Westworld creators want to make a show about AI without ‘going straight to Skynet’ – Polygon

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At times, Westworld is a show about the advancements and dangers of artificial intelligence, but the series creators never wanted it to be Skynet.

Jonathan Nolan and Lisa Joy spoke about the message they were trying to get across with the shows first season during a conference hosted by Wired. Nolan said that one of the tropes they wanted to avoid was turning AI into a terrifying enemy just because the rise of technology seemed scary. Nolan said that they never wanted their AI to be Skynet, the main antagonist and artificially intelligent death machines from the Terminator films.

Until now, AI has tended to lean into a dystopian perspective, Nolan said. It goes straight to Skynet, with the exception of Spike Jonze's Her, which is a beautiful movie. What's becoming increasingly clear is that's not how it's going to play out.

Nolan added that our relationship with the different forms of AI we interact with on a daily basis from Siri, Alexa and Goole to the AI that powers driverless cars is constantly changing, and thats where he finds inspiration. Nolan said the issue is that as technology rapidly changes, we expect more from the artificial intelligence in our lives. We come to rely on it without ever fully trusting it.

One of the goals of Westworld was to examine these relationships between machines and humans, co-creator Joy added. After talking to those who work in Silicon Valley and specialize on how artificial intelligence functions and grows it became clear to both creators that Westworld was introducing an important discussion to those who werent focused on it five days a week.

As technology increases exponentially and AI certainly grows were kind of leaping into the unfathomable with machines that can learn and process better than we can, Joy said. The thing that we've heard most is that it's almost good to have a prophylactic discussion; When is enough enough? What are the safeguards we need?

Orion Pictures

One of the ways that Nolan and Joy got away from turning their show into Terminator was by examining consciousness and storytelling from the perspective of artificial intelligence, instead of just humans. Nolan said they wanted to dive into the commonalities shared by humans and AI instead of just the differences to showcase just how powerful and resonating the technology can be.

We were attempting to look deeply at the question of consciousness and one of the things I was surprised about is that conscious is still largely a question for philosophers, Nolan said. It's not something the [computer science] folks [we talked to] want to get into.

Nolan added, however, that what became apparent during their conversations with experts in the field was the main question is quickly becoming whether consciousness is a necessary function going forward.

Consciousness is either very, very important and very hard to explore, or, as more than one computer scientist we talked to suggested, maybe it doesn't need to exist, Nolan said. It's not necessary.

Nolan and Joy didnt provide any hints as to what the second season of Westworld will focus on specifically, but Nolan said it will continue to examine the question of how stories are told and how important consciousness is to both humans and artificial intelligence.

Throughout history we have defined consciousness as that which others do not possess, Joy said. That bar has shifted. It's all subjective.

Westworld will return for a second season in 2018.

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Westworld creators want to make a show about AI without 'going straight to Skynet' - Polygon

These AI Agents Punched Holes in Their Virtual Universe While Playing Hide and Seek – Computer Business Review

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Bots removed opponents tools from the game space, and launched themselves into the air

Two teams of AI agents tasked with playing a game (or million) of hide and seek in a virtual environment developed complex strategies and counterstrategies and exploited holes in their environment that even its creators didnt even know that it had.

The game was part of an experiment by OpenAI designed to test the AI skills that emerge from multi-agent competition and standard reinforcement learning algorithms at scale. OpenAI described the outcome in a striking paper published this week.

The organisation, now heavily backed by Microsoft, described the outcome as further proof that skills, far more complex than the seed game dynamics and environment, can emerge (from such experiments/training exercises).

Some of its findings are neatly captured in the video below.

In a blog post, Emergent Tool Use from Multi-agent Interaction, OpenAI noted: These results inspire confidence that in a more open-ended and diverse environment, multi-agent dynamics could lead to extremely complex and human-relevantbehavior.

The AI hide and seek experiment, which pitted a team of finders against a team of seekers, made use of two core techniques in AI: multi-agent learning, which uses multiple algorithms in competition or coordination, and reinforcement learning; a form of programming that uses reward and punishment techniques to train algorithms.

In the game of AI hide and seek, the two opposing teams of AI agents created a range of complex hiding and seeking strategies compellingly illustrated in a series of videos by OpenAI that involved collaboration, tool use, and some creative pushing at the bounderies of the virtual parameters the world creators thought theyd set.

Another method to learn skills in an unsupervised manner is intrinsic motivation, which incentivizes agents to explore with various metrics such as model error or state counts, OpenAIs researchers Bowen Baker, Ingmar Kanitscheider, Todor Markov, Yi Wu, Glenn Powell, Bob McGrew and Igor Mordatch noted.

We ran count-based exploration in our environment, in which agents keep an explicit count of states theyve visited and are incentivized to go to infrequently visited states, they added, detailing the outcomes which included the bots removing some of the tools their opponents were given entirely from the game space, and launching themselves into the air for a birds-eye view of their hiding opponent.

As they concluded: Building environments is not easy and it is quite often the case that agents find a way to exploit the environment you build in an unintended way.

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These AI Agents Punched Holes in Their Virtual Universe While Playing Hide and Seek - Computer Business Review

Future Goals in the AI Race: Explainable AI and Transfer Learning – Modern Diplomacy

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Recent years have seen breakthroughs in neural network technology:computers can now beat any living person at the most complex game invented by humankind, as well as imitate humanvoices and faces (both real and non-existent) in a deceptively realistic manner. Is this a victory for artificialintelligence over human intelligence? And if not, what else do researchers anddevelopers need to achieve to make the winners in the AI race the kings of the world?

Background

Over the last 60 years, artificialintelligence (AI) has been the subject of muchdiscussion among researchers representing different approaches and schools ofthought. One of the crucial reasons for this is that there is no unifieddefinition of what constitutes AI, with differences persisting even now. Thismeans that any objective assessment of the current state and prospects of AI, andits crucial areas of research, in particular, will be intricately linked withthe subjective philosophical views of researchers and the practical experienceof developers.

In recent years, the term general intelligence, meaning the ability tosolve cognitive problems in general terms, adapting to the environment throughlearning, minimizing risks and optimizing the losses in achieving goals, hasgained currency among researchers and developers. This led to the concept of artificialgeneral intelligence (AGI), potentially vested not in a human,but a cybernetic system of sufficient computational power. Many refer to thiskind of intelligence as strong AI, as opposed to weak AI, which has becomea mundane topic in recent years.

As applied AI technology has developed over the last 60 years, we cansee how many practical applications knowledge bases, expert systems, imagerecognition systems, prediction systems, tracking and control systems forvarious technological processes are no longer viewed as examples of AI andhave become part of ordinary technology. The bar for what constitutes AIrises accordingly, and today it is the hypothetical general intelligence,human-level intelligence or strong AI, that is assumed to be the real thingin most discussions. Technologies that are already being used are broken downinto knowledge engineering, data science or specific areas of narrow AI thatcombine elements of different AI approaches with specialized humanities ormathematical disciplines, such as stock market or weather forecasting, speechand text recognition and language processing.

Different schools of research, each working within their own paradigms,also have differing interpretations of the spheres of application, goals,definitions and prospects of AI, and are often dismissive of alternativeapproaches. However, there has been a kind of synergistic convergence ofvarious approaches in recent years, and researchers and developers areincreasingly turning to hybrid models and methodologies, coming up withdifferent combinations.

Since the dawn of AI, two approaches to AI have been the most popular.The first, symbolic approach, assumes that the roots of AI lie inphilosophy, logic and mathematics and operate according to logical rules, signand symbolic systems, interpreted in terms of the conscious human cognitiveprocess. The second approach (biological in nature), referred to asconnectionist, neural-network, neuromorphic, associative or subsymbolic, isbased on reproducing the physical structures and processes of the human brainidentified through neurophysiological research. The two approaches have evolvedover 60 years, steadily becoming closer to each other. For instance, logicalinference systems based on Boolean algebra have transformed into fuzzy logic orprobabilistic programming, reproducing network architectures akin to neuralnetworks that evolved within the neuromorphic approach. On the other hand,methods based on artificial neural networks are very far from reproducing thefunctions of actual biological neural networks and rely more on mathematicalmethods from linear algebra and tensor calculus.

Are There Holes in Neural Networks?

In the last decade, it was the connectionist, or subsymbolic, approachthat brought about explosive progress in applying machine learning methods to awide range of tasks. Examples include both traditional statisticalmethodologies, like logistical regression, and more recent achievements inartificial neural network modelling, like deep learning and reinforcementlearning. The most significant breakthrough of the last decade was broughtabout not so much by new ideas as by the accumulation of a critical mass oftagged datasets, the low cost of storing massive volumes of training samplesand, most importantly, the sharp decline of computational costs, including thepossibility of using specialized, relatively cheap hardware for neural networkmodelling. The breakthrough was brought about by a combination of these factorsthat made it possible to train and configure neural network algorithms to makea quantitative leap, as well as to provide a cost-effective solution to a broadrange of applied problems relating to recognition, classification andprediction. The biggest successes here have been brought about by systems basedon deep learning networks that build on the idea of the perceptronsuggested 60 years ago by Frank Rosenblatt. However, achievements in the use ofneural networks also uncovered a range of problems that cannot be solved usingexisting neural network methods.

First, any classic neural network model, whatever amount of data it istrained on and however precise it is in its predictions, is still a black boxthat does not provide any explanation of why a given decision was made, letalone disclose the structure and content of the knowledge it has acquired inthe course of its training. This rules out the use of neural networks incontexts where explainability is required for legal or security reasons. Forexample, a decision to refuse a loan or to carry out a dangerous surgicalprocedure needs to be justified for legal purposes, and in the event that aneural network launches a missile at a civilian plane, the causes of thisdecision need to be identifiable if we want to correct it and prevent futureoccurrences.

Second, attempts to understand the nature of modern neural networks havedemonstrated their weak ability to generalize. Neural networks rememberisolated, often random, details of the samples they were exposed to duringtraining and make decisions based on those details and not on a real generalgrasp of the object represented in the sample set. For instance, a neuralnetwork that was trained to recognize elephants and whales using sets ofstandard photos will see a stranded whale as an elephant and an elephantsplashing around in the surf as a whale. Neural networks are good atremembering situations in similar contexts, but they lack the capacity tounderstand situations and cannot extrapolate the accumulated knowledge tosituations in unusual settings.

Third, neural network models are random, fragmentary and opaque, whichallows hackers to find ways of compromising applications based on these modelsby means of adversarial attacks. For example, a security system trained toidentify people in a video stream can be confused when it sees a person inunusually colourful clothing. If this person is shoplifting, the system may notbe able to distinguish them from shelves containing equally colourful items.While the brain structures underlying human vision are prone to so-calledoptical illusions, this problem acquires a more dramatic scale with modernneural networks: there are known cases where replacing an image with noiseleads to the recognition of an object that is not there, or replacing one pixelin an image makes the network mistake the object for something else.

Fourth, the inadequacy of the information capacity and parameters of theneural network to the image of the world it is shown during training andoperation can lead to the practical problem of catastrophic forgetting. This isseen when a system that had first been trained to identify situations in a setof contexts and then fine-tuned to recognize them in a new set of contexts maylose the ability to recognize them in the old set. For instance, a neuralmachine vision system initially trained to recognize pedestrians in an urbanenvironment may be unable to identify dogs and cows in a rural setting, butadditional training to recognize cows and dogs can make the model forget how toidentify pedestrians, or start confusing them with small roadside trees.

Growth Potential?

The expert community sees a number of fundamental problems that need tobe solved before a general, or strong, AI is possible. In particular, asdemonstrated by the biggest annual AI conference held in Macao, explainable AI and transfer learning are simply necessary in somecases, such as defence, security, healthcare and finance. Many leadingresearchers also think that mastering these two areas will be thekey to creating a general, or strong, AI.

Explainable AI allows for human beings (the user of theAI system) to understand the reasons why a system makes decisions and approvethem if they are correct, or rework or fine-tune the system if they are not.This can be achieved by presenting data in an appropriate (explainable) manneror by using methods that allow this knowledge to be extracted with regard tospecific precedents or the subject area as a whole. In a broader sense,explainable AI also refers to the capacity of a system to store, or at leastpresent its knowledge in a human-understandable and human-verifiable form. Thelatter can be crucial when the cost of an error is too high for it only to beexplainable post factum. And herewe come to the possibility of extracting knowledge from the system, either toverify it or to feed it into another system.

Transfer learning is the possibility of transferringknowledge between different AI systems, as well as between man and machine sothat the knowledge possessed by a human expert or accumulated by an individualsystem can be fed into a different system for use and fine-tuning.Theoretically speaking, this is necessary because the transfer of knowledge isonly fundamentally possible when universal laws and rules can be abstractedfrom the systems individual experience. Practically speaking, it is theprerequisite for making AI applications that will not learn by trial and erroror through the use of a training set, but can be initialized with a base ofexpert-derived knowledge and rules when the cost of an error is too high orwhen the training sample is too small.

How to Get the Best of Both Worlds?

There is currently no consensus on how to make an artificial general intelligence that is capable ofsolving the abovementioned problems or is based on technologies that couldsolve them.

One of the most promising approaches is probabilisticprogramming, which is a modern development ofsymbolic AI. In probabilistic programming, knowledge takes the form ofalgorithms and source, and target data is not represented by values ofvariables but by a probabilistic distribution of all possible values. Alexei Potapov, a leading Russian expert on artificial general intelligence, thinksthat this area is now in a state that deep learning technology was in about tenyears ago, so we can expect breakthroughs in the coming years.

Another promising symbolic area is Evgenii Vityaevs semantic probabilistic modelling, which makes it possible to build explainable predictive models basedon information represented as semantic networks with probabilistic inferencebased on Pyotr Anokhins theory of functional systems.

One of the most widely discussed ways to achieve this is throughso-called neuro-symbolic integration an attempt to get the best of bothworlds by combining the learning capabilities of subsymbolic deep neuralnetworks (which have already proven their worth) with the explainability ofsymbolic probabilistic modelling and programming (which hold significantpromise). In addition to the technological considerations mentioned above, thisarea merits close attention from a cognitive psychology standpoint. As viewed by Daniel Kahneman, human thought can be construed as the interaction oftwo distinct but complementary systems: System 1 thinking is fast, unconscious,intuitive, unexplainable thinking, whereas System 2 thinking is slow,conscious, logical and explainable. System 1 provides for the effectiveperformance of run-of-the-mill tasks and the recognition of familiarsituations. In contrast, System 2 processes new information and makes sure wecan adapt to new conditions by controlling and adapting the learning process ofthe first system. Systems of the first kind, as represented by neural networks,are already reaching Gartnersso-called plateau of productivity in avariety of applications. But working applications based on systems of thesecond kind not to mention hybrid neuro-symbolic systems which the mostprominent industry players have only started to explore have yet to becreated.

This year, Russian researchers, entrepreneurs and government officialswho are interested in developing artificial general intelligence have a uniqueopportunity to attend the first AGI-2020 international conference in St. Petersburg in late June 2020, wherethey can learn about all the latest developments in the field from the worldsleading experts.

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Future Goals in the AI Race: Explainable AI and Transfer Learning - Modern Diplomacy

Realeyes Announces The Development of Enhanced Emotion AI Technology Surpassing Industry Standards for Understanding People’s Attention and Emotions -…

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NEW YORK, April 28, 2020 (GLOBE NEWSWIRE) -- Realeyes, a leading computer vision and emotion AI company, announced today the availability of its next-generation facial coding technology. Realeyes uses front-facing cameras and the latest in computer vision and machine learning technologies to detect attention and emotion among opt-in audiences as they watch video content. The enhanced classification system will provide customers with more sensitive, accurate insights into the emotional impact of their video content.

Realeyes continues to set the industry standard for facial coding accuracy. The improved classification system results in a 20% increase in emotion detection across all measured emotions from facial cues. It also reduces occasional false positive emotion readings by half. Realeyes is the most accurate emotion detection technology among leading API cloud providers, based on an internal benchmark study of thousands of videos.

Our technology has reached a new level of sophistication, with the accuracy of our detection beginning to rival that of humans across certain emotions like happiness and surprise, said Elnar Hajiyev, Chief Technology Officer at Realeyes. Realeyes is building transformational apps to enable companies to create more remarkable experiences for people, and it starts with a foundation of world-class core vision technology. Realeyes today holds 11 patents covering different aspects of building emotion AI technology, and has 29 pending.

The updated classifications are applied to all Realeyes products and improve on the platforms ability to accurately analyze a wider variety of viewers faces and emotions. The new classifications allow for more nuanced reading through more sensitive emotional curves and bring greater value to the data collected through facial coding.

Said Hajiyev: More accurate detection enables companies to better understand the pure attention and emotion response of their audiences. However, more accurate emotion detection also enables advertisers to better predict in-market outcomes such as video view-through rates, so they can create more engaging creative to maximize media spend.

Realeyes upgraded classifiers allow for a greater range of facial measurements across ethnicities, especially those of Asian heritage. Combined with improvements to Realeyes performance on mobile devices, the updates pave the way for an entirely new range of products and applications based on emotion AI, along with relevance in new markets around the world. Realeyes announced the appointment of its Japan country manager Kyoko Tanaka followed by last years strategic investment from notable international investors Draper Esprit, and NTT DOCOMO Ventures, Inc., the VC arm of NTT Group, Japans leading mobile operator.

Trained on the worlds richest database of facial coding data, Realeyes technology now incorporates more than 615 million emotional labels across more than 3.8 million video sessions to provide more nuanced insights into the emotional impact of video content. The recent update strengthens Realeyes predictive modeling for behaviors like view-through rate and responses like interest and likability, while providing best-in-class results 8x faster than its previous version.

About Realeyes

Using front-facing cameras and the latest in computer vision and machine learning technologies, Realeyes measures how people feel as they watch video content online, enabling brands, agencies and media companies to inform and optimize their content as well as target their videos at the right audiences. Realeyes technology applies facial coding to predictive, big-data analytics, driving bottom-line business outcomes for brands and publishers.

Founded in 2007, Realeyes has offices in New York, London, Tokyo and Budapest. Customers include brands such as Mars Inc, AT&T, Hersheys and Coca-Cola, agencies Ipsos, MarketCast and Publicis, and media companies such as Warner Media and Teads.

Media Contact:

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Broadsheet Communications

(917) 826 - 1103

ben@broadsheetcomms.com

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3 Ethical Considerations When Investing in AI – Manufacturing Business Technology

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While Artificial Intelligence (AI) has been prevalent in industries such as the financial sector, where algorithms and decision trees have long been used in approving or denying loan requests and insurance claims, the manufacturing industry is at the beginning of its AI journey. Manufacturers have started to recognize the benefits of embedding AI into business operationsmarrying the latest techniques with existing, widely used automation systems to enhance productivity.

A recent international IFS study polling 600 respondents, working with technology including Enterprise Resource Planning (ERP), Enterprise Asset Management (EAM), and Field Service Management (FSM), found more than 90 percent of manufacturers are planning AI investments. Combined with other technologies such as 5G and the Internet of Things (IoT), AI will allow manufacturers to create new production rhythms and methodologies. Real-time communication between enterprise systems and automated equipment will enable companies to automate more challenging business models than ever before, including engineer-to-order or even custom manufacturing.

Despite the productivity, cost-savings and revenue gains, the industry is now seeing the first raft of ethical questions come to the fore. Here are the three main ethical considerations companies must weigh-up when making AI investments.

At first, AI in manufacturing may conjure up visions of fully automated smart factories and warehouses, but the recent pandemic highlighted how AI can play a strategic role in the back-office, mapping different operational scenarios and aiding recovery planning from a finance standpoint. Scenario planning will become increasingly important. This is relevant as governments around the world start lifting lockdown restrictions and businesses plan back to work strategies. Those simulations require a lot of data but will be driven by optimization, data analysis and AI.

And of course, it is still relevant to use AI/Machine Learning to forecast cash. Cash is king in business right now. So, there will be an emphasis on working out cashflows, bringing in predictive techniques and scenario planning. Businesses will start to prepare ways to know cashflow with more certainty should the next pandemic or crisis occur.

For example, earlier in the year the conversation centered on the just-in-time scenarios, but now the focus is firmly on what-if planning at the macro supply chain level:

Another example is how you can use a Machine Learning service and internal knowledge base to facilitate Intelligent Process Automation allowing recommendations and predictions to be incorporated into business workflows, as well as AI-driven feedback on how business processes themselves can be improved or automated.

The closure of manufacturing organizations and reduction in operations due to depleting workforces highlight AI technology in the front-office isnt perhaps as readily available as desired, and that progress needs to be made before it can truly provide a level of operational support similar to humans.

Optimists suggest AI may replace some types of labor, with efficiency gains outweighing transition costs. They believe the technology will come to market at first as a guide-on-the-side for human workers, helping them make better decisions and enhancing their productivity, while having the potential to upskill existing employees and increase employment in business functions or industries that are not in direct competition with AI.

Indeed, recent IFS research points to an encouraging future for a harmonized AI and human workforce in manufacturing. The IFS AI study revealed that respondents saw AI as a route to create, rather than cull, jobs. Around 45 percent of respondents stated they expect AI to increase headcount, while 24 percent believe it wont impact workforce figures.

The pandemic has demonstrated AI hasnt developed enough to help manufacturers maintain digital-only operations during unforeseen circumstances, and decision makers will be hoping it can play a greater role to mitigate extreme situations in the future.

It is easy for organizations to say they are digitally transforming. They have bought into the buzzwords, read the research, consulted the analysts, and seen the figures about the potential cost savings and revenue growth.

But digital transformation is no small change. It is a complete shift in how you select, implement and leverage technology, and it occurs company-wide. A critical first step to successful digital transformation is to ensure that you have the appropriate stakeholders involved from the very beginning. This means manufacturing executives must be transparent when assessing and communicating the productivity and profitability gains of AI against the cost of transformative business changes to significantly increase margin.

When businesses first invested in IT, they had to invent new metrics that were tied to benefits like faster process completion or inventory turns and higher order completion rates. But manufacturing is a complex territory. A combination of entrenched processes, stretched supply chains, depreciating assets and growing global pressures makes planning for improved outcomes alongside day-to-day requirements a challenging prospect. Executives and their software vendors must go through a rigorous and careful process to identify earned value opportunities.

Implementing new business strategies will require capital spending and investments in process change, which will need to be sold to stakeholders. As such, executives must avoid the temptation of overpromising. They must distinguish between the incremental results they can expect from implementing AI in a narrow or defined process as opposed to a systemic approach across their organization.

There can be intended or unintended consequences of AI-based outcomes, but organizations and decision makers must understand they will be held responsible for both. We have to look no further than tragedies from self-driving car accidents and the subsequent struggles that followed as liability is assigned not on the basis of the algorithm or the inputs to AI, but ultimately the underlying motivations and decisions made by humans.

Executives therefore cannot afford to underestimate the liability risks AI presents. This applies in terms of whether the algorithm aligns with or accounts for the true outcomes of the organization, and the impact on its employees, vendors, customers and society as a whole. This is all while preventing manipulation of the algorithm or data feeding into AI that would impact decisions in ways that are unethical, either intentionally or unintentionally.

Margot Kaminski, associate professor at the University of Colorado Law School, raised the issue of automation biasthe notion that humans trust decisions made by machines more than decisions made by other humans. She argues the problem with this mindset is that when people use AI to facilitate decisions or make decisions, they are relying on a tool constructed by other humans, but often they do not have the technical capacity, or practical capacity, to determine if they should be relying on those tools in the first place.

This is where explainable AI will be criticalAI which creates an audit path so both before and after the fact, there is a clear representation of the outcomes the algorithm is designed to achieve and the nature of the data sources it is working form. Kaminski asserts explainable AI decisions must be rigorously documented to satisfy different stakeholdersfrom attorneys to data scientists through to middle managers.

Manufacturers will soon move past the point of trying to duplicate human intelligence using machines, and towards a world where machines behave in ways that the human mind is just not capable. While this will reduce production costs and increase the value organizations are able to return, this shift will also change the way people contribute to the industry, the role of labor, and civil liability law.

There will be ethical challenges to overcome, but those organizations who strike the right balance between embracing AI and being realistic about its potential benefits alongside keeping workers happy will usurp and take over. Will you be one of them?

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Meet Five Synthetic Biology Companies Using AI To Engineer Biology – Forbes

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AI is changing the field of synthetic biology and how we engineer biology. Its helping engineers design new ways to design genetic circuits -- and it could leave a remarkable impact on the future of humanity

TVs and radios blare that artificial intelligence is coming, and it will take your job and beat you at chess.

But AI is already here, and it can beat you and the worlds best at chess. In2012, it was also used by Google to identify cats in YouTube videos. Today, its the reason Teslas have Autopilot and Netflix and Spotify seem to read your mind. Now, AI is changing the field of synthetic biology and how we engineer biology. Its helping engineers design new ways to design genetic circuits and it could leave a remarkable impact on the future of humanity through the huge investment it has been receiving ($12.3b in the last 10 years) and the markets it is disrupting.

The idea of artificial intelligence is relatively straightforward it is the programming of machines with reasoning, learning, and decision-making behaviors. Some AI algorithms (which are just a set of rules that a computer follows) are so good at these tasks that they can easily outperform human experts.

Most of what we hear about artificial intelligence refers to machine learning, a subclass of AI algorithms that extrapolate patterns from data and then use that analysis to make predictions. The more data these algorithms collect, the more accurate their predictions become. Deep learning is a more powerful subcategory of machine learning, where a high number of computational layers called neural networks (inspired by the structure of the brain) operate in tandem to increase processing depth, facilitating technologies like advanced facial recognition (including FaceID on your iPhone).

[For a more detailed explanation of artificial intelligence and its various subcategories, check out this article and its flowchart.]

Regardless of the type of AI, or its application, we are in the midst of a computational revolution that is extending its tendrils beyond the computer world. Soon, AI will impact the medicines you take, the fuels you burn, and even the detergents that you use to wash your clothes.

Biology, in particular, is one of the most promising beneficiaries of artificial intelligence. From investigating genetic mutations that contribute to obesity to examining pathology samples for cancerous cells, biology produces an inordinate amount of complex, convoluted data. But the information contained within these datasets often offers valuable insights that could be used to improve our health.

In the field of synthetic biology, where engineers seek to rewire living organisms and program them with new functions, many scientists are harnessing AI to design more effective experiments, analyze their data, and use it to create groundbreaking therapeutics. Here are five companies that are integrating machine learning with synthetic biology to pave the way for better science and better engineering.

(Oakland, CA, founded in 2014, has raised $24.9M)

Machine learning algorithms must begin with large amounts of data but, in biology, good data is incredibly challenging to produce because experiments are time-consuming, tedious and hard to replicate. Fortunately, one company is addressing this bottleneck by making it easier for scientists to do exactly that.

Riffyns cloud-based software platform helps researchers standardize, define, and perform experiments and streamlines data analysis, which enables researchers to focus on doing the actual science and makes the use machine learning algorithms to extract deeper insights from their experiments an everyday reality.

With this platform, experiments can be conducted more efficiently, leading to massive decreases in cost, improvements in productivity and quality, and data that is primed to be further analyzed with sophisticated machine learning techniques. That means companies can use this technology to develop new proteins for cancer therapeutics, and they can do it much faster and better than before. Riffyn already works with 8 of the top 15 global biotech and biopharma firms and they were founded just five years ago.

(Cambridge, UK, officially launched in 2019)

There are a lot of moving parts in the synthetic biology world, which makes it difficult but vital to streamline and integrate operations as much as possible. For the last decade, the computational biology arm of Microsoft Research, Station B, has been developing machine learning models for biology to fix this problem and expedite research across a variety of fields, from medicine to construction.

Its efforts are paying off in the form of various new partnerships, too. With Synthace, it is developing software to automate and expedite experiments in the lab. Station B is additionally working with Princeton to research the mechanisms behind biofilms (relevant to how bacterial colonies develop antibiotic resistance) by utilizing machine learning-based methods that extract patterns from images taken during different stages of bacterial growth. Station B is also collaborating with Oxford Biomedica, a company harnessing these machine learning capabilities to improve a promising gene therapy for leukemia and lymphoma. This is perhaps one of synthetic biologys biggest areas for impact: designing therapeutics to combat a variety of diseases.

(Based in San Francisco, CA, founded in 2012, has raised $51M)

Atomwise is tackling drug development with their deep-learning platform, called AtomNet, that can rapidly model molecular structures. It can accurately analyze chemical interactions within small molecules to predict the efficacy of targeting diseases ranging from Ebola to multiple sclerosis. By utilizing data about atomic structure, Atomwise designs novel therapeutics that would otherwise be nearly impossible to develop.

They have numerous academic and corporate partnerships with institutions including Charles River Laboratories, Merck, University of Toronto, and Duke University School of Medicine, that are providing many of the real-world applications and opportunities to drive this research forward. They also recently announced an up-to $1.5B collaboration with the Jiangsu Hansoh Pharmaceutical Group, the Chinese company with one of this years biggest biopharma IPOs.

While Atomwises approach to designing molecules is powerful and well on its way to combatting multiple diseases, there is no one perfect method to computational discovery. Thats where Arzeda comes in.

(Seattle, WA, founded in 2008, has raised $15.2M)

Arzeda, a company originating from the Baker Lab at the University of Washington, uses its protein design platform (rooted, of course, in machine learning algorithms) to engineer proteins for everything from industrial enzymes to crops and their microbiomes.

Arzeda builds its molecules entirely from scratch (or de novo), rather than optimize existing ones, to perform new functions not found anywhere in nature; deep learning techniques are vital to ensure the proteins they design fold correctly (a very computationally demanding problem) and function as intended. Once the computational steps are complete, the new proteins are produced through fermentation (just like beer), bypassing natural evolution to efficiently produce brand-new molecules.

(South San Francisco, CA, founded in 2012, self-funded by licensing technologies)

On the other end of the design spectrum, Distributed Bio harnesses rational protein engineering to optimize existing antibodies, which are the proteins in your body that detect bacteria and fight off other disease-causing invaders, to create novel therapeutics.

Among the many immunology-engineering technologies that the company boasts (from a universal flu vaccine to a broad-coverage snake antivenom) is the Tumbler platform. Using machine learning methods, Tumbler creates over 500 million variations of a starting antibody to expand and quantify the search space of what changes to the molecule are most valuable; then, it scores sequences to predict how well they bind to their target in real life and uses the valuable change information to further improve the best-scoring sequences. The production cycle continues as the top sequences are synthesized and tested in the lab. Eventually, an archetypal molecule emerges to fulfill the intended therapeutic purpose something not necessarily observed in nature, but combining all of the best possible characteristics.

Tumbler has helped to enable a wide range of applications beyond traditional single-target drug development from designing antibodies that bind to multiple targets simultaneously to creating chimeric antigen receptor T-cell (CAR-T) therapies (together with Chimera Bioengineering) for cancer treatments with reduced toxicity, the power of this end-to-end optimization platform to generate ideal antibodies at scale is unprecedented.

While this progress is exciting, artificial intelligence is not a universal replacement for our investigations of the natural world, nor is it the only way to develop cures for human diseases. At times, it may not be technically useful or even ethically sound. As we continue to reap the benefits of this technology and increasingly incorporate it into our daily lives, we must continue having conversations about the design, implementation, and ethics of innovations in synthetic biology and AI; we stand on the precipice of a new age for science and humanity.

Thanks to Aishani Aatresh for additional research and reporting in this article. Aishani is also a researcher at Distributed Bio developing computational immunoengineering methods to generate superior antibodies. Please note: I am the founder of SynBioBeta, the innovation network for the synthetic biology industry, and some of the companies that I write about are sponsors of the SynBioBeta conference (click here for a full list of sponsors).

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Meet Five Synthetic Biology Companies Using AI To Engineer Biology - Forbes

NVIDIA’s Accelerated Computing Platform To Power Japan’s Fastest AI Supercomputer – Forbes

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NVIDIA's Accelerated Computing Platform To Power Japan's Fastest AI Supercomputer
Forbes
Tokyo Tech is in the process of building its next-generation TSUBAME supercomputer, featuring NVIDIA GPU technology and the company's Accelerated Computing Platform. TSUBAME 3.0, as the system will be known, will ultimately be used in tandem with ...
Japan announces AI supercomputerScientific Computing World

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NVIDIA's Accelerated Computing Platform To Power Japan's Fastest AI Supercomputer - Forbes

Reveal Acquires NexLP to become the leading AI-powered eDiscovery Solution – PR Newswire India

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"The future of eDiscovery is artificial intelligence. We've acquired the leader in this space to ensure our platform is powered by cutting-edge AI technology and NexLP's premier data science team," said Reveal CEO, Wendell Jisa. "This exclusive integration of NexLP AI into Reveal's solution provides our clients the opportunity to lead in the evolution of how law is practiced."

NexLP's artificial intelligence platform turns disparate, unstructured data - including email communications, business chat messages, contracts and legal documents - into meaningful insights that can be used to deliver operational efficiencies and proactive risk mitigation for legal, corporate and compliance teams.

Reveal clients have access to the next-generation solution now. The companies have worked to fully integrate NexLP's AI software into Reveal's review software for more than a year. All features, including the industry-exclusive ability to run multiple AI models, as well as all future functionality, become part of Reveal's standard software. NexLP's artificial intelligence platform will remain available as a stand-alone application for current clients.

With the acquisition, Jay Leib, Co-Founder and CEO of NexLP, joins the leadership team of Reveal as its EVP of Innovation & Strategy.

"We chose Reveal, after considering all the major players in the space, because they offer by-far, the most comprehensive, solutions-oriented technology on the market and we have a shared vision for the future of legal technology," said Jay Leib, Reveal EVP of Innovation & Strategy. "Reveal's global footprint and ability to deploy the Reveal solution in the cloud or on-premise enables us to rapidly expand the adoption of AI to tens of thousands of legal, risk and compliance professionals overnight. Our existing clients and partners should all be thrilled with our ability to expand our capabilities by joining Reveal."

The NexLP acquisition is Reveal's second major investment since Gallant Capital Partners, a Los Angeles-based investment firm, acquired a majority stake in Reveal in 2018. In June 2019, Reveal acquired Mindseye Solutions, an industry-leading processing and early case assessment software solution.

About Reveal Data Corporation

Reveal helps legal professionals solve complex discovery problems. As a cloud-based provider of eDiscovery, risk and compliance software, Reveal offers the full range of processing, early case assessment, review and artificial intelligence capabilities. Reveal clients include Fortune 500 companies, legal service providers, government agencies and financial institutions in more than 40 countries across five continents. Featuring deployment options in the cloud or on-premise, an intuitive user design, multilingual user interfaces and the automatic detection of more than 160 languages, Reveal accelerates legal review, saving users time and money. For more information, visit http://www.revealdata.com.

About NexLP

NexLP's Story Engine uses AI and machine learning to derive actionable insight from structured and unstructured data to help legal, corporate and compliance teams proactively mitigate risk and untapped opportunities faster and with a greater understanding of context. In 2014, NexLP was selected to be a member of TechStars Chicago. For more information, visit:http://www.nexlp.com.

Contact

Jennifer Fournier[emailprotected]

Photo - https://mma.prnewswire.com/media/1226822/Jisa_and_Leib_Announcement.jpg

http://www.revealdata.com

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Reveal Acquires NexLP to become the leading AI-powered eDiscovery Solution - PR Newswire India

Chiefs Otis Taylor, Marty Schottenheimer, Carl Peterson and Lloyd Wells nominated to Hall of Fame – Arrowhead Pride

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On Thursday, four former Kansas City Chiefs passed the first hurdle to be inducted into the Pro Football Hall of Fame in August of 2023: wide receiver Otis Taylor, head coach Marty Schottenheimer, general manager Carl Peterson and scout Lloyd Wells.

Taylor was nominated as a Senior candidate, while Schottenheimer, Peterson and Wells were nominated as Coach/Contributor candidates. A total of 25 Seniors candidates were named, along with 29 total Coach/Contributor candidates.

The Senior and Coach/Contributor selection committees will now pare these two lists to 12 candidates each before July 27. The Seniors committee will reduce that to three finalists on August 16, while the Coach/Contributor group will be reduced to one finalist on August 23. In most years, only one person from each group may become a finalist. However, under special rules, the Hall will allow three Senior finalists in 2023, 2024 and 2025.

Taylor, 79, played his entire NFL career in Kansas City after being taken in the fourth round (29th overall) of the 1965 AFL Draft. He was a key member of both the 1966 and 1969 teams that appeared in the first and fourth Super Bowls and his legendary 46-yard touchdown reception to ice the Chiefss 23-7 victory over the Minnesota Vikings in Super Bowl IV is among the most celebrated plays in team history. Taylor retired after the 1975 season and has long been considered as a player who belonged in the Hall.

Schottenheimer served as the teams head coach from 1989 through 1998, returning the team to respectability with a 101-58-1 record. After the Chiefs had made playoffs only once between 1971 and 1990, Schottenheimers teams made the postseason in seven of his 10 Kansas City seasons. Unfortunately, he was never able to translate his regular-season success into postseason victories, winning in only three of his 10 playoff appearances with the Chiefs. Still, he is one of only eight NFL coaches to collect more than 200 wins. Of those, Schottenheimer is the only eligible coach who has not yet been inducted into the Hall. He died in February of 2021.

Peterson, 79, hired Schottenheimer when he was hired as the teams general manager in 1989. While he remained in his position for another ten years after Schottenheimers resignation from the team following the 1998 season, the two are inexorably linked in the minds of Kansas City fans. During Petersons 20 years as the GM, the Chiefs turned in only six losing seasons, compiling a record of 176-143-1.

Wells is often seen as one of those who finally fully opened the doors for Black athletes in professional football, serving with the Chiefs informally before he became professional footballs full-time Black scout in 1963. He was responsible for recruiting many players from historically Black colleges and universities (HBCUs), including previous Hall of Fame inductees Buck Buchanan, Emmitt Thomas and Willie Lanier. His story is also intertwined with Taylors. It was Lloyd who famously arranged to hide the former Prairie View A&M wide receiver in a Texas hotel room to prevent the Dallas Cowboys from signing him to an NFL contract after both teams had drafted him in 1965. Wells died in 2005.

The other Senior candidates include Ken Anderson, Maxie Baughan, Mark Clayton, Roger Craig, LaVern Dilweg, Randy Gradishar, Lester Hayes, Chris Hinton, Chuck Howley, Cecil Isbell, Joe Jacoby, Billie White Shoes Johnson, Mike Kenn, Joe Klecko, Bob Kuechenberg, George Kunz, Jim Marshall, Clay Matthews Jr., Eddie Meador, Stanley Morgan, Tommy Nobis, Ken Riley, Sterling Sharpe and Everson Walls.

The remaining Coach/Contributor candidates are K.S. Bud Adams Jr., Roone Arledge, C.O. Brocato, Don Coryell, Otho Davis, Ralph Hay, Mike Holmgren, Frank Bucko Kilroy, Eddie Kotal, Robert Kraft, Rich McKay, John McVay, Art Modell, Clint Murchison Jr., Buddy Parker, Dan Reeves, Lee Remmel, Art Rooney Jr., Jerry Seeman, Mike Shanahan, Clark Shaughnessy, Seymour Siwoff, Amy Trask, Jim Tunney, Jack Vainisi and John Wooten.

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Chiefs Otis Taylor, Marty Schottenheimer, Carl Peterson and Lloyd Wells nominated to Hall of Fame - Arrowhead Pride

Mom remembers her daughters, 10 years after their brutal deaths – Star Tribune

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RIVER FALLS, Wis. Standing at the gravesite of her three daughters, Jessica Lee Peterson looks at the polished granite marker for a quiet moment, then speaks.

"It's hard to believe it's been 10 years," the River Falls woman said. "It feels like just yesterday sometimes."

Her girls were 11, 8, and 5 years old when their father killed them 10 years ago Sunday in an unconscionable act of hatred. The brutality of the crime he used a knife shocked people far beyond this city of 15,000, a story that found its way into tabloids around the world.

The trial was swift, and Aaron Schaffhausen was sentenced to life in prison. He's never going to be released.

A decade after her three girls were taken away, Peterson has found purpose in keeping alive the memories of the three girls: Amara, 11, Sophie, 8, and Cecilia, just 5. She stays close by talking about them often, and visiting the playground built in their memory near her house. This summer, she will release a book about their lives.

She has her own lifelong sentence to serve, one she tries to carry with grace, she said in a recent interview. Her pain has been eased by others, from close family to kind strangers who know her only as that grieving mother.

"I would be lying if I didn't say I feel guilty sometimes for enjoying life," she said while seated at the dining room table of the River Falls house she shares with her husband, Matthew Peterson. The two married in 2013, and have two young children, plus his two daughters from a previous relationship. They had their first date just days before the girls were killed. He never met them.

Jessica and Aaron had first met in Mankato, and moved to River Falls in 2006. They had three girls, but were divorced a few years later.

Schaffhausen had been threatening to Jessica and the girls in the past, but it seemed in the summer of 2012 that he was making up for his bad behavior, said Peterson. He made a surprise visit while Jessica was at work and the girls were home alone with a babysitter. Jessica told him he could stay and visit the girls as long as he was gone before she got home.

He called her later that day with a chilling statement, part of which was: "I killed the kids."

Peterson's memory of what happened next is spotty, a common reaction to deep trauma. She remembers talking to a 911 operator for 45 minutes as she raced home. She never learned the operator's name, nor spoke to them again, but thinks of that person as the first in a long line of people who helped her survive.

At the funeral, she invited everyone, including her ex-husband's family. She wanted it to be about the girls and not her. And she made a decision: "I decided very early on that I did not want to numb myself, with medication or maybe other things," said Peterson. She wanted to feel the joy of the good days she had with her daughters, even if it meant feeling the pain of losing them. She wanted to feel it all.

Some of the people close to her wanted to feel nothing, to shut it out. She's learned that everyone process grief differently, that there's no right way to do it.

Nothing came easy at first. Her trips to the grocery store could be upended when she found herself bawling over the sight of a jar of peanut butter. The first time she made it through the aisles without crying, a clerk who knew what Jessica was struggling with cheered her on. "You did it!"

It would become one of hundreds of interactions with locals in River Falls that convinced Peterson to stay put. At least there, people understood her story, she said.

She still marvels at the help she got from the leadership of Affinity Plus Federal Credit Union, which held the mortgage of her old house. Affinity Plus foreclosed on the house and razed it, donating materials to Habitat for Humanity before selling the lot. Proceeds of the sale went toward a new playground in honor of Amara, Sophie, and Cecilia Lee. The Tri-Angels playground at Hoffman Park opened in 2015, built with $550,000 in raised funds. It sits near the cemetery that holds some of the girls' ashes. Across the street is their old elementary school, Greenwood.

When Affinity Plus invited Jessica to speak at their all-staff meeting, she went simply to say thank you.

"And then it morphed into a talk about how people can feel impotent in the face of tragedy, but Affinity Plus showed just how powerful individuals and organizations can be in combating darkness. I spoke about how the individual employees may have felt like they did nothing but how their actions of simply working for an organization can help bring about true change," she said.

The experience left Peterson with hope: she could talk about what happened and turn it into something positive. Soon she found herself making public speaking engagements. She found she wanted to talk about her girls. She wanted to carry them forward.

Soon after the girls' deaths, Jessica began sharing things about them on Facebook. It was sporadic at first, but by late 2013 she settled into a rhythm. She started writing letters to her deceased children, first as therapy, and then as the foundation for her book.

"It was very, very therapeutic," said Peterson, a Washington County social worker.

Five years ago her writing got more ambitious, and she stared to dig into what had happened.

"I would hit these moments where I'm going, 'I'm crazy. Is anybody really going to read this?'" said Peterson.

She connected with a published author, Rick Paulas, and he helped her with structure and writing. She thought about self-publishing, but wanted a more professional approach. A lot of the feedback she got from publishers was that the story was too dark.

Finally, one of her husband's old classmates put Peterson in touch with publisher, Written Dreams Publishing out of Green Bay, Wis.

She wrote much of the book while living with Matthew and their blended family not far from the cemetery. For the hardest part of the book, the section that recounts the day that her girls died, Peterson headed to a remote Wausau cabin with plans to spend a week there by herself. Just before she left, she visited her old neighborhood for the first time since the girls died. It had been 7 years.

Now that the book is finished, Peterson can't wait to share it. She has 450 pre-orders, but doesn't know the exact date it will be available. She titled it "Thistles and Thorns", named for a dinnertime conversation game she plays with her children. For Peterson, it's heartening to know that people want to hear the story of Amara, Sophie, and Cecilia Lee.

"So many of the people around you have suffered losses," she said. "If I can help just one person weather the storms that life brings us, it lessens the pain a little bit."

"Plus," she added, "I just like talking about my kids."

Information on Jessica Lee Peterson's book "Thistles and Thorns" can be found online at https://thistlesandthorns3x.squarespace.com/

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Mom remembers her daughters, 10 years after their brutal deaths - Star Tribune

Brian Robinson Jr. reminds RB coach of Alfred Morris and Adrian Peterson – NBC Sports

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Following in the footsteps of two of the best Washington running backs in recent memory isnt a bad way to go for a rookie out of Alabama.

Brian Robinson Jr. is set to be an integral member of the Commanders RB room this season along with Antonio Gibson, J.D. McKissic and Jaret Patterson. Robinson, unlike his position mates, is a big and barreling body who can truck through defenders, rather than solely juking around them.

Washington running backs coach Randy Jordan spoke on the contributions Robinson could make this season during OTAs. He evoked a couple of names that Commanders fans are sure to be familiar with.

It was a run where we didnt get up on the second level and he was able to kind of bait [the defense] with his eyes and his body, knowing that the lineman hadnt come off yet, Jordan said. And the backer went one way and he replaced the backer. I said, Dog, you cant coach that.

"Like, the only other guys Ive kinda seen that were Alfred Morris and Adrian Peterson. Those two guys.

Robinson imitating two of his D.C. predecessors, intentionally or otherwise, is a good omen for the 23-year-old. Morris was Washingtons lead rusher from 2012-15 while Peterson, even at age 33, led the squad in rushing yards in 2018 and 2019.

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Morris was tinier than Robinson and used a stealthy combination of quickness, agility and strength to evade tacklers during his time in Burgundy & Gold. Its easy to see why Jordan sees the connection between Robinson and Peterson, though.

Both backs are around 6-foot-2 and 220 pounds. They both have shown the ability to cut on a dime and plow through defensive lines when need be. Its a lot to live up to, but Robinsons comparison to the future Hall-of-Famer in Peterson is high praise.

Jordan got more specific when discussing what specifically Robinson does that can make him such an effective weapon in the Commanders backfield.

Hes nifty now. Hes sneaky nifty, Jordan said. The thing is hes 62 and his ability to move backwardlike theres a couple runs he had in there and I said, Hey man, thats scary good, like thats graduate work. Thats like tour level.

Robinsons niftiness was part of the reason why he was able to rise through the ranks and become the RB1 at the best college program in the country: Alabama. Robinson impressed coaches early on, but had to wait until his predecessors Najee Harris and Josh Jacobs got their 15 minutes of fame before punching his own ticket.

Once he got the starting job, Robinson never looked back. He broke the Crimson Tide record when he became the first back in school history to rush for 200+ yards in a bowl game, which he did in the college football playoff vs. Cincinnati this past season.

Washington liked the pick immediately when they snagged Robinson in the third round this past April. He hasnt played a down of NFL football yet, but if his Alabama tape and initial impressions in Ashburn are any indication, his coach says, the Commanders could be in for a treat.

Just him being a natural running back, cause thats all hes played, so he understands where his limits are, Jordan said. Every run, he kinda knows where everybody is supposed to fit.

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Brian Robinson Jr. reminds RB coach of Alfred Morris and Adrian Peterson - NBC Sports

Ingram Micro Cloud and Google partner to give Southeast Asian companies a boost – The Sociable

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The Channel just got a huge boost, as now over 600 million people in Southeast Asia can access an array of mission-critical cloud services through Ingram Micro Clouds (IMC) vast online marketplace.

This is good news for Southeast Asia a region that has experienced steep growth in recent years across all sectors, so with this this recent expansion, resellers in Singapore, Indonesia, Malaysia, and Thailand will now have access to Google Cloud Platform (GCP), Google Workspace, and Chrome Enterprise through Ingram Micro Cloud. Needless to say, this latest expansion is a major achievement for the global cloud community as a whole.

Ingram Micro Cloud is incredibly excited to partner with Google Cloud, and in turn provide our reseller partners across Southeast Asia with exclusive access to our Google Cloud Platform, Google Workspace and Chrome Enterprise portfolio. Victor Paradell, executive director, cloud channel sales, emerging markets at Ingram Micro Cloud said

As the cloud becomes more pertinent to everyday life, and inseparable from the way business is conducted, enterprises and small businesses alike are becoming more dependent upon hyperscale platforms to scale. With that, IMCs latest foray into rapidly growing Southeast Asia is focused on spreading Google Cloud offerings into the new territory.

Channel partners in the region will benefit from the strength of this collaboration to capture significant growth opportunities across Googles end-to-end cloud services offerings, Paradell added.

Having first been launched in the US, UK, Canadian and French markets, this latest expansion into Southeast Asia bolsters IMCs global offering by providing partners with new ways to leverage Google Cloud Platform (GCP, Google Workspace and Chrome Enterprise) to grow their businesses.

To ensure the success of this new venture, Google Cloud and Ingram Micro Cloud are currently providing channel partners with the skills and insight they need to manage the needs of end customers and grow their base. This comes as a wider partner growth initiative meant to enable and support new GCP, Google Workspace and Chrome Enterprise practices across Ingram Micro Clouds continuously growing partner network to provide end-to-end engagement for the end customers of systems integrators (SIs) and independent software vendors (ISVs) who comprise the fabric of the vast cloud community.

As a result of this expanded relationship with Google Cloud, Ingram Micro Cloud expects its vast partner network to have new opportunities to meet customer needs, while expanding their IaaS businesses.

The great news is, despite the current economic downturn, the global IaaS market is booming, with Gartner reporting 40.7% growth in worldwide IaaS public cloud services in 2020 and market researchers predicting that the industry will reach $74.63 billion by 2025.

This surge in cloud deployments means that the need for highly-skilled partners to advance our customers digital transformation goals has never been greater, said Ruma Balasubramanian, managing director, Southeast Asia at Google Cloud.

Theres no signs of a slowdown in the cloud computing and IaaS industry, which means channel partners can continue to build their networks, scale, and open up new markets.

This article includes a client of an Espacio portfolio company

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Ingram Micro Cloud and Google partner to give Southeast Asian companies a boost - The Sociable

Cloud computing is about to hit another big milestone – ZDNet

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Image: Shutterstock

Spending on cloud services is about to hit another key tipping point, as business customers spent $18.3 billion on cloud computing and storage infrastructure in the first quarter (Q1) of 2022, up 17.2% year over year.

The total includes budgets on shared and dedicated infrastructure. A major driver of growth was spending on shared cloud infrastructure, which made up $12.5 billion (68%) of the total. That sub-category was also up 15.7% compared to Q1 2021, according to tech analyst IDC.

With growing demand for shared cloud spending in particular, IDC expects spending on shared cloud infrastructure to overtake non-cloud spending in 2022 -- for the first time.

IDC has been monitoring relative spending between cloud and non-cloud infrastructure to measure the change in purchasing of traditional data center compute, storage equipment and IT infrastructure in public clouds from Amazon Web Services (AWS), Microsoft Azure, Google Cloud, and others such as Digital Ocean.

SEE: What is cloud computing? Everything you need to know about the cloud explained

The outlook appears solid for non-cloud IT infrastructure providers, but not as rosy as it is for cloud providers. In this quarter, spending on non-cloud infrastructure still grew year over year to $14.8 billion, up 9.8%. It was the fifth consecutive quarter of growth.

IDC doesn't provide a snapshot of the revenue share between public cloud providers, butanalyst Canalys recently reported AWS received 33% of the $53.5 billion spent globally on cloud infrastructure in Q4 2021, followed by 22% to Azure, and 9% to Google Cloud. Other providers took the remaining 36%.

Spending on cloud infrastructure has accelerated since the pandemic began in March 2020, as businesses accelerated digital transformations. Spending had grown for seven consecutive quarters since Q3 2019, with the exception of a 1.9% year over year decline in Q2 2021. Q2 2020 saw the highest year over year growth of 38.4%. In Q4 2021,spending on cloud infrastructure was up 13.5% in the quarter year on year to $21.1 billion.

Non-cloud spending remains a huge market and is expected to grow for the full year -- albeit at a much slower pace than cloud spending.

For the full year 2022, IDC forecasts cloud infrastructure spending to grow 22% compared to 2021 and reach $90.2 billion. This is the highest annual growth rate since 2018, it notes. Spending on traditional IT infrastructure is expected to grow 1.8% to $60.7 billion.

Greater purchasing of cloud-delivered infrastructure only explains part of the rise in spending. Other contributing factors include inflation and the gradual recovery of the world's snarled supply chains and logistics networks.

At a regional level, IDC expects annual growth in cloud infrastructure spending to grow in the 20% to 25% range in Asia Pacific/Japan, China, the US, and Western Europe. Due to Russia's attack on Ukraine, speeding in Central and Eastern Europe is expected to decline 54% in 2022.

SEE:Why cloud security matters and why you can't ignore it

The analyst expects spending on compute and storage cloud infrastructure to have a compound annual growth rate (CAGR) of 14.5% between 2021 and 2026. It forecasts spending to reach $145.2 billion in 2026, when it will account for 69.7% of total compute and storage infrastructure spend.

It expects spending on non-cloud infrastructure to grow at 1.2% CAGR, reaching $63.1 billion in 2026.

IDC also looks at spends of cloud service providers, digital service providers, communications service providers, and managed service providers, which account for 55.3% of IT infrastructure spending. This group spent $18.3 billion on computing and storage infrastructure in Q1 2022, up 14.5% year on year.

Spending by non-service providers rose 12.9% year over year in Q1 2022, which was the highest growth for 14 quarters.

Spending by service providers on IT infrastructure grew 18.7% over the year to $89.1 billion in 2022.

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Cloud computing is about to hit another big milestone - ZDNet

How to Break Into Cloud Engineering, According to Amazon and Google – Business Insider

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Cloud computing is one of the hottest areas for technical talent looking for well-compensated roles right now.

The cloud market totaled $90.9 billion in 2021, growing 41.4% from $64.3 billion, according to Gartner. To keep up with this pace, cloud giants are willing to pay well for staff.

Cloud engineers can land salaries ranging from $76,690 to $246,000, according to US work-visa data from Amazon Web Services and Google Cloud.

And as more organizations outsource their data-handling to AWS, Google, and Microsoft, business leaders are having to adapt "at a faster pace than they can staff," said Gartner analyst Lydia Leong.

Gartneralso found 64% of tech executives said a lack of available talent had hindered their transition to the cloud, above implementation costs (29%) and security risks (7%).

Cloud engineering is not only lucrative, but versatile. Many traditional engineering roles now involve working on big data, machine learning, and quantum computing, says Kevin Kelly, director of AWS Education Programs.

"The cloud is an enabler for all of those accelerating technologies that are becoming part of a lot of engineering disciplines," Kelly said.

Insider spoke to some of the biggest players in cloud sales to ask their biggest tips for bagging a high-paying job in cloud engineering.

While every engineering role requires a degree of expertise, Google Cloud VP Kripa Krishnan emphasized the variety of roles available.

"You can actually be an engineer working in software, hardware, infrastructure, networking, or machine learning," she said. "But you can also work in product, or user experience, or technical program management, or developer-relationships and then you build your skills as you go along."

Describing herself as "extremely involved" in the recruitment process, Krishnan said her 1,000-strong team recruited from a variety of academic backgrounds not just computer scientists and programmers, but also those with degrees in philosophy and literature.

"I was an arts major," she told Insider. "I spent a lot of time in music and theater, I directed a bunch of plays. I was in a band for a while. I did a bunch of random things."

After going back to school to study for a Masters in computer science, Krishnan soon landed a role as a technical program manager at Google. "If it weren't for my college advisors, who basically forced me into a cab to go to the interview, I don't think I would have tried."

Kevin Kelly, director of education programs at Amazon Web Services, recommended those seeking to pivot into cloud engineering try out some of the firm's online modules, such as AWS Educate, AWS Academy or AWS re/Start.

"I think a lot of employers are looking for people that are curious and willing to learn and understand in a high-tech environment that learning doesn't stop," said Kelly. "It really needs to be a lifelong process."

"Once you realize engineering is something you want to do, clearly there's some amount of skills gap," Krishnan agreed. "But you can do online courses like Coursera or open certs. But learning is the most important part."

"There are a few things I look for," Krishnan added. "Critical thinking and creativity, in particular, and whether someone's able to solve problems holistically.

"I look for persistence. To be cutting-edge and innovative, it's really important to find unconventional ways to get to the right outcomes; and we don't have the option of giving up when the going gets tough."

AWS' Kelly said many traditional engineering roles had been reworked to focus on problems in big data, machine learning, and quantum computing.

"The cloud is an enabler for all of those accelerating technologies that are becoming part of a lot of engineering disciplines," Kelly said.

For those learning on the job, Krishnan agreed: "The first thing to do is ask a lot of incessant questions. The hardest thing to do is get past imposter syndrome. But learning is the key, and being unafraid to ask what seem like basic questions is super, super important."

In fact, Krishnan told Insider the least experienced employees "actually have a more solid foothold later on."

"The more they ask and learn, the better position they are to build better concepts and products."

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How to Break Into Cloud Engineering, According to Amazon and Google - Business Insider

3 Cloud Stocks That Could Help Make You a Fortune – The Motley Fool

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Many high-growth cloud stocks recently crashed as rising interest rates drove investors toward more conservative sectors. As a result, the largest and oldest cloud computing exchange-traded fund, the First Trust Cloud Computing ETF (SKYY 0.48%), lost about a third of its value this year.

It's tempting to avoid cloud stocks entirely and stick with recession-resistant plays in this growling bear market, but investors could be leaving a lot of money on the table by avoiding this high-growth sector. After all, the global cloud market could still grow at a compound annual growth rate (CAGR) of 15.7% between 2022 and 2030, according to Grand View Research.

Image source: Getty Images.

So today, I'll highlight three cloud stocks that could still be worth buying in this challenging market -- ServiceNow (NOW -0.47%), Autodesk (ADSK -0.34%), and Twilio (TWLO 1.26%) -- and explain why they're still well poised to generate impressive long-term returns.

ServiceNow's cloud-based services enable companies to manage their digital workflows. Large companies that operate unstructured work patterns can deploy its services to define, manage, automate, and structure their workflows -- which usually saves a lot of time and money.

ServiceNow served only 602 customers back in 2010, but it ended 2021 with over 7,400 customers -- including about 80% of the Fortune 500. Between 2012 and 2021, its revenue soared from $244 million to $5.9 billion, representing a CAGR of 42.5%. It also turned profitable on a GAAP (generally accepted accounting principles) basis over the past three years.

Last May, ServiceNow predicted it would generate more than $16 billion in annual revenue in 2026 -- which would represent a CAGR of at least 22.1% from 2021. This January, CEO Bill McDermott told investors that ServiceNow could continue to "flourish in any economic environment."

ServiceNow's future looks bright, but its stock price has still declined more than 20% this year amid the broader sell-off in high-growth tech stocks. The stock still isn't cheap at 66 times forward earnings and 13 times this year's sales, but its rosy outlook suggests it deserves that premium valuation.

Autodesk develops AutoCAD, the computer-aided design and drafting application that is widely used by architects, engineers, graphic designers, city planners, and other professionals. It also provides other software for the construction, manufacturing, and media markets.

Over the past decade, Autodesk converted its desktop software into cloud-based services -- which locked in its users with sticky subscriptions. Between fiscal 2012 and 2022 (which ended this January), its revenue grew at a steady CAGR of 7% and doubled from $2.2 billion to $4.4 billion.

Autodesk also turned profitable again in fiscal 2020, and its adjusted earnings per share (EPS) increased 45% in fiscal 2021 and 25% in fiscal 2022. For fiscal 2023, it expects its revenue to rise 13% to 15%, and for its adjusted EPS to increase 27% to 31% -- even after absorbing a $40 million impact (about 3% of its annual revenue) from the suspension of its sales in Russia.

Autodesk's stock price has declined nearly 40% over the past 12 months as the bulls lost their appetite for cloud stocks, but it now looks reasonably valued at 27 times forward earnings and eight times this year's sales.

Twilio's cloud-based platform handles text messages, voice calls, and other communications-based content for mobile apps. Instead of developing those features from scratch -- which can be buggy, time-consuming, and difficult to scale as an app gains more users -- developers can simply outsource them to Twilio with a few lines of code.

Twilio has grown like a weed since its public debut in 2016. Between 2016 and 2021, its annual revenue rose from $277 million to $2.84 billion, representing a CAGR of 59.3%, as its total number of active customer accounts rose from 36,606 to 256,000.

Twilio acquired several companies after its IPO, but it still expects its organic revenue -- which excludes future acquisitions -- to continue growing more than 30% annually over the "next several years."

That's an impressive outlook for a stock that trades at just four times this year's sales.

Twilio's stock lost more than three-quarters of its value over the past 12 months as investors fretted over its declining gross margins and ongoing losses. It had turned profitable on a non-GAAP basis from 2018 to 2020, but it racked up another disappointing net loss in 2021.

But on the bright side, Twilio still expects its gross margins to eventually rise from the low 50s today to more than 60% as economies of scale kick in. Twilio might stay in the penalty box this year, but it could still generate impressive returns in the future if it finally stabilizes its margins and narrows its losses.

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3 Cloud Stocks That Could Help Make You a Fortune - The Motley Fool

For Tech Stocks, the Cloud Is the Silver Lining – Morningstar

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The technology sector was a strong market outperformer at the beginning of the COVID-19 pandemic and performed on par with the market in 2021, but this has evaporated in 2022. Further, mega-cap tech firms (Apple (AAPL), Microsoft (MSFT)) have held up relatively well, masking some of the carnage across smaller tech names.

Although macroeconomic concerns are on the top of investors minds, we still see strong underlying secular tailwinds in technology, such as cloud computing and semiconductor adoption. Technology is now 20% undervalued and 21% undervalued within software, where we often see moaty companies. We would point investors toward high-quality, wide-moat software names, such as Salesforce.com (CRM), ServiceNow (NOW), and Adobe (ADBE), among others.

Techs Performance Has Reverted to Match the Broader Market

- Source: Garner, IDC, Statista, Morningstar. Data as of June 14, 2022.

As of June 27, the Morningstar US Technology Index was down 12.6% on a trailing 12-month basis, a far cry from the 33% TTM performance just six months ago. By comparison, the U.S. equity market is down 11% on a TTM basis. Over the past quarter, tech underperformed the broader market, down 18.6% versus 14.2% for the U.S. equity market.

We See the Most Buying Opportunities in Software

- Source: Garner, IDC, Statista, Morningstar. Data as of June 14, 2022.

As of June 27, the median U.S. technology stock was 20% undervalued compared with 10% undervalued a quarter ago and a sharp reversal from a sector that was 14% overvalued as recently as nine months ago. Software remains the most attractive subsector, 20% undervalued. High-flying growth stocks from 2020 have crashed and many now trade well below our fair value estimates. Meanwhile, more mature, higher-quality software names now provide investors with an attractive margin of safety. The semiconductor sector is 22% undervalued, while hardware is 12% undervalued.

The Cloud Opportunity Is the Most Obvious Secular Theme in Software

- Source: Garner, IDC, Statista, Morningstar. Data as of June 14, 2022.

In software, IT departments have been focused on digital transformation, first from the secular shift to cloud computing and software as a service, followed by the coronavirus pandemic and the critical rush to implement remote working tools. We foresee enterprises using software to modernize all types of business processes, in turn leading to software industry growth at a low-double-digit CAGR.

Additionally, we see an ongoing data boom that not only bodes well for cloud computing, but database management systems, too. Traditional databases like Oracle's still have their place, but emerging beneficiaries will be companies like Snowflake and MongoDB, with premier data-lake, data-warehouse, and data-marketplace offerings.

The Explosion of Data, Including Database Management System Revenue, Should Not Slow Down

- Source: Garner, IDC, Statista, Morningstar. Data as of June 14, 2022.

Top Picks

Salesforce.com (CRM) Star Rating: Economic Moat Rating: Wide Fair Value Estimate: $305 Fair Value Uncertainty: Medium

We believe Salesforce.com represents one of best long-term growth stories in large-cap software because of its ever-expanding portfolio of complementary solutions that allow users to completely embrace their customers, thereby building relationships, strengthening retention, and driving revenue. In our view, Salesforce will benefit further from natural cross-selling among its clouds, upselling more robust features within product lines, pricing actions, international growth, and continued acquisitions, such as the recent deals for Slack and Tableau.

ServiceNow (NOW) Star Rating: Economic Moat Rating: Wide Fair Value Estimate: $700 Fair Value Uncertainty: Medium

ServiceNow excels at executing the land-and-expand strategy, and it continues to use its strength in workflow automation to penetrate existing customers more deeply in IT and more broadly with human resources, customer-service specific, and other back-office products. We expect both tiered offerings and vertical-specific versions to continue to provide a nice tailwind to revenue. We think ServiceNow has become a key partner in digital transformation, as shown in retention statistics, which remains at the elite level. Importantly, we are impressed with ServiceNow's excellent balance between strong and highly visible revenue growth and robust margins.

ASML Holding (ASML) Star Rating: Economic Moat Rating: Wide Fair Value Estimate: $800 Fair Value Uncertainty: Medium

ASML is one of our top semiconductor picks, thanks to the increasing adoption of extreme ultraviolet lithography at large chipmakers such as TSMC (TSM) and Intel (INTC) to support explosive chip demand. Although the firm's first-quarter outlook is negatively affected by supply chain constraints, we think ASML will outgrow the wafer fab equipment industry in 2022. With TSMC, Intel, and Samsung (SSNNF) all vying for process technology leadership, we expect ASML to be a primary beneficiary since it sells tools to all three chipmakers.

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For Tech Stocks, the Cloud Is the Silver Lining - Morningstar

Global Cloud Security Market Value Projected To Reach USD 91.21 Billion By 2027, Registering A CAGR Of 14.9% – Digital Journal

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Global Cloud Security Market is Expected to Reach USD 91.21 Billion By 2027 At a CAGR of 14.9 percent.

Maximize Market ResearchsGlobal Cloud Security Marketreport contains extensive data for the forecast period of 2021 to 2027. Global Cloud Security Market size was valued at USD 34.50 Billion in 2020 and the total revenue is expected to grow at a CAGR of 14.9 percent through 2021 to 2027, reaching nearly USD 91.21 Billion.

Global Cloud Security Market Overview:

Global Cloud Security, also known as cloud computing protection, is a group of practises, checks, tools, and regulations that cooperate to safeguard the systems, data, and infrastructure that are hosted in the cloud. On their local servers, where they assume they should have more control over the data, they believe their data is more trustworthy. It offers all of the features of traditional IT security. For businesses in the industry who are developing a system for cloud technology, it is crucially important.

The market size, share, and volume are all highlighted in the reports regional development status section. The Global Cloud Security Market study provides some of the crucial information that will benefit industry participants in addition to analysts and corporate decision-makers. The research includes information on production, consumption, cost, gross margin, revenue, market share, and market and CAGR-influencing factors.

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Global Cloud Security Market Dynamics:

The MMR report examines market elements from both the demand and supply sides, as well as market characteristics that will affect the market during the forecast period, such as drivers, constraints, opportunities, and future trends.

Market expansion is anticipated to be fuelled by the growing importance of cloud computing among enterprises and the general public. Security maintenance is now a major issue due to the increase in technical threats and internet breaches. As a result, businesses need to take certain precautions before the cyber threat materialises. Current figures show that many businesses have adopted cloud-based security solutions. About 90% of enterprises utilise cloud-based services. Only 12% of the worlds IT industries are aware of the potential effects of the General Data Protection Regulation (GDPR) on cloud computing. Security was the main concern for 66 percent of IT engineers when embracing the cloud computing platform.

Both small and large businesses have low confidence and reliability levels when it comes to cloud migration. Additionally, the lack of security policies adds to this lack of trust. The lack of maturity of cloud services, data breaches, and other challenges are expected to make it tough for the Global Cloud Security market to expand during the forecast period in 2029.

Global Cloud Security Market Regional Insights:

The report offers a brief analysis of the major regions in the market, namely, APAC, Europe, North America, South America, and the Middle East & Africa. The market for Global Cloud Security is predicted to be dominated by North America because of the regions large enterprises and growing database. With the pace of technological advancement and the frequent occurrences of lost important corporate data, the Global Cloud Security market is anticipated to grow favourably. In Europe, the market infrastructure for Global Cloud Security has advanced significantly. By promoting awareness of the numerous cyber-attacks and providing incentives for customers to bring their own devices, the market is still being established in the Asian region.

Global Cloud Security Market Segmentation:

by Organization Size

by Security Type

by Application

by Verticals

Global Cloud Security Market Key Competitors:

In recent years, the market for Global Cloud Security has become more competitive. Few of the big competitors now control the majority of the market in terms of market share. These significant firms, which hold a sizable market share, are concentrating on growing their consumer base internationally. These businesses are using smart joint ventures to boost their market share and profitability.

To Get A Detailed Report Summary Of the Global Cloud Security Market, Click Here:@https://www.maximizemarketresearch.com/market-report/global-cloud-security-market/18082/

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Maximize Market Research undertakes business-to-business and business-to-consumer market research on technological innovations and opportunities in the chemical, health services, pharmaceuticals, electronics and communications, internet of things, food and beverage, aerospace and defense, and other industrial sectors. Because companies all over the world are struggling to deal with the changing market, financial, and technological situations, Maximize Market Research is well-positioned to anticipate the future market size and competitive assessment of industries. At the same time, our industry experts are well placed to identify and forecast product life cycles, technological advances, and industry trends in manufacturing environments.

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Global Cloud Security Market Value Projected To Reach USD 91.21 Billion By 2027, Registering A CAGR Of 14.9% - Digital Journal

Cloud Computing IaaS In Life Science Market Size, Scope and Forecast | Cleardata Networks Dell Inc., Global Net Access (GNAX), Carecloud Corporation,…

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New Jersey, United States The Cloud Computing IaaS In Life ScienceMarket research report aims at providing a quick overview of the overall performance of the industry and significant novel trends. Important insights, as well as findings, latest key drivers, and constraints, are also depicted here. A huge array of quantitative and qualitative techniques is used by market analysts including in-depth interviews, ethnography, customer surveys, and analysis of secondary data. It becomes easy for major players to collect important data regarding key organizations along with insights such as customer behavior, market size, competition, and market need. By referring to this Cloud Computing IaaS In Life Science market study report, it becomes easy for key players to take evidence-based decisions.

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Key Players Mentioned in the Cloud Computing IaaS In Life Science Market Research Report:

Cleardata Networks Dell Inc., Global Net Access (GNAX), Carecloud Corporation, Vmware Carestream Health IBM Corporation, Iron Mountain Athenahealth, Inc. and Oracle Corporation.

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Cloud Computing IaaS In Life ScienceMarket Segmentation:

Cloud Computing IaaS In Life Science Market, By Component

Software Hardware Services

Cloud Computing IaaS In Life Science Market, By Application

Nonclinical Information Systems (NCIS) Clinical Information Systems

Cloud Computing IaaS In Life Science Market, By Deployment Model

Private Cloud Public Cloud Hybrid Cloud

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Cloud Computing IaaS In Life Science Market Report Scope

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Cloud Computing IaaS In Life Science Market Size, Scope and Forecast | Cleardata Networks Dell Inc., Global Net Access (GNAX), Carecloud Corporation,...

The Global Cloud Security Posture Management Market size is expected to reach $9.9 billion by 2028, rising at a market growth of 15.2% CAGR during the…

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New York, July 07, 2022 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Global Cloud Security Posture Management Market Size, Share & Industry Trends Analysis Report By Component, By Cloud Model, By Organization Size, By Vertical, By Regional Outlook and Forecast, 2022 2028" - https://www.reportlinker.com/p06289220/?utm_source=GNW CSPM can apply best practices for cloud security across hybrid, multi-cloud, and container systems universally, and can be used for risk visualization and assessment, compliance monitoring, incident response, and DevOps integration. Numerous different networks might connect and disconnect from a cloud.

Security posture is termed as an organizations total cyber-security strength and its ability to identify, avoid, and respond to the always-changing threat landscape. Security teams should be capable of understanding the attack surface, with efficient, real-time visibility into security flaws and risks, along with the track of the present position and effectiveness of security controls that have been placed, and prevent, detect, and remediate threats in order to have a robust security posture.

Infrastructure as Code (IaC) is a way that brings these new technologies together by allowing infrastructure to be managed and provisioned with the use of machine-readable definition files. This API-driven approach is critical in cloud-first environments because it allows for quick infrastructure changes while also making it easy to program in misconfigurations that make the environment vulnerable.

COVID-19 Impact Analysis

The COVID-19 pandemic resulted in a huge increase in the number of people using internet for employment, education, and leisure. Other crucial areas, such as banking, finance, and insurance, as well as retail and government, have seen large increases in user traffic on their websites and online portals. This expansion has resulted in a significant increase in bandwidth demand, as well as an unexpected increase in the number of cyber-attacks like Ransomware, Distributed Denial of Service (DDoS), and other threats.

Market Growth Factors

Less Visibility Over It Infrastructure And Growth In Configuration Errors

The likelihood of misconfigurations has increased dramatically as cloud adoption has grown. Cloud security and posture management aid monitoring through automation, allowing security workers to resolve issues as soon as it is notified. For example, as per a CheckPoint analysis, the most significant danger in 2020 will be a cloud platform setup problem (68 percent), followed by unauthorized cloud access (58 percent), insecure interfaces (52 percent), and account theft (52 percent) (50 percent). Recently, Capital One, an American financial organization, had a misconfigured threat detection on SQL databases, leaving the cloud vulnerable to vulnerabilities and data breaches.

Migration To The Cloud Allows For Management Of Cloud Security Posture

The cloud sector has gotten a huge boost because of increased agility and faster delivery of new apps and services. Traditional businesses are under enormous competitive pressure as a result of technological improvements. Most firms migrate their old IT infrastructure from on-premises to the cloud in order to become faster, more agile, and more competitive. Migration of traditional data center activities to the cloud can result in higher expenditures, limited capacity of IT team equipment, and a loss of vision, all of which raise the need for cloud security posture management. During the pandemic, there was a dramatic spike in the adoption of cloud services and services across different industrial verticals.

Market Restraining Factors

Scarcity Of Competent Experts To Handle And Secure Cspm Solutions

When it comes to the actual use of CSPM solutions, specialists or personnel must have the necessary technical skills and knowledge to execute, process, analyze, and secure the cloud solutions. While executing and handling operations, organizations hiring security specialists lack the necessary expertise to analyze and discover advanced security holes. According to the Fortinet and Cyber-security Insiders 2021 Application Security Report, a shortage of experienced employees is the main barrier to securing cloud-based infrastructure for 46 percent of the questioned firms. This is a significant issue in the security business.

Component Outlook

On the basis of the Components, the cloud security posture management market is bifurcated into Solutions and Services. The services segment garnered a significant revenue share in the cloud security posture management market in 2021. The CSPM services segment includes deployment, consulting, maintenance, and managed services (as-a-service). Services aims to educate and create expertise, deliver timely solution upgrades, and support clients in combining them with other information technology (IT) solutions. With the growing usage of CSPM systems, the demand for assistance services is projected to rise as well.

Vertical Outlook

On the basis of Vertical, the cloud security posture management market is bifurcated into Government, BFSI, IT and ITeS, Healthcare and life sciences, Retail and Commerce, Education, and others. The BFSI segment acquired the highest revenue share in the cloud security posture management market in 2021. The requirement for CSPM solutions is expanding in response to the banking industrys demand for cloud computing. While the industry was a pioneer in the case of essential financial systems, where security, privacy, and asset management are still top of mind. To accelerate innovation and better serve customers, companies in these industries are rapidly adopting cloud-native technology and DevOps automation.

Organization Size Outlook

By the Organization Size, the cloud security posture management market is divided into large organizations and SMEs. The Small and Medium-sized enterprises segment garnered a substantial revenue share in the cloud security posture management market in 2021. The best feature of CSPM solutions is that it is cost-efficient and most small and medium-sized firm are increasingly adopting cloud security posture management. The cloud security posture management system is easy to be implemented by small and medium enterprises.

Cloud Model Outlook

On the basis of the Cloud Model, the cloud security posture management market is segmented into Infrastructure-as-a-Service, Platform-as-a-Service, and Software-as-a-Service. Infrastructure as a Services segment acquired the highest revenue share in the cloud security posture management market in 2021. Infrastructure as a service (IaaS) is referred as a pay-as-you-go cloud computing service that offers on-demand compute, storage, and networking capabilities. Alongside platform as a service (PaaS), software as a service (SaaS), and serverless, IaaS is one of the four categories of cloud services. The infrastructure as a service (IaaS) layer is the foundation of the cloud computing model. Iaas providers include Linode, DigitalOcean, Amazon Web Services (AWS), Rackspace, Microsoft Azure, Cisco Metapod, and Google Compute Engine (GCE).

Regional Outlook

Region-wise, the cloud security posture management market is analyzed across North America, Europe, Asia-Pacific, and LAMEA.North America emerged as the leading region in the cloud security posture management market with the largest revenue share in 2021 and is expected to continue this trend over the forecast period. Despite its strict rules, the United States provides numerous chances for CSPM providers to serve a diverse spectrum of consumers in a variety of industries. To sustain operational functionality, and business continuity, and prevent misconfiguration, North American organizations have taken multiple steps forward into cloud adoption and are progressively adopting cloud data protection methods like data encryption, DLP, data integrity monitoring, and data threat protection, and CSPM.

The major strategies followed by the market participants are Product Launches. Based on the Analysis presented in the Cardinal matrix; Microsoft Corporation is the major forerunner in the Cloud Security Posture Management Market. Companies such as IBM Corporation, Check Point Software Technologies Ltd. and Palo Alto Networks, Inc. are some of the key innovators in Cloud Security Posture Management Market.

The market research report covers the analysis of key stake holders of the market. Key companies profiled in the report include IBM Corporation, Cisco Systems, Inc., Microsoft Corporation, VMware, Inc., Check Point Software Technologies Ltd., Palo Alto Networks, Inc., Sophos Group PLC, Crowdstrike Holdings, Inc., Atos Group, and Forcepoint LLC.

Recent Strategies deployed in Cloud Security Posture Management Market

Partnerships, Collaborations and Agreements:

Nov-2021: Cisco came into a partnership with JupiterOne, a provider of continuous monitoring to surface problems impacting critical assets and infrastructure, for a new cloud security product named Cisco Secure Cloud Insights. This new cloud security product would use the cyber asset data that JupiterOne analyzes- which comprises vulnerability management, public cloud inventories, compliance reviews, and gaps in security controls. This partnership aimed to deliver enhanced context for the telemetry collected over Ciscos extended detection and response (XDR) SecureX platform. This provides customers with public cloud inventory and insights, security compliance reporting capabilities, and relationship mapping to navigate cloud-based entities and access rights.

Sep-2021: IBM Corporation formed a partnership with Aqua Security, the pure-play cloud-native security provider. This partnership aimed to aid customers in better securing the full lifecycle of Red Hat OpenShift containerized workloads on IBM Power servers, as a significant component of end-to-end application modernization and hybrid cloud adoption. In addition, Aqua Security would assist in the launch of the new IBM Power10-based IBM Power E1080 server.

Sep-2021: Microsoft Corporation came into a partnership with Synack, the premier crowdsourced platform for on-demand security. This partnership aimed to deliver a one-stop shop for Microsoft Azure-based cloud security. By combining the expertise of Microsofts Azure Security Modernization (ASM) solution and Synacks premier crowdsourced platform for on-demand security expertise, government organizations and enterprises would have a scalable solution for cloud security planning, management, and improvement.

Sep-2021: Check Point Software Technologies came into a partnership with Alkira, a cloud networking pioneer. This partnership aimed to deliver Check Point Softwares CloudGuard firewalls in Alkiras Cloud Network infrastructure as-a-Service (CNaaS). The Check Point CloudGuard platform would give cloud-native security with advanced threat prevention for all assets and workloads allowing customers to position the same robust security in the cloud that they are used to with on-premises systems.

Product Launches and Product Expansions:

Apr-2022: Crowdstrike unveiled a cloud-native application protection platform (CNAPP) powered by Falcon Cloud Workload Protection (CWP) offering. This product launch aimed to detect threats targeted at containers, prevent rogue containers from running and discover binaries that have been developed or modified at runtime. CrowdStrike has updated its security orchestration, response (SOAR) platform, and automation, dubbed Falcon Fusion, to allow IT, teams, to automate security remediations on the Amazon Web Services (AWS) cloud.

Apr-2022: Sophos Group expanded its product line of Sophos Cloud Workload Protection, with a new Linux host and container security capabilities. This product expansion aimed to fasten the detection and response of in-progress attacks and security incidents inside Linux operating systems, boost application performance, and enhance security operations. Sophos Cloud Workload Protection already automates and simplifies the prevention and detection of these attacks on Windows systems.

Jun-2021: Palo Alto Networks expanded its product line of Prisme Cloud, with ML-Powered Next-Generation Cloud Security Posture Management Capabilities. This product line expansion aimed to help organizations fasten cloud adoption and remove dangerous cloud blind spots and free security teams from a load of alert fatigue. The new capabilities in Prisma Cloud would enable security teams to do this with greater breadth than earlier and reduce the overall amount of alerts that must be addressed by security teams.

Jun-2021: Check Point Software Technologies expanded its product line Cloud-Native Security Platform, by expanding its capabilities. This product expansion aimed to provide application-first workload protection with Check Point CloudGuard Workload Protection. This fully automated cloud workload security solution would empower security teams with tools to automate security over applications, Application Programming Interfaces (APIs), and microservices from development to runtime through a single interface.

May-2021: Crowdstrike Holding expanded its product line of CrowdStrike Falcon Horizon Cloud Security Posture Management (CSPM), with new features. These new features are supported by the vast, real-time telemetry of the CrowdStrike Security Cloud. This product expansion aimed to provide behavioral detections and attack trends for a unique adversary-emphasized approach to safeguarding the cloud control plane. In addition, these new capabilities comprise continuous threat detection, monitoring, and correlation over the cloud and on-premises environments, giving security teams the ability to cut through the noise of a multi-cloud environment and take the most effective action.

Mar-2021: IBM Corporation expanded its product line of IBM Security Services with new and enhanced services. This product expansion aimed to aid organizations to handle their cloud security strategy, policies, and controls across hybrid cloud environments. These enhanced services would aid companies to implement a consistent security strategy over their hybrid cloud environments assisted by IBM specialists with the expertise to handle native security controls over Amazon Web Services (AWS), IBM Cloud, Google Cloud, and Microsoft Azure, amongst others.

Oct-2020: Palo Alto Networks introduced Prisma Cloud 2.0, consisting of four new cloud security modules. This product launch aimed to safeguard multi- and hybrid-cloud environments and cloud-native applications integrating security over the full DevOps lifecycle. With this inclusion of Cloud Infrastructure Entitlement Management and Cloud Network Security, Prisma Cloud would hold industry-leading offerings in each of the four CNSP areas, making Prisma Cloud 2.0 the only true Cloud-Native Security Platform.

Jan-2020: Atos Group introduced a new Cloud Security Posture Management (CSPM) service supported by Palo Alto Networks Prisma Cloud technology. This product launch aimed to aid customers to resolve the challenges with public cloud adoption by allowing comprehensive visibility, control, and compliance from a single pane of glass. With the use of Atos CSPM service, clients would gain overall 360 visibility of all their resources in the public cloud and can enforce an integrated compliance strategy that unifies seamlessly into their enterprise security model, Atos CSPM service consists of Prisma Cloud by Palo Alto Networks, configuration, consulting and integration services, Operations and response services.

Acquisitions and Mergers:

Oct-2021: Forcepoint entered into an agreement to acquire Bitglass, a leader in Security Service Edge (SSE). This acquisition aimed to bring together best-in-class Secure Web Gateway (SWG), Cloud Access Security Broker (CASB), Zero Trust Network Access (ZTNA), and Cloud Security Posture Management (CSPM), integrated with Data Loss Prevention (DLP) capabilities to allow uniform security policies for accessing the cloud, web, and private data centers handled via a single console. Bitglass provides the industrys only unified cloud-native SSE platform for securing access to and usage of information as organizations transform to the cloud.

Jul-2021: Microsoft Corporation took over CloudKnox Security, a platform created to safeguard resources and identities over multi-cloud and hybrid cloud environments. This acquisition aimed for customers to be able to right-size permissions and enforce least-privilege principles, employing continuous analytics to aid prevent security breaches.

Jun-2021: Cisco Systems took over Kenna Security, the industrys-leading risk-based vulnerability management platform. This acquisition aimed to get rid of complex security posture challenges by working cross-functionally to rapidly automate the prediction, identification, prioritization, and remediation of cybersecurity threats. Adding Ciscos SecureX platforms market-leading detection and response capabilities (XDR) and Kennas vulnerability management platform would provide customers the ability to discover and prioritize an organizations assets with a centralized, contextual view. This acquisition would lead to speed decision making, accelerated and simplified response with orchestration, and reduced friction associated with compliance efforts.

Jun-2020: IBM Corporation took over Spanugo, a cloud security posture management startup. This acquisition aimed to unify Spanugos software into its public cloud to aid match the security and compliance requirements of its customers in regulated industries like banking and health care.

Scope of the Study

Market Segments covered in the Report:

By Component

Solution

Services

By Cloud Model

Infrastructure-as-a-Service (IaaS)

Platform-as-a-Service (PaaS)

Software-as-a-Service (SaaS)

By Organization Size

Large Enterprises

Small & Medium Enterprises

By Vertical

BFSI

Retail & Ecommerce

Government

IT & ITeS

Healthcare & Life Sciences

Education

Others

By Geography

North America

o US

o Canada

o Mexico

o Rest of North America

Europe

o Germany

o UK

o France

o Russia

o Spain

o Italy

o Rest of Europe

Asia Pacific

o China

o Japan

o India

o South Korea

o Singapore

o Malaysia

o Rest of Asia Pacific

LAMEA

o Brazil

o Argentina

o UAE

o Saudi Arabia

o South Africa

o Nigeria

o Rest of LAMEA

Companies Profiled

IBM Corporation

Cisco Systems, Inc.

Microsoft Corporation

VMware, Inc.

See the rest here:

The Global Cloud Security Posture Management Market size is expected to reach $9.9 billion by 2028, rising at a market growth of 15.2% CAGR during the...