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This post was written by Vatsal Ghiya, co-founder and chief operating officer of Shaip.

It is hard to imagine fighting a global pandemic without technologies such as Artificial Intelligence (AI) and Machine Learning (ML). The exponential rise of Covid-19 cases around the world left many health infrastructures paralyzed. However, institutions, governments, and organizations were able to fight back with the help of advanced technologies. Artificial intelligence and machine learning, once seen as a luxury for elevated lifestyles and productivity, have become life-saving agents in combating Covid thanks to their innumerable applications.

With allied technologies like Big Data, IoT, and data science, AI offered tools to frontline caregivers and resources to researchers and drug developers. In this post, we explore how AI and ML have helped battle Covid-19 and how they will continue to assist us in recovering from the chaos.

One of the most practical solutions to curb the spread of the virus is through contact tracing. This allows officials and healthcare providers to identify possible victims and carriers they have come in contact with. With this information, they can isolate Covid-positive patients and deliver healthcare solutions.

By coming up with models like SIR (Susceptible, Infectious, and Recovered), caregivers have been able to seamlessly trace contacts, identify vulnerable regions and clusters, announce containment zones, deploy additional healthcare facilities, and more.

In addition to offering prescriptive solutions, AI has also been used to predict positivity and mortality rates, probable mutations of viruses and their reflections on symptoms, and even arrive at dates and times when the contagion will be at its peak. With data-driven statistics and credible AI modules, officials have been able to proactively take measures like announcing lockdowns and shelter in place protocols, procuring vaccines, oxygen cylinders, PPE kits, testing apparatus, and more. This has been of immense help in developing nations with higher population density to stop the spread of the virus, or at least curb the intensity.

The circulation of fake news concerning the virus has been a significant challenge.With social media devoid of supervision or any form of moderation, many people (anonymously) took to social media platforms and instant messengers to circulate false information and conspiracy theories.

From posts that claimed how to cure Covid through home remedies to theories about last Junes Great Reset meeting of the World Economic Forum, thousands of unfounded messages and posts have been going viral. This has been increasing anxiety levels and paranoia among a world population that has already faced a high level of stress. However, through moderations and screening, AI has been doing an incredible job at preventing conspiracy theories and fake information from making the rounds.

Healthcare centers and institutions have been overburdened like never before. For more than a year, many frontline workers including doctors, nurses, and paramedics have been overworked beyond their capacity. With every incoming patient requiring immediate attention, it becomes nearly impossible to maintain sufficient focus to treat everyone.

Thankfully, AI systems have come to the rescue with precise diagnostic chatbots. Through tech concepts such as Natural Language Processing (NLP), an organization called Paginemediche rolled out a chatbot that offered a highly accurate diagnosis of Covid-19 through data fed to it by users.

Based on responses to questions, the chatbot retrieved and offered guidelines, diagnosis, and solutions from the most credible resources and suggested if a patient needed to be isolated, seek medical attention. or understand that their infection is a common flu and not Covid-19. This has slowed the flow of patients to hospitals and healthcare centers to a significant extent.

Vaccines typically are developed through extensive, time-consuming rounds of clinical trials. However, with AI and ML, Covid vaccine development moved forward at lightning speeds compared to previous viral outbreaks. Through pattern recognition and simulation, researchers have been able to come up with the most effective formulas of medications to help the body develop antigens and build immunity against the virus.

Before the AI models were able to provide accurate results for combating Covid, they went through extensive testing. Covid datasets from multiple resources have all assisted solution providers and development companies to launch reliable Covid-related services. For a healthcare-based AI solution to be precise, healthcare datasets that are fed to it should be airtight.

Also, despite offering such revolutionary apps and solutions, AI models for battling Covd are not universally applicable. Every region of the world is fighting its own version of a mutated virus and a population behavior and immune system specific to that particular geographic location. Thats why there is an inherent need for more AI-driven healthcare solutions to penetrate deeper levels of specific world populations.

Any AI or MLcompany looking to develop a solution and contribute to the fight against the virus should be working with highly accurate medical datasets to ensure optimized results. This is the only they you can offer meaningful services or solutions to society right now. The functionality of your solution is crucial. Thats why we recommend you source your healthcare datasets from the most credible avenues in the market, so you have a fully functional solution to roll out and help those in need.

As co-founder and chief operating officer of Shaip, Vatsal Ghiya has 20-plus years of experience in healthcare software and services. Ghiya also co-founded ezDI, a cloud-based software solution company that provides a Natural Language Processing (NLP) engine and a medical knowledge base with products including ezCAC and ezCDI.

Read the rest here:
How AI and machine learning help fight the COVID-19 battle - VentureBeat

As enterprise deployments of machine learning continue at a strong pace, including in mission-critical environments such as in contact centers, for fraud detection and in regulated sectors like healthcare and finance for example, they are doing so against a backdrop of rising and evermore ferocious cyberattacks.

Take, for example, the SolarWinds hack in December 2020, arguably one of the largest on record, or the recent exploits that hit Exchange servers and affected tens of thousands of customers. Alongside such attacks, we've seen new impetus behind the regulation of artificial intelligence (AI), with the world's first regulatory framework for the technology arriving in April 2021. The EU's landmark proposals build on GDPR legislation, carrying heavy penalties for enterprises that fail to consider the risks and ensure that trust goes hand in hand with success in AI.

Altogether, a climate is emerging in which the significance of securing machine learning can no longer be ignored. Although this is a burgeoning field with much more innovation to come, the market is already starting to take the threat seriously.

Our research surveys reveal a steep change in deployments of machine learning during the pandemic, with more than 80 percent of enterprises saying they are trialing the technology or have put it into production, up from just over half a year ago.

But the topic of securing those systems has received little fanfare by comparison, even though research into the security of machine learning models goes back to the early 2000s.

We've seen several high-profile incidents that highlight the risks stemming from greater use of the technology. In 2020, a misconfigured server at Clearview AI, the controversial facial recognition start-up, leaked the company's internal files, apps and source code. In 2019, hackers were able to trick the Autopilot system of a Tesla Model S by using adversarial approaches involving sticky notes. Both pale in comparison to more dangerous scenarios, including the autonomous car that killed a pedestrian in 2018 and a facial recognition system that caused the wrongful arrest of an innocent person in 2019.

The security community is becoming more alert to the dangers of real-world AI. The CERT Coordination Center, which tracks security vulnerabilities globally, published its first note on machine learning risks in late 2019, and in December 2020, The Partnership on AI introduced its AI Incident Database, the first to catalog events in which AI has caused "safety, fairness, or other real-world problems".

The challenges that organizations are facing with machine learning are also shifting in this direction.

Several years ago, problems with preparing data, gaining skills and applying AI to specific business problems were the dominant headaches, but new topics are now coming to the fore. Among them are governance, auditability, compliance and above all, security.

According to CCS Insight's latest survey of senior IT leaders, security is now the biggest hurdle companies face with AI, cited by over 30 percent of respondents. Many companies struggle with the most rudimentary areas of security at the moment, but machine learning is a new frontier, particularly as business leaders start to think more about the risks that arise as the technology is embedded into more business operations.

Missing until recently are tools that help customers improve the security of their machine learning systems. A recent Microsoft survey, for example, found that 90 percent of businesses said they lack tools to secure their AI systems and that security pros were looking for specific guidance in the field.

Responding to this need, the market is now stepping up. In October 2020, non-profit organization MITRE, in collaboration with 12 firms including Microsoft, Airbus, Bosch, IBM and Nvidia, released an Adversarial ML Threat Matrix, an industry-focused open framework to help security analysts detect and respond to threats against machine learning systems.

Additionally, in April 2021, Algorithmia, a supplier of an enterprise machine learning operations (MLOps) platform that specializes in the governance and security of the machine learning life cycle, released a host of new security features focused on the integration of machine learning into the core IT security environment. They include support for proxies, encryption, hardened images, API security and auditing and logging. The release is an important step, highlighting my view that security will become intrinsic to the development, deployment and use of machine learning applications.

Finally, just last week, Microsoft released Counterfit, an open-source automation tool for security testing AI systems. Counterfit helps organizations conduct AI security risk assessments to ensure that algorithms used in businesses are robust, reliable and trustworthy. The tool enables pen testing of AI systems, vulnerability scanning and logging to record attacks against a target model.

These are early but important first steps that indicate the market is starting to take security threats to AI seriously. I encourage machine learning engineers and security professionals to get going begin to familiarize yourselves with these tools and the kinds of threats your AI systems could face in the not-so-distant future.

As machine learning becomes part of standard software development and core IT and business operations in the future, vulnerabilities and new methods of attack are inevitable. The immature and open nature of machine learning makes it particularly susceptible to hacking and that's why I predicted last year that we would see security become the top priority for enterprises' investment in machine learning by 2022.

A new category of specialism will emerge devoted to AI security and posture management. It will include core security areas applied to machine learning, like vulnerability assessments, pen testing, auditing and compliance and ongoing threat monitoring. In future, it will track emerging security vectors such as data poisoning, model inversions and adversarial attacks. Innovations like homomorphic encryption, confidential machine learning and privacy protection solutions such as federated learning and differential privacy will all help enterprises navigate the critical intersection of innovation and trust.

Above all, it's great to see the industry beginning to tackle this imminent problem now. Matilda Rhode, Senior Cybersecurity Researcher at Airbus, perhaps captures this best when she states, "AI is increasingly used in industry; it is vital to look ahead to securing this technology, particularly to understand where feature space attacks can be realized in the problem space. The release of open-source tools for security practitioners to evaluate the security of AI systems is both welcome and a clear indication that the industry is taking this problem seriously".

I look forward to tracking how enterprises progress in this critical field in the months ahead.

Nick McQuire, Chief of Enterprise Research, CCS Insight

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Hardening AI: Is machine learning the next infosec imperative? - ITProPortal

But theres another crucial observation here. Intelligence was never an endpoint for evolution, something to aim for. Instead, it emerged in many different forms from countless tiny solutions to challenges that allowed living things to survive and take on future challenges. Intelligence is the current high point in an ongoing and open-ended process. In this sense, evolution is quite different from algorithms the way people typically think of themas means to an end.

Its this open-endedness, glimpsed in the apparently aimless sequence of challenges generated by POET, that Clune and others believe could lead to new kinds of AI. For decades AI researchers have tried to build algorithms to mimic human intelligence, but the real breakthrough may come from building algorithms that try to mimic the open-ended problem-solving of evolutionand sitting back to watch what emerges.

Researchers are already using machine learning on itself, training it to find solutions to some of the fields hardest problems, such as how to make machines that can learn more than one task at a time or cope with situations they have not encountered before. Some now think that taking this approach and running with it might be the best path to artificial general intelligence.We could start an algorithm that initially does not have much intelligence inside it, and watch it bootstrap itself all the way up potentially to AGI, Clune says.

The truth is that for now, AGI remains a fantasy. But thats largely because nobody knows how to makeit.Advances in AI are piecemeal and carried out by humans, with progress typically involving tweaks to existing techniques or algorithms, yielding incremental leaps in performance or accuracy. Clune characterizes these efforts as attempts to discover the building blocks for artificial intelligence without knowing what youre looking for or how many blocks youll need. And thats just the start. At some point, we have to take on the Herculean task of putting them all together, he says.

Asking AI to find andassemble those building blocks for usis a paradigm shift. Its saying we want to create an intelligent machine, but we dont care what it might look likejust give us whatever works.

Even if AGI is never achieved, the self-teaching approach may still change what sorts of AI are created. The world needsmore than a very good Go player, says Clune. For him, creating a supersmart machine means building a system that invents its own challenges, solves them, and then invents new ones. POET is a tiny glimpse of this in action. Clune imagines a machine that teaches a bot to walk, then to play hopscotch, then maybe to play Go. Then maybe it learns math puzzles and starts inventing its own challenges, he says. The system continuously innovates, and the skys the limit in terms of where it might go.

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AI is learning how to create itself - MIT Technology Review

IT equipment consists of products such as Personal computers (PCs), servers, monitors, storage devices etc. Software comprises of computer programs, firmware and applications. The IT & business services segment is further classified into consulting, custom solutions development, outsourcing services etc. The telecommunication equipment segment consists of telecom equipments such as switches, routers etc. The carrier services segment comprises of operations related revenue spent by telecom service provider on acquiring telecom capacity, primarily from overseas carrier.

How Important Is Machine Learning as a Service (MLaaS) ?

Market Dynamics

In 20th century data is considered as new oil. Due to this many technology companies are heavily investing in data. These data may be structured and unstructured forms. It has become extremely crucial for these organizations to get a better insight into their data, in order to enhance efficiency and competitiveness. Moreover, many organizations are increasingly adopting machine learning as a service to analyze both structured and unstructured data for future predictions and also use it for further marketing purposes.

The research is derived through primary and secondary statistics sources and it comprises both qualitative and quantitative detailing.

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Some of the key players profiled in the study areH2O.ai, Google Inc., Predictron Labs Ltd, IBM Corporation, Ersatz Labs Inc., Microsoft Corporation, Yottamine Analytics, Amazon Web Services Inc., FICO, and BigML Inc.

Market Trends

Advent of new intelligent application is expected to be major trendMachine learning capabilities are expected to be integrated into more platforms and software in the years to come, enabling organizations to take advantage of them. A number of companies are focused on becoming a data company irrespective of what an organization does. Previously, organizations have been dependent on structured data to make appropriate decisions or estimate future outcomes. However, upsurge of big data and machine learning capabilities has allowed analysis of unstructured data to make more informed decisions. Moreover, rapid speed of data generation and the availability of a huge amount of computing power are expected to facilitate advent of more and more applications that generate real-time predictions and get better constantly over time.

Machine Learning as a Service (MLaaS) Market Taxonomy:

Global Machine Learning as a Service (MLaaS) Market, By Deployment:

Global Machine Learning as a Service (MLaaS) Market, By End-use Application:

Global Machine Learning as a Service (MLaaS) Market, By Region:

Frequently Asked Questions (FAQ) :

Is Machine Learning as a Service (MLaaS) Market Booming In Near Future?

Yes, The global Machine Learning as a Service (MLaaS) market is estimated to account for US$ 38,063.0 million by 2027

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Yes, You can addSpecific Companyupto 3 Companies.

Companies Covered as part of this study include: H2O.ai, Google Inc., Predictron Labs Ltd, IBM Corporation, Ersatz Labs Inc., Microsoft Corporation, Yottamine Analytics, Amazon Web Services Inc., FICO, and BigML Inc.,

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[PDF] Machine Learning as a Service (MLaaS) Market : Some Ridiculously Simple Ways To Improve. The Courier - The Courier

This Has Brought Along Several Changes In This Report Also Covers The Impact Of Covid-19 On The Global Market

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Chapter 1:Overview, Product Overview, Market Segmentation, Market Overview of Regions, Market Dynamics, Limitations, Opportunities, and Industry News and Policies.Chapter 2:Machine Learning Data Catalog Software Industry Chain Analysis, Upstream Raw Material Suppliers, Major Players, Production Process Analysis, Cost Analysis, Market Channels, and Major Downstream Buyers.Chapter 3: Value Analysis, Production, Growth Rate, and Price Analysis by Type.Chapter 4: Downstream Characteristics, Consumption, and Market Share by Application ofMachine Learning Data Catalog Software.Chapter 5: Production Volume, Price, Gross Margin, and Revenue ($) ofMachine Learning Data Catalog Software by Regions.Chapter 6:Machine Learning Data Catalog Software Production, Consumption, Export, and Import by Regions.Chapter 7:Status and SWOT Analysis by Regions.Chapter 8: Competitive Landscape, Product Introduction, Company Profiles, Market Distribution Status by Players ofMachine Learning Data Catalog Software.Chapter 9:Analysis and Forecast by Type and Application.Chapter 10: Analysis and Forecast by Regions.Chapter 11: Characteristics, Key Factors, New Entrants SWOT Analysis, Investment Feasibility Analysis.Chapter 12: Conclusion of the Whole Report.Continue

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Machine Learning Data Catalog Software Market is Anticipated to Rise at a Considerable Growth Rate During 2021-2027 | IBM, Alation, Oracle, Cloudera,...