Global Machine Learning as a Service (MLaaS) Market: Past, Current, and Future Market Analysis, Trends, and Opportunities, 2016-2026

The new report published by theMarket Research StoreglobalMachine Learning as a Service (MLaaS) marketis slated for a rapid growth in the coming years. The research study projects that the market is expected to grow at a good CAGR of XX% during the forecast period. The valuation for the Machine Learning as a Service (MLaaS) market made by our research analysts is around USD XX Million in 2019 and anticipates USD XX Million by the end of 2026.

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The competitive landscape evaluation of the Machine Learning as a Service (MLaaS) market players includeIBM, Hewlett Packard, Fuzzy.ai, Microsoft, AT&T, Ersatz Labs, Inc., Amazon Web Services, BigML, Hypergiant, Sift Science, Inc., Google, Yottamine Analytics. The information that is profiled for each of the market player will include their primary foundation business model as well as their current business strategy, SWOT analysis, their market share, revenue, pricing, gross margin , and the recent developments.

Machine Learning as a Service (MLaaS) Market Report Insights

Overview of the Machine Learning as a Service (MLaaS) market, its scope, and target audience. In-depth description about the market drivers, restraints, future market opportunities, and challenges. Details about the advanced technologies, including big data & analytics, artificial intelligence, and social media platforms used by the global Machine Learning as a Service (MLaaS) Market Primary legislations that will have a great impact on the global platform. Comprehensive analysis about the key players in the global Machine Learning as a Service (MLaaS) market. Recent developments, mergers and acquisitions, collaborations, R&D projects are mentioned in the Machine Learning as a Service (MLaaS) market report.

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Machine Learning as a Service (MLaaS) Market Segmentation

Global Machine Learning as a Service (MLaaS) market: By Type Analysis

Cloud and Web-based Application Programming Interface (APIs), Software Tools, Others

Global Machine Learning as a Service (MLaaS) market: By Application Analysis

Cloud and Web-based Application Programming Interface (APIs), Software Tools, Others

Global Machine Learning as a Service (MLaaS) market: By Regional Analysis North America Europe Asia Pacific Latin America Middle East and Africa

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In the segmentation part of the report a thorough research of each and every segment is done. For in-depth information some of the major segments have been segregated into sub-segments. In the regional segmentation also our research analysts have not only concentrated on the major regions but have also included the country-wise analysis of the Machine Learning as a Service (MLaaS) market.

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Global Machine Learning as a Service (MLaaS) Market 2020 Coronavirus (COVID-19) Updated Analysis By Product (Cloud and Web-based Application...

According toBlueWeave Consulting, The globalArtificial Intelligence market&Advanced Machinehas reached USD 29.8 Billion in 2019 and projected to reach USD 281.24 Billion by 2026 and anticipated to grow with CAGR of 37.95% during the forecast period from 2020-2026, owing to increasing overall global investment in Artificial Intelligence Technology.

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Artificial Intelligence (AI) is a computer science algorithm and analytics-driven approach to replicate human intelligence in a machine and Machine learning (ML) is an enhanced application of artificial intelligence, which allows software applications to predict the resulted accurately. The development of powerful and affordable cloud computing infrastructure is having a substantial impact on the growth potential of artificial intelligence and advanced machine learning market. In addition, diversifying application areas of the technology, as well as a growing level of customer satisfaction by users of AI & ML services and products is another factor that is currently driving the Artificial Intelligence & Advanced Machine Learning market. Moreover, in the coming years, applications of machine learning in various industry verticals is expected to rise exponentially. Proliferation in data generation is another major driving factor for the AI & Advanced ML market. As natural learning develops, artificial intelligence and advanced machine learning technology are paving the way for effective marketing, content creation, and consumer interactions.

In the organization size segment, large enterprises segment is estimated to have the largest market share and the SMEs segment is estimated to grow at the highest CAGR over the forecast period of 2026. The rapidly developing and highly active SMEs have raised the adoption of artificial intelligence and machine learning solutions globally, as a result of the increasing digitization and raised the cyber risks to critical business information and data. Large enterprises have been heavily adopting artificial intelligence and machine learning to extract the required information from large amounts of data and forecast the outcome of various problems.

Predictive analysis and machine learning and is rapidly used in retail, finance, and healthcare. The trend is estimated to continue as major technology companies are investing resources in the development of AI and ML. Due to the large cost-saving, effort-saving, and the reliable benefits of AI automation, machine learning is anticipated to drive the global artificial intelligence and Advanced machine learning market during the forecast period of 2026.

Digitalization has become a vital driver of artificial intelligence and advanced machine learning market across the region. Digitalization is increasingly propelling everything from hotel bookings, transport to healthcare in many economies around the globe. Digitalization had led to rising in the volume of data generated by business processes. Moreover, business developers or crucial executives are opting for solutions that let them act as data modelers and provide them an adaptive semantic model. With the help of artificial intelligence and Advanced machine learning business users are able to modify dashboards and reports as well as help users filter or develop reports based on their key indicators.

Geographically, the Global Artificial Intelligence & Advanced Machine Learning market is bifurcated into North America, Asia Pacific, Europe, Middle East, Africa & Latin America. The North America is dominating the market due to the developed economies of the US and Canada, there is a high focus on innovations obtained from R&D. North America has rapidly changed, and the most competitive global market in the world. The Asia-pacific region is estimated to be the fastest-growing region in the global AI & Advanced ML market. The rising awareness for business productivity, supplemented with competently designed machine learning solutions offered by vendors present in the Asia-pacific region, has led Asia-pacific to become a highly potential market.

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The major market players in the Artificial Intelligence & Advanced Machine Learning market are ICarbonX, TIBCO Software Inc., SAP SE, Fractal Analytics Inc., Next IT, Iflexion, Icreon, Prisma Labs, AIBrain, Oracle Corporation, Quadratyx, NVIDIA, Inbenta, Numenta, Intel, Domino Data Lab, Inc., Neoteric, UruIT, Waverley Software, and Other Prominent Players are expanding their presence in the market by implementing various innovations and technology.

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Artificial Intelligence & Advanced Machine learning Market is expected to grow at a CAGR of 37.95% from 2020-2026 - Bulletin Line

Even after two decades of corporate experience, I still crave for learning new tools, methods, business and literally anything that excites me.

For this weeks data science column, Analytics India Magazine got in touch with Vidhya Veeraraghavan, Head of Analytics at Standard Chartered Global Business Services. Vidhya has been in the analytics industry for almost two decades, and in this interview, she shares the most valuable insights from her analytics journey.

Vidhya: Through and through, I have been a commerce student. Right from grade 11 till I got my Masters degree in Commerce. Even for my MBA, I had chosen Banking as my specialisation. Another moving force behind me was Florence Nightingale, a renowned statistician and arguably the mother of healthcare analytics. She is a great inspiration, and her story has powerful lessons for passionate analytics professionals like me.

The irony about my data science career is, I used to hate computers in school and today, Im Head Of Analytics for a multinational financial services company. My love for data science started with SQL, which paved the way for SAS, R & Python. That said, my first love was, is, and always will be Microsoft Excel.

My first data science job interview was simple. The position required someone with SAS certification, SQL experience, with Banking background and people with leader qualifications. I fitted the bill perfectly because I made a point to fill in the gaps that I found.

Nothing can match the thrill of your model yielding results with great accuracy.

Vidhya: Having an only formal education is not enough to land in a good job or sustain in the cut-throat environment. Experience matters. One needs hands-on experience to be good at anything. And, experience doesnt always mean corporate experience.

For instance, I have backed up my academics with certifications in SQL, SAS, R, Python, Machine Learning, Deep Learning, Tableau and Power BI. Im also a Certified Scrum Product Owner (CSPO). The latest certification I added was Executive Data Science from John Hopkins University. Currently, Im working on my RPA Program Manager certification.

During the course of my career, I have learnt that not everyone can understand data like a coder does. So, I turned my attention towards visualisation tools, which introduced me to Tableau, which also happens to be my favourite tool although I like the easiness of using Power BI (an extended arm of Microsoft Excel) and the vibrancy of SAS Visual Analytics.

Any project that you can work on, be it Kaggle competitions or an internship that deals with cleaning data for an extensive period of time, all this counts as good experience. It is important to be a continuous learner. I have almost two decades of corporate experience, but I still crave for learning new tools, methods, business, literally anything that excites me. I am learning German language, RPA and the art of being an influential leader.

Vidhya: Fraud Analytics is one area in financial institutions where we have witnessed a constant change. You are always expected to be up in the game. Earlier, the primary tool sets for banks were Microsoft Excel and Microsoft Access, which were used for data churning and pattern recognition. Whereas, SAS, due to its highly secured nature, was preferred by most of the banks. Top vendors have gradually started to include Fraud detection modules to their gamut of offerings. Today, there are many open-source offerings in the market, which are quite user-friendly. My personal favourites are the AI-based modules that can detect fraud using machine learning to recognise patterns with data mining techniques. The advent of machine learning has provided an opportunity for the banks to be more proactive in the situations.

Vidhya: Vast amounts of structured data is available for mining within every banks network. These are both transaction data and static data. Pattern recognition, tracking location-wise transactions for early detection of skimming fraud and setting robust parameters for detecting application frauds are key features. For example, in this pandemic situation, markets are quite volatile and at high risk. Hence financial crime risk metrics are now being closely watched with heightened securities within the Banking industry.

If you take your people along with you on your journey of data science, you can reach greater heights with minimal efforts.

Vidhya: In any organisation, people can be both valuable and challenging. My strategy is to convert this biggest challenge to your treasured asset through inclusion. If you take your people along with you on your journey of data science, you can reach greater heights with minimal efforts. My worst experience would be when I am the only one in the room who understands the need for data analytics and data science, and the rest are resisting the inevitable. To build and drive a data culture around you is the toughest challenge that I have faced so far.

Home From A Commerce Student To Head Of Analytics: Interview With Vidhya Veeraraghavan

Vidhya: Few projects that I have spearheaded include delivery of analytical models forging alliances between business teams, developers and analysts in an AGILE manner to churn out meaningful insights. I have also leveraged Big Data technologies & Business Intelligence tools to identify the financial risk exposure and have applied data as a solution to highlight the client digitisation opportunities, which has resulted in significant increase in digital footprints!

Vidhya: Analytics is a broader area, so while hiring analytics professionals, I look for those who bring multiple skills on board, which include Business Analysis, Business Intelligence, coding, statistics, most importantly passion and learnability.

That said, networking plays a key role too. Unironically, I met my first contact in data science in a networking event. LinkedIn helped me with my first break, and it has been a good companion ever since. Another small trick that I often tell my prodigies compare your current position with the position that you aim for. You can easily identify the gaps, and once you do, it is easy to fill the gaps, both education-wise and experience-wise.

I am a firm believer of continuous learning, and this is much easier nowadays. All you need is a smartphone. Google answers almost every question that we have, including writing code or learning a new shortcut or formula in excel. Beginners can also make good use of Youtube, which has a plethora of lectures from experts in the field.

Personally, I have gained a lot of knowledge through MOOCs like Coursera, Udemy and others. Books for Dummies and Harvard Business Reviews along with certifications from Great Learning institute helped me as well. I also closely follow Analytics India Magazine, AnalyticsVidhya.com, Business Insider (Tech), BernardMarr.com, forbes.com (analytics), KDnuggets.com to keep in close touch with the industry news. I am also active in LinkedIn, which helps me keep my networking alive.

It is important to remember, Data Science is an intellectual and practical activity that includes a systematic study of the structure and behaviour of data. But it is also an art form that requires some creativity and imagination to communicate the data story. Data Science is not all work and no play. So, remember to have fun while learning and experiencing the exciting world of Data Science.

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From A Commerce Student To Head Of Analytics: Interview With Vidhya Veeraraghavan - Analytics India Magazine

The pressureto reduce margins, technical debt, and investments in core systems creates a tremendous incentive for increased automation. The benefits are numerous and obvious: less staffing, reduced errors, smarter decisions, and security at scale. The quest for an autonomous enterprise starts with the need to consider what decisions require intelligent automation versus human judgment.

Vendors from multiple fronts intend to deliver on this promise. Legacy CRM and customer experience providers, cloud vendors, business process management suppliers, robotic process automation providers, process-mining vendors, and IT services firms with software solutions are attempting to compete with pure-play vendors for both mindshare and market dominance in the intelligent automation market, which Constellation Research expects to hit $10.4 billion by 2030.

Almost every marketing leader has sought to intelligently automate processes as part of critical operational efficiency initiatives. From campaign to lead, order to cash, incident to resolution, and concept to market, no department is immune and no business process is exempt. While these efforts to automate often start with the desire to cut costs, they can evolve into something more. The advent of artificial intelligence (AI) components such as natural language processing, machine learning, and neural networks present opportunities to deploy fully autonomous capabilities that have strategic and long-ranging impacts. Seven forces drive the quest for autonomous capabilities in the enterprise:

1. POST-PANDEMIC PRIORITIES: AGILITY AND BUSINESS CONTINUITY

Widespread business disruptions and the growth of disruptive business models have shifted boardroom and organizational priorities. Organizations expect to spend more on agility and business continuity, and they no longer seek to invest more in legacy technologies and systems that do not support those two areas. Key investment themes include self-driving, self-learning, and self-healing systems. While the long-term goal is sentience, the short-term capabilities enable redundancy at scale as well as rapid development, testing, deployment, upgrades, and refreshes.

2. FINANCIAL PERFORMANCE PRESSURES

The ongoing battle to address short-term, quarter-to-quarter profitability and the scarcity of top talent gives companies an incentive to invest in automation to augment the labor force. The good news: Enterprises have the technology to automate business processes at an unimaginable scale. Thus, every organizational leader must determine when to trust the judgment of a machine, when to augment a machine with a human, when to augment a human with a machine, and when to trust human ingenuity. In this autonomous future, machines will deliver services that are continuous, auto-compliant, self-driving, self-healing, self-learning, and self-aware. Access to larger datasets and more engagements to refine algorithms will be needed to ensure precision decisions and ever-higher confidence levels.

3. DECLINING POPULATION DYNAMICS AND RISING LABOR COSTS

Many industrialized countries face declining populations. Japan, for instance, faces a projected population decline of 16 percent, dropping from 127 million in 2014 to 107 million by 2040. Europe is projected to have 0.3 percent to 0.5 percent negative growth by 2040. Furthermore, aging populations, declining birth rates, and minimal immigration create systemic declines that hamper productivity gains, reduce the labor force, and erode any economies of scale. Meanwhile, rising labor costs and regulations drive up labor inflation for both services and manufacturing. Leaders seek ways to drive down labor costs from recruiting, re-skilling and retrainingby replacing with automation.

4. RISK MITIGATION AND COMPLIANCE

Leaders seek to mitigate compliance risk and reduce errors through the automation of manual tasks. With more than 70 percent of employee time focused on manual and repetitive tasks, many seek relief from the mundane. Manual entry and labor for transactional systems lead to higher risk of errors. Todays volume of transactions and the downstream implications of improperly entered data, bad data, and late data create exponential issues in human-led errors that must be addressed. Consequently, every enterprise must automate at an unprecedented scale. One compliance fine or privacy breach caused by human error could lead to hundreds of millions to billions of dollars in losses.

5. ENABLING A FUTURE OF PRECISION DECISIONS

Successful AI projects seek a spectrum of outcomes. Automation and training models will improve with more data and more interactions. The disruptive nature of AI comes from the speed, precision, and capacity for augmenting human workers and delivering on the goal of a more automated enterprise. Seven AI outcomes show the progression from perception to sentience on the spectrum:

Perception describes whats happening now.The first set of outcomes describes surroundings as manually programmed. Perception provides a first-level report of activity.

Notifications tell you what you asked to know.Notifications through alerts, workflows, reminders, and other signals help deliver additional information through manual input and learning.

Suggestions recommend action.Suggestions build on past behaviors and modify over time based on weighted attributes, decision management, and machine learning.

Automation repeats what you always want.Automation enables leverage as machine learning matures over time and tuning.

Prediction informs you about what to expect.Prediction starts to build on deep learning and neural networks to anticipate and test for behaviors.

Prevention helps you avoid bad outcomes.Prevention applies cognitive reckoning to identify potential threats and to augment human judgment.

Situational awareness tells you what you need to know right now.Situational awareness comes close to mimicking human capabilities in decision making.

6. COMBATING DEEPFAKES AND DELIVERING CYBERSECURITY AT SCALE

In this world of relativism and enhanced technologies, humans have more trouble discerning authenticity. The blurred line between reality and fiction creates conditions that can sway public opinion, incite violence or riots, and bilk others of value. The need for authenticity still remains, and those individuals and enterprises that can deliver authenticity will win trust and significant business. AI and automation must quickly identify, notify, respond to, and eradicate deepfakes and prevent them from intruding on existing systems. With an increasing number of systems networked to outside systems, customers can expect the greater attack surface to spawn high volumes of denial-of-service attacks, phishing scams, fake invoices, and usage of stolen identities. Autonomous systems will effectively combat these at scale.

7. PRESERVING AND SHARING INSTITUTIONALKNOWLEDGE

Despite massive efforts to grow and train talent, foster innovation, and create institutional knowledge, regressive factors such as high turnover, agile project methodologies, mergers and acquisitions, and short-term thinking challenge the ability to retain and share institutional knowledge. Without easy approaches, organizations quickly forget, facing a degradation of knowledge with each departure and each organizational restructuring. Autonomous enterprises capture the informal and people-centric institutional knowledge from processes, leading to best practices and nuance in decision making. This enables consistent planning, shared institutional knowledge, and a permanent and living memory.

The future of CX points to a more automated enterprise. The more we automate, the more we can build models to improve next best action. The ultimate goal is to deliver precision decisions. Keep in mind that AI enablement requires a strong data strategy, deep data governance, and mature business process optimization.

KNOW WHEN TO AUTOMATE

Seven factors play a significant role in identifying which AI-driven smart services deliver the greatest opportunities:

1.Repetitiveness.The greater the frequency a process is repeated, the more likely the process should be AI-powered. One-offs and custom processes with minimal repetition are lower-priority candidates for AI.

2.Volume.When the volume of transactions and interactions exceed human capacity, the service should be AI-powered. Volumes within human capacity can remain human-powered.

3.Time to complete.High time-to-market requirements favor AI-powered approaches. Lower time-to-completion requirements can remain human-powered.

4.Nodes of interaction.Simple interaction nodes will lean toward the human-powered option. AI serves best in complex and high-volume nodes of interaction.

5.Complexity.Good candidates for AI-powered uses include complexity beyond human comprehension or, at the other end of the spectrum, simple tasks that can be optimized by AI.

6.Creativity.Today, the cognitive processes required for creativity mostly reside with humans; higher creative powers are less likely to be AI-powered. But with advancements in cognitive learning, one can expect creativity to improve with AI-powered approaches over the next decade.

7.Physical presence.Processes that require a heavy physical presence will most likely require human-powered capabilities. However, processes that put lives in jeopardy serve as great candidates for automated, AI-powered options. In general, low physical presence requirements play well to AI-powered approaches.

R Ray Wang is founder, chairman, and principal analyst of Constellation Research. He is the author of the business strategy and technology blog A Software Insiders Point of View. His latest best-selling book isDisrupting Digital Business, published by Harvard Business Review Press.

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Automation Is the Future of CX - Destination CRM

Businesses that have put their data house in order, or made significant progress to that end find themselves in the enviable position of being able to apply their data assets to transformational technologies like AI and machine learning, and automation. The laggards risk getting left behind.

Even as analytics emerged over the last few years as a core capability for businesses with a data-driven decision making culture, companies often found themselves struggling to get the information they need out of disparate data silos.

Those that have done so, or made significant progress to that end now find themselves in the envious position of being able to apply their data assets to transformational technologies like AI and machine learning, and automation. The laggards risk getting left behind

Whether that is for mass personalisation, asset intelligence, or internet of things implementations, all of these functions require data and integrations that need to form part of the AI and automation roadmap.

According to Darren Cockerells, Head of Solutions Consulting ANZ, Blue Prism, When it comes to being able to harness AI technologies, the ability to manage data is everything. Digital disruption relies on the ability to ingest from, and disseminate to, legacy operations.

Cockerell says there are already many impressive cognitive technologies available today, including those for marketers designed to gather insights to better understand and redefine the customer journeys But every single one of these solutions follows the same paradigm; you have to get data in and then find a way to act upon what the solution delivers.

Getting access to the cognitive service is the easy part since so many exist as cloud-based SaaS applications, says Cockerell. However, he cautions that corralling the data to feed to that service is the difficult part.

The biggest barrier tends to be the volume of disparate legacy systems, spreadsheets and PDF documents across siloed departments. The data required is often voluminous and dispersed, he says.

Impediments

The impediments organisations face getting their data story straight are myriad, says Simon Belousoff, executive director of Beta Evolution, an independent digital, data analytics, and customer experience consultancy, and who was previously Head of Personalisation/ Customer Decisioning (Customer Transformation) at Bupa.

He says organisations often adopt a mindset and approach for data, CX and AI that is based on their legacy approaches to reporting. What they really need is a different and evolved perspective and approach. This mistake often results in the data not being available in a timely way where it needs to be used.

Unlike in previous processes, humans are often not directly involved

Furthermore, he says, Data available for AI is consumed machine-to-machine at scale and needs to be consumable like this.

He also cautions that operational silos are as corrosive as technical ones,

Data is not seen as an enterprise asset, that is usable for the collective benefit of customers and the business. Instead it is seen as a discrete channel or function, or a business asset that is not for sharing with others in the organisation. You need to democratise the data.

According to Belousoff, Internal organisation data benefits from being progressively augmented with many forms of external data to deliver use case and experience outcomes and that this needs to be done in an integrated, timely and governed manner.

Belousoff nominates the CBAs Customer Engagement Engine which is powered by Pega and which saw 200 machine learning models created by Pegas AI based on the CBAs data scientist developed predictive models.

This article was produced for ADMA by the Which-50 Digital Intelligence unity. For the complete version of this story please visit ADMA.

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Are you data assets ready to power transformational technology like AI and automation? - Which-50

Amazon Web Services (AWS), has announced the general availability of Contact Lens, a set of machine learning-driven capabilities for Amazon Connect that provides customer interaction analytics for contact centres.

Amazon Connect is a fully managed cloud contact centre service.

With Contact Lens, contact centre supervisors can discover themes and trends from customer conversations, conduct a full-text search on call transcripts to troubleshoot customer issues, and improve contact centre agents performance with call analytics from within the Amazon Connect console.

Coming late-2020, Contact Lens also provides the ability for supervisors to be alerted to issues during in-progress calls, giving them the ability to intervene earlier when a customer is having a poor experience.

Contact Lens requires no technical expertise and can be activated through Amazon Connect.

It uses machine learning to transcribe calls and automatically indexes call transcripts so they can be searched from the Amazon Connect console.

Machine learning is also used to make it easier for supervisors to search voice interactions based on call content (e.g. customers asking to cancel a subscription or return an item), customer sentiment (e.g. calls that ended with a negative customer sentiment score), and conversation characteristics (e.g. talk speed, long pauses, or customers and agents talking over one another).

By clicking on search results, supervisors can view a contact detail page to see the call transcript, customer and agent sentiment, a visual illustration of conversation characteristics, and use this information to share feedback with their agents to improve customer interactions.

Contact Lens also uses natural language processing to help supervisors uncover new issues (e.g. a price discrepancy between a website and an email promotion) on the contact detail page by visually identifying words and phrases in call transcripts that indicate reasons for customer outreach.

Supervisors can automatically monitor all of their agents interactions for customer experience, regulatory compliance, and adherence to script guidelines by defining custom categories on a new page in Amazon Connect that allows them to organise customer contacts based on words or phrases said by the customer or agent (e.g. a customer mentioning a competitor, membership in a customer loyalty program, certain regulatory disclosures, etc.).

The machine learning capabilities can automatically detect and redact sensitive personally identifiable information (PII) like names, addresses, and social security numbers from call recordings and transcripts to help customers more easily protect customer data.

Later this year, Contact Lens will introduce new features that provide supervisors with real-time assistance by offering a dashboard that shows the sentiment progression of live calls in a contact centre.

This dashboard continuously updates as interactions progress and allows supervisors to look across live calls to spot opportunities to help their customers. Real-time alerting gives supervisors the ability to engage and de-escalate situations earlier.

Contact Lens capabilities are built into Amazon Connect to provide metadata (such as transcriptions, sentiment, and categorisation tags) in customers' Amazon Simple Storage Service (Amazon S3) buckets in a well-defined schema.

Businesses can export this information and use additional tools like Amazon QuickSight or Tableau to do further analysis and combine it with data from other sources.

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AWS Contact Lens for Connect set to arrive in A/NZ - IT Brief Australia

Global Machine Learning as a Service market report provides major statistics on the state of the industry and is a valuable source of guidance and direction for companies and individuals interested in the market.

The research report on Machine Learning as a Service market encompasses analytical data and other industry-linked information to deliver precise and reliable analysis of the market scenario over the forecast timeframe. In addition, the document answers important questions pertaining to the impact of COVID-19 on the industry growth. The driving factors as well the restraints and other market dynamics are also validated in the report. Besides this, the report offers a magnified view of the regional markets and the companies shaping the competitive terrain.

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Addressing the major pointers from the Machine Learning as a Service market study:

A brief overview of the regional analysis of the Machine Learning as a Service market:

Other takeaways from the report which will affect the Machine Learning as a Service market remuneration:

Elaborating the competitive arena of the Machine Learning as a Service market:

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Major Highlights from Table of contents are listed below for quick look up into Machine Learning as a Service Market report

The key questions answered in the report:

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Some of the Major Highlights of TOC covers:

Executive Summary

Manufacturing Cost Structure Analysis

Development and Manufacturing Plants Analysis of Machine Learning as a Service

Key Figures of Major Manufacturers

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Machine Learning as a Service Market: Technological Advancement & Growth Analysis with Forecast to 2026 - My Kids Health

When Deep Blue defeated world chess champion Garry Kasparov in 1997, it may have seemed artificial intelligence had finally arrived. A computer had just taken down one of the top chess players of all time. But it wasnt to be.

Though Deep Blue was meticulously programmed top-to-bottom to play chess, the approach was too labor-intensive, too dependent on clear rules and bounded possibilities to succeed at more complex games, let alone in the real world. The next revolution would take a decade and a half, when vastly more computing power and data revived machine learning, an old idea in artificial intelligence just waiting for the world to catch up.

Today, machine learning dominates, mostly by way of a family of algorithms called deep learning, while symbolic AI, the dominant approach in Deep Blues day, has faded into the background.

Key to deep learnings success is the fact the algorithms basically write themselves. Given some high-level programming and a dataset, they learn from experience. No engineer anticipates every possibility in code. The algorithms just figure it.

Now, Alphabets DeepMind is taking this automation further by developing deep learning algorithms that can handle programming tasks which have been, to date, the sole domain of the worlds top computer scientists (and take them years to write).

In a paper recently published on the pre-print server arXiv, a database for research papers that havent been peer reviewed yet, the DeepMind team described a new deep reinforcement learning algorithm that was able to discover its own value functiona critical programming rule in deep reinforcement learningfrom scratch.

Surprisingly, the algorithm was also effective beyond the simple environments it trained in, going on to play Atari gamesa different, more complicated taskat a level that was, at times, competitive with human-designed algorithms and achieving superhuman levels of play in 14 games.

DeepMind says the approach could accelerate the development of reinforcement learning algorithms and even lead to a shift in focus, where instead of spending years writing the algorithms themselves, researchers work to perfect the environments in which they train.

First, a little background.

Three main deep learning approaches are supervised, unsupervised, and reinforcement learning.

The first two consume huge amounts of data (like images or articles), look for patterns in the data, and use those patterns to inform actions (like identifying an image of a cat). To us, this is a pretty alien way to learn about the world. Not only would it be mind-numbingly dull to review millions of cat images, itd take us years or more to do what these programs do in hours or days. And of course, we can learn what a cat looks like from just a few examples. So why bother?

While supervised and unsupervised deep learning emphasize the machine in machine learning, reinforcement learning is a bit more biological. It actually is the way we learn. Confronted with several possible actions, we predict which will be most rewarding based on experienceweighing the pleasure of eating a chocolate chip cookie against avoiding a cavity and trip to the dentist.

In deep reinforcement learning, algorithms go through a similar process as they take action. In the Atari game Breakout, for instance, a player guides a paddle to bounce a ball at a ceiling of bricks, trying to break as many as possible. When playing Breakout, should an algorithm move the paddle left or right? To decide, it runs a projectionthis is the value functionof which direction will maximize the total points, or rewards, it can earn.

Move by move, game by game, an algorithm combines experience and value function to learn which actions bring greater rewards and improves its play, until eventually, it becomes an uncanny Breakout player.

So, a key to deep reinforcement learning is developing a good value function. And thats difficult. According to the DeepMind team, it takes years of manual research to write the rules guiding algorithmic actionswhich is why automating the process is so alluring. Their new Learned Policy Gradient (LPG) algorithm makes solid progress in that direction.

LPG trained in a number of toy environments. Most of these were gridworldsliterally two-dimensional grids with objects in some squares. The AI moves square to square and earns points or punishments as it encounters objects. The grids vary in size, and the distribution of objects is either set or random. The training environments offer opportunities to learn fundamental lessons for reinforcement learning algorithms.

Only in LPGs case, it had no value function to guide that learning.

Instead, LPG has what DeepMind calls a meta-learner. You might think of this as an algorithm within an algorithm that, by interacting with its environment, discovers both what to predict, thereby forming its version of a value function, and how to learn from it, applying its newly discovered value function to each decision it makes in the future.

Prior work in the area has had some success, but according to DeepMind, LPG is the first algorithm to discover reinforcement learning rules from scratch and to generalize beyond training. The latter was particularly surprising because Atari games are so different from the simple worlds LPG trained inthat is, it had never seen anything like an Atari game.

LPG is still behind advanced human-designed algorithms, the researchers said. But it outperformed a human-designed benchmark in training and even some Atari games, which suggests it isnt strictly worse, just that it specializes in some environments.

This is where theres room for improvement and more research.

The more environments LPG saw, the more it could successfully generalize. Intriguingly, the researchers speculate that with enough well-designed training environments, the approach might yield a general-purpose reinforcement learning algorithm.

At the least, though, they say further automation of algorithm discoverythat is, algorithms learning to learnwill accelerate the field. In the near term, it can help researchers more quickly develop hand-designed algorithms. Further out, as self-discovered algorithms like LPG improve, engineers may shift from manually developing the algorithms themselves to building the environments where they learn.

Deep learning long ago left Deep Blue in the dust at games. Perhaps algorithms learning to learn will be a winning strategy in the real world too.

Image credit: Mike Szczepanski /Unsplash

Original post:
DeepMind's Newest AI Programs Itself to Make All the Right Decisions - Singularity Hub

Market Study Report provides a detailed overview of Machine Learning in Healthcare Industry market with respect to the pivotal drivers influencing the revenue graph of this business sphere. The current trends of Machine Learning in Healthcare Industry market in conjunction with the geographical landscape, demand spectrum, remuneration scale, and growth graph of this vertical have also been included in this report.

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Machine Learning in Healthcare Market: Technological Advancement & Growth Analysis with Forecast to 2025 - Owned

Over the past few decades, medical imaging techniques, such as computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), mammography, ultrasound, and X-ray, have been used for the early detection, diagnosis, and treatment of diseases. In the clinic, medical image interpretation has been performed mostly by human experts such as radiologists and physicians.

However, given wide variations in pathology and the potential fatigue of human experts, researchers and doctors have begun to benefit from the machine learning methods. The process of applying machine learning methods in medical image analysis is called medical image computation. We will introduce our work in medical image synthesis, classification, and segmentation.

Medical image synthesis:

Complementary imaging modalities are always acquired simultaneously to indicate the disease areas, present the various tissue properties, and help to make an accurate and early diagnosis. However, some imaging modalities are unavailable or lacking due to different reasons such as cost, radiation or other limitations. In such cases, medical imaging synthesis is a novel and effective solution.

Although the classic synthesis algorithms has achieved remarkable results, they are confronted with the same fundamental limitation: it is difficult to generate plausible images with significantly diverse structures, because the generator learns to largely ignore the latent vectors (i.e. the noise vectors input) without any prior knowledge in the training process of GANs.

Especially for the generation of brain images that have diverse structural details (e.g. gyri and sulci) between different subjects. To deal with this challenge, our team proposed a novel end-to-end network, called Bidirectional GAN [1], where image contexts and latent vector were effectively used and jointly optimized for the brain MR to PET synthesis. The framework of the proposed Bidirectional GAN is shown in Fig 1.

To be more specific, a bidirectional mapping mechanism between the latent vector and the output image was introduced, while an advanced generator architecture was adopted to optimally extract and generate the intrinsic features of PET images.

Finally, this work devised a composite loss function containing an additional pixel-wise loss and perceptual loss to encourage less blurring and yield visually more realistic results. As an attempt to bridge the gap between network generative capability and real medical images, the proposed method not only focused on synthesizing perceptually realistic images, but also concentrated on reflecting the diverse brain attributes of different subjects.

Medical image segmentation

Medical image segmentation plays an important role in computer-aided diagnosis (CAD) for the detection and diagnosis of diseases. However, traditional segmentation needed to process manually by pathologists and is thus subjective and time-consuming. Therefore, automatic methods for segmentation are in urgent demand to get measurements in the clinical practice.

Fully supervised training requires a large number of manually labeled masks, which is hard to obtain and only experts can provide reliable annotations. To address this issue, we proposed a novel method named Consistent Perception GAN for semi-supervised segmentation task. Firstly, we joined the similarity connection module into the segmentation network to address the challenges of encoder-decoder architectures mentioned above. This module combined skip connection with local and non-local operations, collected multi-scale feature map to capture long-range spatial information.

Moreover, the proposed Assistant network was verified to improve the performance of discriminator using meaningful feature representations. A consistent transformation strategy was developed in the adversarial training which encouraged a consistent prediction of the segmentation network. Semi-supervised loss was designed according to the discriminators judgment, which limited segmentation network to making approximate prediction between labeled and unlabeled images. The proposed model was employed for skin lesion segmentation [4] and stroke lesion segmentation (Fig 3).

Medical image classification

In medical imaging, the accurate diagnosis or assessment of a disease depends on both image acquisition and image interpretation. Medical image classification can be seen as the core of image interpretation. Generative adversarial network has attracted much attention for medical image classification as it is capable of generating samples without explicitly modeling the probability density function.

It is intelligent for the discriminator to incorporate unlabeled data into the training process by utilizing the adversarial loss. Our team proposed a novel Tensorizing GAN with High-order pooling for medical image classification. Fig. 4 shows the framework of the proposed Tensorizing GAN with High-order pooling. More specifically, the proposed model utilized the compatible learning objects of the three-player cooperative game. Instead of vectorizing each layer as conventional GAN, the tensor-train decomposition was applied to all layers in classifier and discriminator, including fully-connected layers and convolutional layers. Besides, in such a tensor-train format, our model could benefit from the structural information of the object. The proposed model was employed to detect Alzheimers disease [2].

Diabetic retinopathy is one of the major causes of blindness. It is of great significance to apply deep-learning techniques for DR recognition. However, deep-learning algorithms often depend on large amounts of labeled data, which is expensive and time-consuming to obtain in the medical imaging area. To address this issue, we proposed a multichannel-based generative adversarial network (MGAN) with semi-supervision to grade DR [3]. By minimizing the dependence on labeled data, the proposed semi-supervised MGAN could identify the inconspicuous lesion features by using high-resolution fundus images without compression.

Future works:

Finally, we will continue to overcome the challenges of medical image computation so as to:

First, most works still adopt traditional computer vision metrics such as Mean Absolute Error (MAE), Peak-Signal-to-Noise Ratio (PSNR), or Structural Similarity Index Measure (SSIM) for evaluating the quality of synthetic images. The validity of these metrics for medical images remains to be explored. And we will explore some other metrics that are relevant to diagnosis.

Second, deep learning methods have often been described as black boxes. We will focus on the researches about the interpretability of medical image computation.

References:

[1] Hu Shengye, Wang Shuqiang et al. Brain MR to PET Synthesis via Bidirectional Generative Adversarial Network. MICCAI 2020

[2] Lei Baiying, Wang Shuqiang et al. Deep and joint learning of longitudinal data for Alzheimers disease prediction.Pattern Recognition102 (2020): 107247.

[3] Wang Shuqiang, Xiangyu Wang et al., Diabetic Retinopathy Diagnosis using Multi-channel Generative Adversarial Network with Semi-supervision, IEEE Transactions on Automation Science and Engineering, DOI: 10.1109/TASE.2020.2981637, 2020

[4] Lei Baiying, Wang Shuqiang et al. Skin Lesion Segmentation via Generative Adversarial Networks with Dual Discriminators.Medical Image Analysis(2020): 101716.

About Prof. Shuqiang Wang

Shuqiang Wang is currently an Associate Professor with Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Science. He received the Ph.D. degree from the City University of Hong Kong in 2012. He was a Research Scientist with Huawei Technologies Noahs Ark Lab. Before joining the SIAT, he was a Post-Doctoral Fellow with The University of Hong Kong. He has published more than 50 papers on Pattern Recognition, Medical Image Analysis, IEEE Trans on SMC, IEEE Trans on ASE, MICCAI et al. He has applied more than 40 patents of which 15 patents are authorized. His current research interests include machine learning, medical image computing, and optimization theory. As for the medical image computing, He mainly focuses on medical image synthesis, medical segmentation and medical classification. As for the machine learning, he mainly focuses on the GAN theory and its application.

Views expressed in this article do not represent the opinion of Synced Review or its editors.

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