Monthly Archives: January 2022

Power Movesis a column where we chart the comings and goings of talent across the region. Got a new hire, new gig or promotion? Email us at dc@technical.ly.

This week, Leesburg, Virginia company Quantum Computing added former board member William McGann as its new COO and CTO. According to Quantum Computing, hell lead the team as it continues to advance and innovate its quantum optimization software.

As a leader, I look forward to establishing an exciting, fast-paced business rhythm that is customer-focused and leading through innovation, McGann said in a statement. We will aspire to be the best in all that we do and endeavor to become the preeminent leader in delivering quantum solutions to the market that advance customer business and application outcomes.

William McGann. (Photo via LinkedIn)

The company also added Michael Turmelle, a former chairman of Ideal Power, to its board of directors.

And following its uber-successful IPO in 2021, College Park, Maryland quantum firm IonQ has pulled some power players into its fold.

On the leadership side, IonQ has added two new members. Ariel Braunstein, the cofounder of Pure Digital Technology and a former product lead at Cisco and Googles AV/VR department will join as senior VP of product management. Dean Kassman, former senior director at Blue Origin, was also named as VP of research and development.

Additionally, the company added Inder Singh, executive VP and CEO at Arm, a British semiconductor firm, to its board.

It is an incredibly exciting time to join IonQs Board, having watched the company grow from a startup to the quantum computing industry leader it is today, said Singh in a statement. Government, business and society as a whole are quickly recognizing quantums ability to solve previously impossible problems, and they are turning to IonQ for counsel and strategic partnership. I could not be more excited to be joining the Board and helping the team potentially shape the next frontiers of technology.

Inder Singh. (Photo via LinkedIn)

In the past month, Fairfax, Virginia software developer 3Pillar has made a ton of hiring moves, including adding three members to its C-suite.

In mid-December, the company added Ken Krtiz as senior VP of banking and insurance, Chris Hansen as senior VP of communications and Kim Mirazimi as senior VP of commercial sector clients.

At the top, 3Pillar promoted its chief delivery officer David Sawatzky to the role of COO. Jamie Whitacre, previously the senior VP of talent, was also bumped up to the role of chief people officer. And just this week, 3Pillar appointed Jeff Sperber as its new CFO. Sperber was previously the CFO for companies including Class Valuation, TalentReef, NetDocuments and Accenture Mortgage Cadence.

Im thrilled to continue my journey at 3Pillar as we expand our global presence and scale our culture, said Whitacre in a statement. Our next phase of growth is critical in helping us attain our vision of becoming the employer of choice in digital product development services.

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Power Moves: Meet the newest faces on the DMV's quantum computing scene - Technical.ly

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Expect smarter apps, quantum leaps, deeper space exploration, nanovaccines and more in 2022 - Cyprus Mail

The team of academician GUO Guangcan of University of Science and Technology of China (USTC) of the Chinese Academy of Sciences has made important progress in the research of cold atom super-resolution imaging. The team achieved super-resolution imaging of a single ion in an ion trap system. The results were published in Physical Review Letters.

The cold atom system is an ideal experimental platform for studying quantum physics, as well as an important physical system for experimental research on quantum simulation, quantum computing, and quantum precision measurement. One of the core experimental techniques in the cold atom system is high-resolution single-particle imaging. In the past ten years, the microscopic imaging technology of the cold atom system has developed rapidly. However, the newly developed technologies are still limited by the fundamental optical diffraction limit, and the resolution can only reach the order of optical wavelength. It is difficult to study quantum phenomena related to the details of the wave function. To study such problems requires optical super-resolution imaging.

Optical super-resolution imaging has developed into a mature tool in the fields of chemistry and biology. However, due to the complexity of cold atom experiments, it is extremely challenging to apply super-resolution imaging technology to cold atom systems. Prior to this, the world has not yet made progress on the direct super-resolution imaging of single atoms (ions).

In this study, the researchers adopted the main idea of the Stimulated Emission Depletion (STED) microscopy in the classical super-resolution imaging field, and combined it with the atomic quantum state initialization and reading technology of the cold atom system. They realized super-resolved imaging of a single cold atom (ion) directly for the first time.

Experimental results showed that the spatial resolution of the imaging method can exceed the diffraction limit by more than one order, and the imaging resolution of 175 nm can be achieved by using an objective lens with a numerical aperture of only 0.1.

To further demonstrate the time resolution advantage of this method, the researchers achieved both a time resolution of 50 ns and a single ion positioning accuracy of 10 nm, and used this method to clearly capture the rapid harmonic oscillations of the ion in the trap. Theoretically, by increasing the numerical aperture of the imaging objective and the center extinction ratio of the depleted light (the doughnut spot), the spatial resolution can be further improved to below 10 nm.

This experimental technique can be extended to the multi-body and correlation measurement of cold atom systems, and has good compatibility with other cold atom systems. It can be applied to optical lattices, neutral atom optical tweezers, and cold atom-ion hybrid systems.

Reference: Super-resolved Imaging of a Single Cold Atom on a Nanosecond Timescale by Zhong-Hua Qian, Jin-Ming Cui, Xi-Wang Luo, Yong-Xiang Zheng, Yun-Feng Huang, Ming-Zhong Ai, Ran He, Chuan-Feng Li and Guang-Can Guo, 23 December 2021, Physical Review Letters.DOI: 10.1103/PhysRevLett.127.263603

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Super-Resolution Imaging of a Single Cold Atom on a Nanosecond Timescale - SciTechDaily

Dario Gil, Director of IBM Research, standing in front of IBM Q System One on October 18, 2019 at ... [+] the company's research facility in Yorktown Heights, N.Y.

In the past year the share price of Apple AAPL has outperformed Bitcoin, and Tesla's TSLA share price which has hitherto been umbilically linked to bitcoin, has been far more jumpy (over the last two years it has had 29 one day moves of more than 10% as compared to a mere 17 for bitcoin).

These insights beg lots of questions, about market functioning, investor appetiteand simply, whether bitcoin is now old and dull. There is a debate that instead of being a risky asset across the spectrum of broad asset classes (bonds, equities etc), bitcoin is simply a safe asset within a highly volatile crypto world, though the fact that bitcoin has fallen 15% since I started writing this note nods to the former.

More importantly, it might be that bitcoin isout of fashion and is being replaced in the mindsets of investors by other speedy innovations. Recall the memorableline from a Davos speech by Canadian Premier Justin Trudeau where he said 'The pace of change has never been this fast, yet it will never be this slow again.

Exponential

The spirit of this phrase is caught by the many year ahead forecasts from futurologists, economists and thinkers. Having made my own forecasts before Christmas I have the benefit of sitting back and reading others, one excellent example being Azeem Azars weekly Exponential email, and a growing number of other notes that try to summarise what is bubbling up.

What is noticeable is that there is a strong sense of the Roaring 20s in threads of structural trends that analysts see taking place in the aftermath of a pandemic. Chief amongst them is a focus on nuclear power as a substitute to fossil fuels, not to mention the entire greentech complex.

If the forecasts and thought pieces I have mentioned are a good representation of where capital will flow, then a new, exciting infrastructure is being built in computing, logistics and finance to name a few sectors that happen to be united by data intensity. I might say that having witnessed the dot.com bubble, the only value of an asset bubble is that it leaves behind it an important infrastructure (telecoms in this case).

MetaVerse

My three key takeaways from parsing many reports, is that whilst on one hand exciting, the central message of the new emerging technologies is that they will lead to a historic and potentially overwhelming challenge to humanity, and in particular to our minds and sociability, with potentially very impactful health related benefits.

Without exaggerating, I feel that 2022 is a threshold year when innovative technologies make our bodies healthier but invade our mental spaces.

To first take a collection of the most popular trends, they cluster around web 3.0, NFTs, metaverse, social media and crypto currencies. In a brutal way that means that we will all spend more time out of the light, hunched over phones and doubting whether the people and things we have encountered in the metaverse are real at all, and whether it was worth investing USD 10,000 in a metaverse apartment.

This trend is historic because for the first time, and with only the force of religion (and maybe politics) as a rival, humans will spend time and enjoy experiences in an unreal world, and for some this will come to dominate their existence. Two very obvious side-effects will be sociability and mental health.

Quantum

In previous bulletins I have remarked that mental health needs to become a core pillar of how health services are reimagined, and it may be that the metaverse is the trigger (ironically it is used to help soldiers overcome post traumatic stress disorder).

Also on the positive side, the enormous advances in medicine and health tech much of it spurred by the coronavirus crisis, will have a positive effect on humans pending at least two factors, that the bounty of these advances can be as widely spread as possible, and that the ways in which they are delivered is rethought in the sense that healthcare systems need to change.

One inspiration here, and my second point, is that the rising attention that technologies like blockchain have cast on decentralized autonomous organisations effectively organisations run by a coded relationships between disparate parties, as opposed to a centralized or even hierarchical bureaucracy (i.e. healthcare systems). While it is a stretch to imagine that todays healthcare systems and other institutional arrangements can be quickly replaced by decentralized forms, there is much that blockchain can do to cut that bureaucracies (closer relationships between doctors, patients, billing and other parties like pharmacists).

DAO

A third related, threshold change in technological influence that is getting a lot of attention and capital, quantum computing (70% of startup level investment in tech hardware goes to quantum computing projects). Governments, notably China and the European Union are also spending heavily on it. In brief quantum computing is revolutionary in that it uses different arrangements of bits to produce more powerful processing. Though there are not yet many applications of quantum computing it has the capacity to dramatically change aspects of healthcare, finance and industrial sectors like chemicals.

Having already witnessed historic, positive changes that resulted from the pandemic the general patience and obeisance of the worlds population, the acceptance of working from home and the power of vaccines, we are now crossing a threshold in terms of how technology will change our bodies, as well as our minds and the ways we relate to the world.

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Will Innovation Make Us Better Off? - Forbes

By Shannon Brescher Shea, U.S. Department of EnergyJanuary 3, 2022

A computer is suspended from the ceiling. Delicate lines and loops of silvery wires and tubes connect gold-colored platforms. It seems to belong in a science-fiction movie, perhaps a steam-punk cousin of HAL in 2001: A Space Odyssey. But as the makers of that 1968 movie imagined computers the size of a spaceship, this technology would have never crossed their minds a quantum computer.

Quantum computers have the potential to solve problems that conventional computers cant. Conventional computer chips can only process so much information at one time and were coming very close to reaching their physical limits. In contrast, the unique properties of materials for quantum computing have the potential to process more information much faster.

These advances could revolutionize certain areas of scientific research. Identifying materials with specific characteristics, understanding photosynthesis, and discovering new medicines all require massive amounts of calculations. In theory, quantum computing could solve these problems faster and more efficiently. Quantum computing could also open up possibilities we never even considered. Its like a microwave oven versus a conventional oven different technologies with different purposes.

But were not there yet. So far, one company has claimed its quantum computer can complete a specific calculation faster than the worlds fastest conventional supercomputers. Scientists routinely using quantum computers to answer scientific questions is a long way off.

To use quantum computers on a large scale, we need to improve the technology at their heart qubits. Qubits are the quantum version of conventional computers most basic form of information, bits. The DOEs Office of Science is supporting research into developing the ingredients and recipes to build these challenging qubits.

DOEs Lawrence Berkeley National Laboratory is using a sophisticated cooling system to keep qubits the heart of quantum computers cold enough for scientists to study them for use in quantum computers. Credit: Image courtesy of Lawrence Berkeley National Laboratory

At the atomic scale, physics gets very weird. Electrons, atoms, and other quantum particles interact with each other differently than ordinary objects. In certain materials, we can harness these strange behaviors. Several of these properties particularly superposition and entanglement can be extremely useful in computing technology.

The principle of superposition is the idea that a qubit can be in multiple states at once. With traditional bits, you only have two options: 1 or 0. These binary numbers describe all of the information in any computer. Qubits are more complicated.

Imagine a pot with water in it. When you have water in a pot with a top on it, you dont know if its boiling or not. Real water is either boiling or not; looking at it doesnt change its state. But if the pot was in the quantum realm, the water (representing a quantum particle) could both be boiling and not boiling at the same time or any linear superposition of these two states. If you took the lid off of that quantum pot, the water would immediately be one state or the other. The measurement forces the quantum particle (or water) into a specific observable state.

Entanglement is when qubits have a relationship to each other that prevents them from acting independently. It happens when a quantum particle has a state (such as spin or electric charge) thats linked to another quantum particles state. This relationship persists even when the particles are physically far apart, even far beyond atomic distances.

These properties allow quantum computers to process more information than conventional bits that can only be in a single state and only act independently from each other.

But to get any of these great properties, you need to have fine control over a materials electrons or other quantum particles. In some ways, this isnt so different from conventional computers. Whether electrons move or not through a conventional transistor determines the bits value, making it either 1 or 0.

Rather than simply switching electron flow on or off, qubits require control over tricky things like electron spin. To create a qubit, scientists have to find a spot in a material where they can access and control these quantum properties. Once they access them, they can then use light or magnetic fields to create superposition, entanglement, and other properties.

In many materials, scientists do this by manipulating the spin of individual electrons. Electron spin is similar to the spin of a top; it has a direction, angle, and momentum. Each electrons spin is either up or down. But as a quantum mechanical property, spin can also exist in a combination of up and down. To influence electron spin, scientists apply microwaves (similar to the ones in your microwave oven) and magnets. The magnets and microwaves together allow scientists to control the qubit.

Since the 1990s, scientists have been able to gain better and better control over electron spin. Thats allowed them to access quantum states and manipulate quantum information more than ever before.

To see where thats gone today, its remarkable, said David Awschalom, a quantum physicist at DOEs Argonne National Laboratory and the University of Chicago as well as Director of the Chicago Quantum Exchange.

Whether they use electron spin or another approach, all qubits face major challenges before we can scale them up. Two of the biggest ones are coherence time and error correction.

When you run a computer, you need to be able to create and store a piece of information, leave it alone, and then come back later to retrieve it. However, if the system that holds the information changes on its own, its useless for computing. Unfortunately, qubits are sensitive to the environment around them and dont maintain their state for very long.

Right now, quantum systems are subject to a lot of noise, things that cause them to have a low coherence time (the time they can maintain their condition) or produce errors. Making sure that you get the right answer all of the time is one of the biggest hurdles in quantum computing, said Danna Freedman, an associate professor in chemistry at Northwestern University.

Even if you can reduce that noise, there will still be errors. We will have to build technology that is able to do error correction before we are able to make a big difference with quantum computing, said Giulia Galli, a quantum chemist and physicist at DOEs Argonne National Laboratory and the University of Chicago.

The more qubits you have in play, the more these problems multiply. While todays most powerful quantum computers have about 50 qubits, its likely that they will need hundreds or thousands to solve the problems that we want them to.

The jury is still out on which qubit technology will be the best. No real winner has been identified, said Galli. [Different ones] may have their place for different applications. In addition to computing, different quantum materials may be useful for quantum sensing or networked quantum communications.

To help move qubits forward, DOEs Office of Science is supporting research on a number of different technologies. To realize quantum computings enormous scientific potential, we need to reimagine quantum R&D by simultaneously exploring a range of possible solutions, said Irfan Siddiqi, a quantum physicist at the DOE Lawrence Berkeley National Laboratory and the University of California, Berkeley.

Superconducting Qubits

Superconducting qubits are currently the most advanced qubit technology. Most existing quantum computers use superconducting qubits, including the one that beat the worlds fastest supercomputer. They use metal-insulator-metal sandwiches called Josephson junctions. To turn these materials into superconductors materials that electricity can run through with no loss scientists lower them to extremely cold temperatures. Among other things, pairs of electrons coherently move through the material as if theyre single particles. This movement makes the quantum states more long-lived than in conventional materials.

To scale up superconducting qubits, Siddiqi and his colleagues are studying how to build them even better with support from the Office of Science. His team has examined how to make improvements to a Josephson junction, a thin insulating barrier between two superconductors in the qubit. By affecting how electrons flow, this barrier makes it possible to control electrons energy levels. Making this junction as consistent and small as possible can increase the qubits coherence time. In one paper on these junctions, Siddiqis team provides a recipe to build an eight-qubit quantum processor, complete with experimental ingredients and step-by-step instructions.

Qubits Using Defects

Defects are spaces where atoms are missing or misplaced in a materials structure. These spaces change how electrons move in the materials. In certain quantum materials, these spaces trap electrons, allowing researchers to access and control their spins. Unlike superconductors, these qubits dont always need to be at ultra-low temperatures. They have the potential to have long coherence times and be manufactured at scale.

While diamonds are usually valued for their lack of imperfections, their defects are actually quite useful for qubits. Adding a nitrogen atom to a place where there would normally be a carbon atom in diamonds creates whats called a nitrogen-vacancy center. Researchers using the Center for Functional Nanomaterials, a DOE Office of Science user facility, found a way to create a stencil just two nanometers long to create these defect patterns. This spacing helped increase these qubits coherence time and made it easier to entangle them.

But useful defects arent limited to diamonds. Diamonds are expensive, small, and hard to control. Aluminum nitride and silicon carbide are cheaper, easier to use, and already common in everyday electronics. Galli and her team used theory to predict how to physically strain aluminum nitride in just the right way to create electron states for qubits. As nitrogen vacancies occur naturally in aluminum nitride, scientists should be able to control electron spin in it just as they do in diamonds. Another option, silicon carbide, is already used in LED lights, high-powered electronics, and electronic displays. Awschaloms team found that certain defects in silicon carbide have coherence times comparable to or longer than those in nitrogen-vacancy centers in diamonds. In complementary work, Gallis group developed theoretical models explaining the longer coherence times.

Based on theoretical work, we began to examine these materials at the atomic scale. We found that the quantum states were always there, but no one had looked for them, said Awschalom. Their presence and robust behavior in these materials were unexpected. We imagined that their quantum properties would be short-lived due to interactions with nearby nuclear spins. Since then, his team has embedded these qubits in commercial electronic wafers and found that they do surprisingly well. This can allow them to connect the qubits with electronics.

Materials by Design

While some scientists are investigating how to use existing materials, others are taking a different tack designing materials from scratch. This approach builds custom materials molecule by molecule. By customizing metals, the molecules or ions bound to metals, and the surrounding environment, scientists can potentially control quantum states at the level of a single particle.

When youre talking about both understanding and optimizing the properties of a qubit, knowing that every atom in a quantum system is exactly where you want it is very important, said Freedman.

With this approach, scientists can limit the amount of nuclear spin (the spin of the nucleus of an atom) in the qubits environment. A lot of atoms that contain nuclear spin cause magnetic noise that makes it hard to maintain and control electron spin. That reduces the qubits coherence time. Freedman and her team developed an environment that had very little nuclear spin. By testing different combinations of solvents, temperatures, and ions/molecules attached to the metal, they achieved a 1 millisecond coherence time in a molecule that contains the metal vanadium. That was a much longer coherence time than anyone had achieved in a molecule before. While previous molecular qubits had coherence times that were five times shorter than diamond nitrogen-vacancy centers times, this matched coherence times in diamonds.

That was genuinely shocking to me because I thought molecules would necessarily be the underdogs in this game, said Freedman. [It] opens up a gigantic space for us to play in.

The surprises in quantum just keep coming. Awschalom compared our present-day situation to the 1950s when scientists were exploring the potential of transistors. At the time, transistors were less than half an inch long. Now laptops have billions of them. Quantum computing stands in a similar place.

The overall idea that we could completely transform the way that computation is done and the way nature is studied by doing quantum simulation is really very exciting, said Galli. Our fundamental way of looking at materials, based on quantum simulations, can finally be useful to develop technologically relevant devices and materials.

Excerpt from:

Creating the Heart of a Quantum Computer: Developing Qubits - SciTechDaily

The global use and further development of AI continued to grow in 2021 as enterprises found more ways to deploy it and developers discovered new ways to capture its possibilities for business users.

So, what might 2022 bring for AI and a wide range of related IT fields from MLOps to security, cloud and edge computing, open source, the metaverse and more?

To answer that question, we received a wide range of predictions from IT industry experts who shared their thoughts with EnterpriseAI. We are publishing them here, edited for clarity and brevity, to give our readers an early look at what may come in 2022 in enterprise AI and related technologies.

Rodrigo Liang, the CEO and co-founder of AI platform vendor SambaNova Systems, said he sees companies moving away from DIY AI and linking up with vendors who can help them better reach their business goals.

Organizations recognize the talent shortage and internal inertia to technology advancements adds significant performance risk, said Liang. As a result, traction for AI-as-a-Service expands allowing for investment in applying insights over creating those insights. How many companies are going to be able to afford to hire hundreds of people just to manage one model and have thousands of GPUs interconnected to run one thing?

Rodrigo Liang, of SambaNova

In addition, business verticals including banking, financial services, insurance and manufacturing will further deploy AI in transformative ways to bolster their performance and operations and will never look back, said Liang. Just like how the internet changed every facet of commercial transactions, AI will have the same level if not more of an impact. These industries will move from test deployments to production and will earn the returns promised with AI.

Mark Brayan, the CEO of machine learning data training vendor Appen, said that in 2022 he sees the idea of responsible AI shifting from an aspiration to a foundational requirement for most AI projects.

In 2021, responsible AI was one of the hottest topics in the AI industry, but adoption remained relatively low, he said. In 2022, however, the stakes become much higher, as businesses recognize that responsible AI leads to better business outcomes. The principles of responsible AI are now well-established: unbiased data, fair treatment on the data collection and labeling side of the industry, and a recognition that AI projects should promote the social good, or at least avoid the potential for social harm. Implementing these principles ensures that AI projects work as expected and protects the brand.

In addition, said Wilson Pang, Appens CTO, the new year will also see model evaluation and tuning going mainstream for enterprises.

Wilson Pang of Appen

In 2022, the need for regular model evaluation and tuning becomes AI program table stakes, said Pang. Machine learning models are dynamic they cannot be deployed and forgotten. ML models in production need to be updated and retrained based on a variety of factors, including the ongoing results, as well as changes in infrastructure, data sources and business models.

Bill Scudder, the AIoT (artificial intelligence of things) general manager at industrial optimization software vendor AspenTech, said he sees AIs maturation into industrial AI reach full bloom in 2022, graduating to real-world product deployments with concrete time-to-value.

To achieve this, we will see more industrial organizations make a conscious shift from investments in generic AI models to morefit-for-purpose, precise industrial AI applicationsthat help them achieve their profitability and sustainability goals, said Scudder. This means moving away from AI models that are trained on large volumes of [facility] data that cannot cover the full range of potential operations, to more specific industrial AI models that leverage domain expertise for interpreting and predicting with deep analytics and machine learning. Industrial data will be transformed into successful business outcomes across the full asset lifecycle.

Scudder said he also expects to see progress as executive buy-in and cultural change within organizations accelerate industrial AI deployments.

Digital executives like chief digital officers(CDOs) will be crucial to overcoming these obstacles, he said. CDOs will have a unique role to play in shepherding digital transformation and industrial AI through their organization bridging the gap between legacy systems and new technologies, fostering collaboration across silos, and shifting from mass data collection to strategic industrial data management. All these duties will be essential to ensure that an industrial organization can execute a digital transformation plan that sees wider adoption of, and strategy around, fit-for-purpose industrial AI applications.

Omer Har, the co-founder and CTO ofexternal data platform vendor Explorium, said he sees MLOps (machine learning ops) move from the periphery to the center of DevOps, data and ML practices.

The pandemic gave many large organizations the push they needed to embrace AI and analytics, said Har. These predictive models have assumed critical importance in industries ranging from insurance to consumer packaged goods. The key to their predictive power is a constantly refreshed stream of external training data. That means the models must be frequently retrained and seamlessly redeployed to production. For many DevOps professionals, this process has become as mission-critical as traditional SaaS deployments and require the same level of instrumentation and careful tool selection. MLOPs is no longer a sideshow. In many organizations, it is quickly becoming the main show.

Ali Siddiqui of BMC

Ali Siddiqui, the chief product officer for BMC Software, said he sees similar progress for AIOps (AI operations) inside enterprises this year.

AIOps will grow in 2022 as businesses adapt to be successful in delivering the digital experiences customers demand as they move to hybrid cloud environments, said Siddiqui. AIOps will provide organizations with insights into their data to help them identify pain points, reduce noise, provide visibility to issues before they impact the business and meet business objectives while saving time and money. AIOps also eliminates the need to analyze thousands of events and transforms large amounts of data into actionable information which is key for business success and efficiency.

Matt Watts the chief technology evangelist atdata-centric software vendor NetApp, said he expects to see more progress in AI adoption at the edge in the new year.

"The adoption of AI technology at the edge of networks will continue to accelerate across the manufacturing, transportation, agriculture, entertainment and hospitality industries, he said. For example, the agriculture and food processing industries will use AI for harvesting and packaging. An explosion in tiny machine learning chipsets for low-cost and resource-constrained devices such as remote sensors that can collect and process data at the edge will drive this trend. These chipsets will fuel the ever-growing edge-core-cloud data pipeline, which industries will need to access and leverage to differentiate themselves in the marketplace.

In addition, Watts said he expects to see more forward progress in the use of quantum computing in 2022. Quantum computing will accelerate as major IT players and startups use the technology for complex tasks such as drug discovery, financial risk calculation, automotive battery design, and supply chain optimization, he said. Organizations will be more vocal about their quantum computing strategy in 2022, sharing how they will deliver quantum computing as a service to their customers and overcome challenges such as building a data pipeline into the quantum computing cloud.

Peak co-founder and CEO Richard Potter said he sees the nascent field of decision intelligence, which he called the commercial application of AI to decision-making processes, as the most important B2B movement of a generation. We are at the stage of narrow AI, where machine learning and AI can make predictions and categorizations for specific purposes. But to solve the biggest business challenges, AI needs to be focused on an outcome, on delivering against business objectives and driving tangible results. Businesses that make great decisions consistently win. That is why decision intelligence is how most businesses will adopt AI.

Alicia Frame of Neo4j

Alicia Frame, the director of product management for data science at graph database vendor Neo4j, said that in 2022 companies must embrace the role of the citizen data scientist for employees whose primary job functions lie outside the field of data science and analytics. The data science field is one of the fastest growing, and with the workforce currently experiencing The Great Resignation, companies will need to make data science more accessible to help fill gaps on their teams, said Frame. Over the past five years, the inquiry volume of clients striving to learn about digital ethics has more than tripled. Graphs are built for providing context to systems, which allows for increased explainability in AI/ML systems. As more organizations explore the different technologies to reach this point, 2022 will be a turning point for many organizations as they leverage graph technology to enhance their ability to address bias and create more ethical AI/ML systems.

Steven Mih, the co-founder and CEO at Ahana, which provides managed Presto services on AWS to help simplify open data lakes analytics, said he expects to see investments and adoption of managed services for open source to soar in the new year. More companies will adopt managed services for open source in 2022 as more cloud-native open source technologies become mainstream, including Spark, Kafka, Presto, Hudi and Superset, he said. Open source companies offering easier-to-use, managed service versions of installed software enable companies to take advantage of these powerful systems without the resource overhead so they can focus on business-driven innovation.

Manjusha Madabushi, the chief technology officer at Talentica Software, said he sees big changes coming in the world of metaverses in 2022. "With Facebook renaming itself as Meta and starting to build the metaverse, we will see big investments and innovation in virtual world-based online gaming, social networking and virtual reality based products like virtual conference platforms. Expect new applications that enable meetings with characters in the virtual world filling the place of corresponding human entities.

Disruptions will come in 2022 when it comes to cyberattacks on already-pressured companies including chipmakers, said James Carder, the chief security officer and vice president of labs at IT security vendor LogRhythm. A leading country producing semiconductor chips will have its supply-chain compromised, resulting in major shortages of critical materials, said Carder. As we have seen with the pandemic, cybercriminals will take advantage of periods of societal disruption to manipulate companies and governments for financial gain.The global chip shortage, whichshows no sign of slowing down is another period of disruption that hackers will soon exploit.As countries seek to ramp up production, one country will be caught attempting to corner the market by using fraudulent methods to gain access to the production and supply of the leading chip-producing countries.

Carder also predicts that the supply chain of a major vaccinemanufacturerwill be halted by ransomware. In 2021, ransomware attacks crippled Colonial Pipeline and JBS. In 2022, cybercriminals will set their sights on conducting a ransomware attack against one of the pharmaceutical companies producing the COVID-19 vaccine. This will interrupt the production of critical booster shots and keep other lifesaving drugs from reaching patients. The resulting fallout will fan the flame for foreign and domestic vaccine disinformation campaigns.

Murli Thirumale of Pure Storage

Murli Thirumale, the vice president and general manager of the cloud native business unit at flash storage vendor Pure Storage, said he sees 2022 as the year when containers will become a technology staple of mid-market companies and not just used by huge corporations with large IT staffs. Previously, Global 2000 companies were those that could afford to experiment and deploy newer technologies like containers. After all, they have the means to staff talented DevOps teams and invest in multi-year transformation initiatives. However, as containers move past the stages of initial innovation and adoption, and the industry moves into early maturity, mid-market companies will begin deploying and experimenting with this technology even more. Not only will containers work better out of the box, they will be delivered as a service and consumed with ease.

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2022 Technology Predictions for AI in the Enterprise - EnterpriseAI