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As healthcare businesses transform for a post-COVID-19 era, they are embracing digital technologies as essential for outmaneuvering the uncertainty faced by businesses and as building blocks for driving more innovation. Maturing digital technologies such as social, mobile, analytics and cloud (SMAC); emerging technologies such as distributed ledger, artificial intelligence, extended reality and quantum computing (DARQ);and scientific advancements (e.g., CRISPR, materials science) are helping to make innovative breakthroughs a reality.

These technologies are also proving essential in supporting COVID-19 triage efforts. For example, hospitals in China are using artificial intelligence (AI) to scan lungs, which is reducing the burden on healthcare providers and enabling earlier intervention. Hospitals in the United States are also using AI to intercept individuals with COVID-19 symptoms from visiting patients in the hospital.

Because AI and machine learning (ML) definitions can often be confused, it may be best to start by defining our terms.

AI can be defined as a collection of different technologies that can be brought together to enable machines to act with what appears to be human-like levels of intelligence. AI provides the ability for technology to sense, comprehend, act and learn in a way that mimics human intelligence.

ML can be viewed as a subset of AI that provides software, machines and robots the ability to learn without static program instructions.

ML is currently being used across the health industry to generate personalized product recommendations to consumers, identify the root cause of quality problems and fix them, detect healthcare claims fraud, and discover and recommend treatment options to physicians. ML-enabled processes rely on software, systems, robots or other machines which use ML algorithms.

For the healthcare industry, AI and ML represent a set of inter-related technologiesthat allow machines to perform and help with both administrative and clinical healthcare functions. Unlike legacy technologies that are algorithm-based tools that complement a human, health-focused AI and ML today can truly augment human activity.

The full potential of AI is moving beyond mere automation of simple tasks into a powerful tool enabling collaboration between humans and machines. AI is presenting an opportunity to revolutionize healthcare jobs for the better.

Recent research indicates that in order to maximize the potential of AI and to be digital leaders, healthcare organizations must re-imagine and re-invent their processes and create self-adapting, self-optimizing living processes that use ML algorithms and real-time data to continuously improve.

In fact, theres consensus among healthcare organizations hat ML-enabled processes help achieve previously hidden or unobtainable value, and that these processes are finding solutions to previously unsolved business problems.

Despite these key findings, additional research surprisingly finds that only 39% of healthcare organizations report that they have inclusive design or human-centric design principles in place to support human-machine collaboration. Machines themselves will become agents of process change, unlocking new roles and new ways for humans and machines to work together.

In order to tap into the unique strengths of AI, healthcare businesses will need to rely on their peoples talent and ability to steward, direct, and refine the technology. Advances in natural language processing and computer vision can help machines and people collaborate and understand one another and their surroundings more effectively. It will be vital to prioritize explainability to help organizations ensure that people understand AI.

Powerful AI capabilities are already delivering profound results across other industries such as retail and automotive. Healthcare organizations now have an opportunity to integrate the new skills needed to enable fluid interactions between human and machines and adapt to the workforce models needed to support these new forms of collaboration.

By embracing the growing adoption of AI, healthcare organizations will soon see the potential benefits and value of AI such as organizational and workflow improvements that can unleash improvements in cost, quality and access. Growth in the AI health market is expected to reach $6.6 billion by 2021 thats a compound annual growth rate of 40%. In just the next couple of years,the health AI market will grow more than 10 times.

AI generally, and ML specifically, gives us technology that can finally perform specialized nonroutine tasks as it learns for itself without explicit human programing shifting nonclinical judgment tasks away from healthcare enterprise workers.

What will be key to the success of healthcare organizations leveraging AI and ML across every process, piece of data and worker? When AI and ML are effectively added to the operational picture, we will see healthcare systems where machines will take on simple, repetitive tasks so that humans can collaborate on a larger scale and work at a higher cognitive level. AI and ML can foster a powerful combination of strategy, technology and the future of work that will improve both labor productivity and patient care.

Brian Kalis is a managing director of digital health and innovation for Accenture's health business.

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Now More Than Ever We Should Take Advantage of the Transformational Benefits of AI and ML in Healthcare - Managed Healthcare Executive

By Tirthankar Dutta

On October 23rd, 2019, Google claimed that they had achieved Quantum supremacy by solving a particularly difficult problem in 200 seconds by using their quantum computer, which is also known as "sycamore." This performance was compared with a Supercomputer known as 'Summit" and built by IBM. According to Google, this classical computer would have taken 10,000 years to solve the same problem.

The advancement of large quantum computers, along with the more computational power it will bring, could have dire consequences for cybersecurity. It is well known that important problems such as factoring, whose considered hardness ensures the security of many widely used protocols (RSA, DSA, ECDSA), can be solved efficiently, if a quantum computer that is sufficiently large, "fault-tolerant" and universal, is developed. However, addressing the imminent risk that adversaries equipped with quantum technologies pose is not the only issue in cybersecurity where quantum technologies are bound to play a role.

Because quantum computing speeds up prime number factorization, computers enabled with that technology can easily break cryptographic keys by quickly calculating or exhaustively searching secret keys. A task considered computationally infeasible by a conventional computer becomes painfully easy, compromising existing cryptographic algorithms used across the board. In the future, even robust cryptographic algorithms will be substantially weakened by quantum computing, while others will no longer be secure at all:

There would be many disconnects on the necessity to change the current cryptographic protocols and infrastructure to counter quantum technologies in a negative way, but we can't deny the fact that future adversaries might use this kind of technology to their benefit. As it allows them to work on millions of computations in parallel, exponentially speeding up the time it takes to process a task.

According to the National, Academies Study notes, "the current quantum computers have very little processing power and are too error-prone to crack today's strong codes. The future code-breaking quantum computers would need 100,000 times more processing power and an error rate 100 times better than today's best quantum computers have achieved. The study does not predict how long these advances might takebut it did not expect them to happen within a decade."

But does this mean that we should wait and watch the evolution of quantum computing, or should we go back to our drawing board to create quantum-resistant cryptography? Thankfully, researchers have been working on a public-key cryptography algorithm that can counter code-breaking efforts by quantum computers. US National Institute of Standards and Technology (NIST) evaluating 69 potential new methods for what it calls "post-quantum cryptography." The institution expects to have a draft standard by 2024, which would then be added to web browsers and other internet applications and systems

No matter when dominant quantum computing arrives, it poses a large security threat. Because the process of adopting new standards can take years, it is wise to begin planning for quantum-resistant cryptography now.

The author is SVP and Head of Information Security at Infoedge.

DISCLAIMER: The views expressed are solely of the author and ETCIO.com does not necessarily subscribe to it. ETCIO.com shall not be responsible for any damage caused to any person/organisation directly or indirectly.

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Cybersecurity in the quantum era - ETCIO.com

Given the enormous potential for quantum computing to change the way we forecast, model and understand the world, many are beginning to question whether it could have helped to better prepare us all for a global pandemic such as the Covid-19 crisis. Governments, organisations and the public are continuing the quest for answers about when this crisis will end and how we can find a way out of the current state of lockdown, and we are all continuing to learn through incremental and experimental steps. It certainly seems plausible that the high compute simulation capabilities of our most revolutionary technology could hold some of the answers and enable us to respond in a more coherent and impactful way.

Big investments have already been made in quantum computing, as countries and companies battle to create the first quantum supercomputer, so they can harness the power of this awesome technology. The World Economic Forum has also recognised the important role that this technology will play in our future, and has a dedicated Global Future Council to drive collaboration between public and private sector organisations engaged in the development of Quantum Computing. Although its unlikely to result in any overnight miracles, its understandable that many are thinking about whether these huge efforts and investments can be turned towards the mutual challenge we face in finding a solution to the Covid-19 pandemic.

There are already some ground-breaking use-cases for quantum computing within the healthcare industry. Where in the past some scientific breakthroughs such as the discovery of penicillin came completely by accident, quantum computing puts scientists in a much stronger position to find what they were looking for, faster. Quantum raises capacity to such a high degree that it would be possible to model penicillin using just a third of the processing power a classical computer would require to do the job meaning it can do more with less, at greater speed.

In the battle against Covid-19, the US Department of Energys Oak Ridge National Laboratory (ORNL) is already using quantum supercomputers in its search for drug compounds that can treat the disease. IBM has also been using quantum supercomputers to run simulations on thousands of compounds to try and identify which of them is most likely to attach to the spike that Covid-19 uses to inject genetic material into healthy cells, and thereby prevent it. It has already emerged with 77 promising drugs that are worth further investigation and development progress that would have taken years if traditional computing power had been used.

Other businesses are likely to be keen to follow in the footsteps of these examples, and play their own part in dealing with the crisis, but to date its only been the worlds largest organisations that have been using quantum power. At present, many businesses simply dont have the skills and resources needed to fabricate, verify, architect and launch a large-scale quantum computer on their own.

It will be easier to overcome these barriers, and enable more organisations to start getting to work with quantum computing, if they open themselves up to collaboration with partners, rather than trying to go it alone. Instead of locking away their secrets, businesses must be willing to work within an open ecosystem; finding mutually beneficial partnerships will make it much more realistic to drive things forward.

The tech giants have made a lot of early progress with quantum, and partnering with them could prove extremely valuable. Google, for example, claims to have developed a machine that can solve a problem in 200 seconds that would take the worlds fastest supercomputer 10,000 years imagine adding that kind of firepower to your computing arsenal. Google, IBM and Microsoft have already got the ball rolling by creating their own quantum partner networks. IBM Q and Microsoft Quantum Network bring together start-ups, universities, research labs, and Fortune 500 companies, enabling them to enjoy the benefits of exploring and learning together. The Google AI quantum initiative brings together strong academia support along with start-up collaboration on open source frameworks and tools in their lab. Collaborating in this manner, businesses can potentially play their own part in solving the Covid-19 crisis, or preventing future pandemics from doing as much damage.

Those that are leading the way in quantum computing are taking a collaborative approach, acknowledging that no one organisation holds all the answers or all the best ideas. This approach will prove particularly beneficial as we search for a solution to the Covid-19 crisis: its in everyones interests to find an exit to the global shutdown and build knowledge that means we are better-prepared for future outbreaks.

Looking at the bigger picture, despite all the progress that is being made with quantum, traditional computing will still have an important role to play in the short to medium term. Strategically, it makes sense to have quantum as the exploratory left side of the brain, while traditional systems remain in place for key business-as-usual functions. If they can think about quantum-related work in this manner, businesses should begin to feel more comfortable making discoveries and breakthroughs together. This will allow them to speed up the time to market so that ideas can be explored, and new ground broken, much faster than ever before and thats exactly what the world needs right now.

Kalyan Kumar, CVP & CTO, IT Services, HCL Technologies

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Solving problems by working together: Could quantum computing hold the key to Covid-19? - ITProPortal

The decision to deploy nuclear-powered ballistic missile submarines (SSBNs) in the years to come will be a product of the major paradigms and concepts used to manage nuclear dangers more broadly. Recently, an emerging literature has pointed to a change in the way that at least the major powers plan to mitigate nuclear threats to their interests. This shift in thinking can be summarised as involving a greater reliance on strategic non-nuclear weaponsweapons and enabling systems that can be used to compromise an adversarys nuclear forces using both kinetic and non-kinetic means that dont involve nuclear weaponsand a decreased commitment to mutual vulnerability as the basis of strategic stability between nuclear-armed adversaries.

Strategic non-nuclear weapons include ballistic missile defence, conventional precision-strike missiles, anti-satellite weapons and anti-submarine weapons. When combined with advances in enabling platforms and systems such as elements of cyber, artificial intelligence and quantum technology, they can, in principle, be used to compromise an adversarys nuclear capabilities, with serious implications for issues of deterrence and stability.

Traditional approaches to deterrence based on the threat of punishment now compete with policies based instead on deterrence by denial. Stability based on rational calculations under conditions of mutual vulnerability appears set to be even harder to maintain.

The potential for conventional counterforce strikes makes future scenarios involving use them or lose them logic more likely for states that face adversaries armed with more sophisticated capabilities.

The current challenge to traditional nuclear deterrence relationships has a dual but paradoxical effect on the incentives to deploy sea-based nuclear weapons. In general, as missile silos (and even, over time, mobile land-based missiles), air fields, satellites, and command, control and communications stations become more vulnerable to counterforce attacks, the incentives to diversify a states nuclear force structure increase. In particular, SSBNs still remain the most secure form of second-strike capability, meaning that the further spread of strategic non-nuclear weapons is likely to result in ever more nuclear weapons being deployed at sea.

On the other hand, one of the key technologies that falls under the banner of strategic non-nuclear weapons is anti-submarine weapons themselves, and much analysis now is focusing on whether advances in this area may in fact undermine the perceived invulnerability of SSBNs. Its important to note that growing concerns over the effects of new anti-submarine capabilities on strategic stability are, at least in part, based on projections about the future. Little serious analysis or commentary predicts that the oceans are going to become effectively transparent overnight. However, advances in sensing and signal processing in particular mean that its a serious possibility that the oceans will become significantly more transparent than they are today. And when it comes to nuclear force structure planning, serious possibilities are enough to keep decision-makers up at night.

As the development of strategic non-nuclear weapons and the associated shift in thinking about stable deterrence based on mutual vulnerability continues, policymakers and analysts will need to give serious attention to what might become the new determinants of stability in the global nuclear order.

The development of countermeasures will play an important role in mitigating the destabilising effects of disruptive technological breakthroughs in anti-submarine weaponry. The role of countermeasures is already evident in other domains. For example, as a reaction to US missile defence, both China and Russia today are placing increasing emphasis on hypersonic missiles because their combination of speed and manoeuvrability makes them extraordinarily difficult to defend against.

Countermeasures for anti-submarine weapons need not rely on kinetic effects. The development both of ever quieter SSBNs with smaller acoustic signatures and of new techniques of deception (for example, unmanned underwater vehicles designed to produce tonals that match those of SSBNs that are thought to have been identified by an adversary) can increase a states confidence that at least some of its SSBNs can remain undetected and uncompromised in a crisis.

Developments in anti-submarine weapons aimed at compromising SSBNs and developments in countermeasures aimed at mitigating those breakthroughs will take on a tit-for-tat dynamic in the years to come. This is not a new phenomenon, but as rapid increases in things such as sensing techniques and data processing allow for technological leaps in anti-submarine capabilities, countermeasures should be expected to take on a new and much greater importance.

Defensive measures for SSBNs aimed at increasing their reliability in the face of technological breakthroughs in anti-submarine weaponry are unlikely to solely rely on new technologies themselves. For example, James Holmes has suggested that both bastion strategies for SSBNs (vessels constricted to a much smaller, actively defended area for patrols) and SSBNs being accompanied by convoys of skirmisher-type defensive units (adopting a similar principle to aircraft carrier battle groups) may be necessary to regain confidence in the survivability of SSBNs.

Stability needs to be seen as the most important goal and that will require a degree of what has been termed security dilemma sensibility among the nuclear-armed powers. Leaders that develop security dilemma sensibility display an openness to the idea that, as Nicholas Wheeler has put it, an adversary is acting out of fear and insecurity and not aggressive intent, as well as a recognition that ones own actions have contributed to that fear.

For example, future Chinese breakthroughs on quantum computing and their application to SSBN communication technology could be a positive development in the USChina strategic relationship. The more confidence Beijing has in the security of its second-strike capability, the less likely it is that a crisis between the US and China will inadvertently escalate.

Beyond unilateral measures, it may be possible, over the longer term, to negotiate, and design, limited multilateral efforts aimed at restoring stability between adversaries, including in relation to sea-based nuclear deployments. History suggests that confidence-building measures can play as important a role as formal arms control measures in reducing nuclear dangers, meaning that finding avenues for dialogue, even at a low level, should now be a top priority.

In the short term, the increasing salience of strategic non-nuclear weapons and the abandonment of deterrence strategies based on mutual vulnerability, is likely to continue to encourage states to deploy more SSBNs. Simultaneously, these forces will intensify the pressures to better protect SSBN fleets that are already deployed from technological breakthroughs in the anti-submarine weapons domain. Restraint in the deployment of anti-submarine capabilities may need to become a substitute for the more traditional tools used to instil stability in nuclear-armed relationshipsrestraint in defensive technology (such as missile defence) and negotiated limits on arms.

This piece was produced as part of the Indo-Pacific Strategy: Undersea Deterrence Project, undertaken by the ANU National Security College. This article is a shortened version of chapter 20, Strategic non-nuclear weapons, SSBNs, and the new search for strategic stability, as published in the 2020 edited volume The future of the undersea deterrent: a global survey. Support for this project was provided by a grant from Carnegie Corporation of New York.

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Nuclear submarines, non-nuclear weapons and the search for strategic stability - The Strategist

Menten AI has an impressive founding team and a pitch that combines some of the hottest trends in tech to pursue one of the biggest problems in healthcare new drug discovery. The company is also $4 million richer with a seed investment from firms including Uncork Capital and Khosla Ventures to build out its business.

Menten AIs pitch to investors was the combination of quantum computing and machine learning to discover new drugs that sit between small molecules and large biologics, according to the companys co-founder Hans Melo.

A graduate of the Y Combinator accelerator, which also participated in the round alongside Social Impact Capital*, Menten AI looks to design proteins from scratch. Its a heavier lift than some might expect, because, as Melo said in an interview, it takes a lot of work to make an actual drug.

Menten AI is working with peptides, which are strings of amino acid chains similar to proteins that have the potential to slow aging, reduce inflammation and get rid of pathogens in the body.

As a drug modality [peptides] are quite new, says Melo. Until recently it was really hard to design them computationally and people tried to focus on genetically modifying them.

Peptides have the benefit of getting through membranes and into cells where they can combine with targets that are too large for small molecules, according to Melo.

Most drug targets are not addressable with either small molecules or biologics, according to Melo, which means theres a huge untapped potential market for peptide therapies.

Menten AI is already working on a COVID-19 therapeutic, although the companys young chief executive declined to disclose too many details about it. Another area of interest is in neurological disorders, where the founding team members have some expertise.

Image of peptide molecules. Image Courtesy: D-Wave

While Menten AIs targets are interesting, the approach that the company is taking, using quantum computing to potentially drive down the cost and accelerate the time to market, is equally compelling for investors.

Its also unproven. Right now, there isnt a quantum advantage to using the novel computing technology versus traditional computing. Something that Melo freely admits.

Were not claiming a quantum advantage, but were not claiming a quantum disadvantage, is the way the young entrepreneur puts it. We have come up with a different way of solving the problem that may scale better. We havent proven an advantage.

Still, the company is an early indicator of the kinds of services quantum computing could offer, and its with that in mind that Menten AI partnered with some of the leading independent quantum computing companies, D-Wave and Rigetti Computing, to work on applications of their technology.

The emphasis on quantum computing also differentiates it from larger publicly traded competitors like Schrdinger and Codexis.

So does the pedigree of its founding team, according to Uncork Capital investor, Jeff Clavier. Its really the unique team that they formed, Clavier said of his decision to invest in the early-stage company. Theres Hans the CEO who is more on the quantum side; theres Tamas [Gorbe] on the bio side and theres Vikram [Mulligan] who developed the research. Its kind of a unique fantastic team that came together to work on the opportunity.

Clavier has also acknowledged the possibility that it might not work.

Can they really produce anything interesting at the end? he asked. Its still an early-stage company and we may fall flat on our face or they may come up with really new ways to make new peptides.

Its probably not a bad idea to take a bet on Melo, who worked with Mulligan, a researcher from the Flatiron Institute focused on computational biology, to produce some of the early research into the creation of new peptides using D-Waves quantum computing.

Novel peptide structures created using D-Waves quantum computers. Image Courtesy: D-Wave

While Melo and Mulligan were the initial researchers working on the technology that would become Menten AI, Gorbe was added to the founding team to get the company some exposure into the world of chemistry and enzymatic applications for its new virtual protein manufacturing technology.

The gamble paid off in the form of pilot projects (also undisclosed) that focus on the development of enzymes for agricultural applications and pharmaceuticals.

At the end of the day what theyre doing is theyre using advanced computing to figure out what is the optimal placement of those clinical compounds in a way that is less based on those sensitive tests and more bound on those theories, said Clavier.

*This post was updated to add that Social Impact Capital invested in the round. Khosla, Social Impact, and Uncork each invested $1 million into Menten AI.

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Menten AIs combination of buzzword bingo brings AI and quantum computing to drug discovery - TechCrunch