Microsoft built their success not on AI or big data, but on breakthrough innovations that transformed the way we shop, work, and consume information. These companies have benefited substantially from analytical capabilities, but they are largely selling products that sprang from human .

The surest path to success isn’t through incremental advances, but breakthrough innovations. While a trillion-dollar firm built on AI may someday rise, it’s not here yet. Companies should not let the shininess of AI and big data distract them from the importance of human processes like creativity and discovery to unlocking breakthrough innovation.

Related: What Is Artificial Intelligence? Whether You’re a Student …

0 comment
0 FacebookTwitterPinterestEmail
Newer Posts