(Image sourced via H20.ai)

Today, the enterprise faces not just the immediate, overwhelming issue of COVID-19, but multiple challenges. If you're a bank, every day you need better answers to mortgage lending, credit risk scores, fraud/fraud detection, anti-money laundering; if a retailer, you're always having to think about how you'll find your next best customer, what your next best offer is, how to attract new customers? How do you understand the patterns of your customers, and so on.

The latest tool in the enterprise armory to do this is supposed to be Machine Learning (ML), which is really what 99.9% of what's meant when we say "Artificial Intelligence" right now. The promise is that Machine Learning is an additional set of capabilities that should give businesses in every industry the ability to garner better intelligence on trends with their data in their possession.

The problem: doing ML right is hard. You need deep mathematical capability, as well as lots of data to work over-and not every company has a bank of trained data scientists ready to be unleashed on a problem. This is where H20.ai, a leading ML company, is trying to make a play by a Freemium model of ML access, effectively making it very easy for "any" company to get up to speed quickly with the approach. Simply put, it's a maths/stats package that gives you a quick on-ramp to do "automated" Machine Learning.

That means that even if you haven't heard of it, you may already be using it somewhere in your organisation; H2O is used by 20,000 separate entities and by hundreds of thousands of individual data scientists. Analysts think its Open Source route to market is valid, with Gartner telling clients it has the "Strongest Completeness of Vision" in its 2020 Data Science and Machine Learning Magic Quadrant.

H2O has impressive penetration in financial services/insurance, healthcare, telco, retail, pharmaceutical and marketing. H2O-based ML is being used in managing claims, detecting fraud, improving clinical workflows and predicting hospital acquired infections. On the paid-for version, commercial customers of include brands like Wells Fargo, Capital One, Kaiser Permanente and Nationwide Insurance, do similar things; we're talking sifting large datasets to spot anomalies and patterns to do credit scoring and investing better, essentially, as well as supply chain optimisation and, right now, a lot of COVID-19 response planning.

Getting to 20,0000 user organisations in only eight years since you started offering payroll is progress that's attracted the interest of VCs, with the company successfully raising north of $72m last year. The company claims that it's really the main play in automated Open Source Machine Learning platforms, but in the corporate space there are two main competitors Dataiku and DataRobot.

What's particularly interesting about H20 is that its users are investors, according to its CEO, Sri Ambati, who told us many of the financial firms who use the company's offering are also "very strong believers in what we do" in more tangible ways (including Goldman Sachs). As Ambati told diginomica,

What we're trying to do is democratice AI and make sure very high-grade Machine Learning and the best mathematical algorithms are there for you to build your own data science capability from scratch. 250,000 such data scientists use us every day, already.

Fine, but quite a few CIOs and CEOS remain sceptical about the "AI" bubble, which surely has to burst (again) soon. For Ambati:

There's a lot of AI hype, yes. But some sort of intelligence is super-important for any business leader trying to react to the crises that we seem to be experiencing one after another, from Brexit to COVID; they used to be like 9/11, and be once a decade, now they're every year.

We can help you spot the patterns before they become widely visible. But you still have to have the courage to act on that intelligence and take the decisive step, of course. But there are always three people that need to come together to make that happen: AI is a team sport.

The CIO can put AI in the enterprise and ensure the software fits within the specifications of the enterprise, can ingest the data, and the team has the data ready to go-but you need a data science person or a savvy business analyst to actually run it. All those people have to all be in agreement that this is the way forward.

The obvious place where such decisive steps need to be taken will be getting set for Recovery, post-COVID-19, of course. This is where H20 believes Machine Learning will really come into its own:

The big thing right now in retail is distribution, right? So as stores come back online, they will need to determine their supply chain, their inventory, but even so they're trying to figure out customer patterns. AI can detect those patterns better than humans. It can help you by saying Serve this up to Sri,' for example, "or this next offer to Derek because he's likely to take that offer and buy something.'

So no, I don't think AI will go out of fashion. In fact, now more so than ever, companies are calling us because of Coronavirus and the impact on their business and how we can help them. What we see as the next logical step of a digital transformation is an AI transformation, in fact.

Ambati's clearly all about the quality of the work, and less so about the big IPO cash-out. After all, this is a guy who's teaching his young daughters Python at home on Saturday mornings and explaining logarithmic scales to them so they understand what's going on with Coronavirus; he's still a programmer at heart, and one still in love, as they say at the Stanford d.School, with "the problem and not the solution". And as he says, the people who come to work for him seem to be cut from the same cloth, too; he claims the "world's best physicists, fastest compiler writers and best data scientists" are rocking up at 2307 Leghorn St, and we have no reason to doubt him.

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H20.ai CEO on using intelligence and teamwork to respond to a crisis - Diginomica

ZURICH--(BUSINESS WIRE)--The Multi-Scale Robotics Lab (MSRL) at ETH Zurich and Ninety One, Inc have partnered to advance Precision Medicine and Surgical Robotics through advanced Artificial Intelligence and Machine Learning. Ninety One has five priority areas that will be core to our near and long-term growth and that will define the future of Digital Health; Personalized Patient Care, Precision Diagnostics, Robotic Surgery, Image Guided Therapy, and Connected Care Delivery. We are proactively teaming up centers of innovation globally to identify ways to improve patient outcomes, quality of care delivery, and cost productivity, all centered around the Quadruple Aim in medicine. We are delighted to have the opportunity to work with Prof. Bradley Nelson, Christophe Chautems and their medical robotics team at ETH Zurich. said Bleron Baraliu CEO Ninety One, Inc.

The combination of the remote magnetic navigation systems designed at MSRL with machine learning algorithms will open new opportunities to improve the outcome of multiple medical procedures, said Christophe Chautems, Group Leader Medical Robotics at ETH Zurich.

About ETH Zurich

The Multi-Scale Robotics Lab (MSRL) at ETH Zurich pursues a dynamic research program that maintains a strong robotics research focus on several emerging areas of science and technology. A major component of the MSRL research leverages advanced robotics for creating minimally invasive devices for medical application. These devices are controlled with a Magnetic Navigation System that generates a magnetic field in the 3D space. Such systems are used to generate magnetic torques and forces on permanent magnets, or soft magnetic materials embedded on tethered robots such as catheters, or untethered microrobots.

For more information visit https://msrl.ethz.ch/

About Ninety One

Ninety One, a privately-held data science and software technology company -- with their newly-released software platform -- has redefined the model for CIED Remote Monitoring. Ninety One couples latest mathematical advances in data science with state-of-the-art technologies to digitize, analyze, and prioritize data from implantable cardiac devices, wearables, and beyond. Ninety One is focusing on clinical advancement in predictive analytics and Precision Medicine, and has established key, exclusive partnerships with leading research and healthcare institutions in the United States, Europe, and Asia.

For more information visit https://www.91.life

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Ninety One, Inc. Partners with the Multi-Scale Robotics Lab at ETH Zurich to Advance Robotic Surgery Through Machine Learning and Artificial...

We have had a blockchain winter as the hype around the technology moves towards a reality and the same will happen with artificial intelligence (AI).

Thats according to Dr Karol Przystalski, CTO at IT consulting and software development provider Codete. Przystalski founded Codete having had a significant research background in AI, with previous employers including Sabre and IBM and a PhD exploring skin cancer pattern recognition using neural networks.

Yet what effect will the Covid-19 pandemic have on this change? Speaking with AI News, Przystalski argues much like Dorian Selz, CEO of Squirro, in a piece published earlier this week that while AI isnt quite there to predict or solve the current pandemic, the future can look bright.

AI News: Hi Karol. Tell us about your career to date and your current role and responsibilities as the CTO of Codete?

Dr Karol Przystalski: The experience from the previous companies I worked at and the AI background that I had from my PhD work allowed me to get Codete off the ground. At the beginning, not every potential client could see the advantages of machine learning, but it has changed in the last couple of years. Weve started to implement more and more machine learning-based solutions.

Currently, my responsibilities as the CTO are not focused solely on development, as we have already grown to 160 engineers. Even though I still devote some of my attention to research and development, most of my work right now is centred on mentoring and training in the areas of artificial intelligence and big data.

AI: Tell us about the big data and data science services Codete provides and how your company aims to differ from the competitors?

KP: We offer a number of services related to big data and data science: consulting, auditing, training, and software development support. Based on our extensive experience in machine learning solutions, we provide advice to our clients. We audit already implemented solutions, as well as whole processes of product development. We also have a workshop for managers on how not to fail with a machine learning project.

All the materials are based on our own case studies. As a technological partner, we focus on the quality of the applications that we deliver, and we always aim at full transparency in relationships with our clients.

AI: How difficult is it, in your opinion, for companies to gather data science expertise? Is there a shortage of skills and a gap in this area?

KP: In the past, to become a data scientist you had to have a mathematical background or, even better, a PhD in this field. We now know its not that hard to implement machine learning solutions, and almost every software developer can become a data scientist.

There are plenty of workshops, lectures, and many other materials dedicated to software developers who want to understand machine learning methods. Usually, the journey starts with a few proof of concepts and, in the next build, production solutions. It usually takes a couple of months at the very minimum to become a solid junior level data scientist, even for experienced software engineers. Codete is well-known in the machine learning communities at several universities, and thats why we can easily extend our team with experienced ML engineers.

AI: What example can you provide of a client Codete has worked with throughout their journey, from research and development to choosing a solution for implementation?

KP: We dont implement all of the projects that clients bring to us. In the first stage, we distinguish between projects that are buzzword-driven and the real-world ones.

One time, a client came to us with an idea for an NLP project for their business. After some research, it turned out that ML was not the best choice for the project we recommended a simpler, cheaper solution that was more suitable in their case.

We are transparent with our clients, even if it takes providing them with constructive criticism on the solution they want to build. Most AI projects start with a PoC, and if it works well, the project goes through the next stages to a full production solution. In our AI projects, we follow the fail fast approach to prevent our clients from potential over-investing.

AI: Which industries do you think will have the most potential for machine learning and AI and why?

KP: In the Covid-19 times, for sure the health, med, and pharma industries will grow and use AI more often. We will see more use cases applied in telemedicine and medical diagnosis. For sure, the pharma industry and the development of drugs might be supported by AI. We can see how fast the vaccine for Covid-19 is being developed. In the future, the process of finding a valid vaccine can be supported by AI.

But it is not only health-related industries which will use AI more often. I think that almost every industry will invest more in digitalisation, like process automation where ML can be applied. First, we will see an increasing interest in AI in the industries that were not affected by the virus so much, but in the long run even the hospitality and travel industry, as well as many governments, will introduce AI-based solutions to prevent future lockdown.

AI: What is the greatest benefit of AI in business in your opinion and what is the biggest fear?

KP: There are plenty of ways machine learning can be applied in many industries. There is a machine learning and artificial intelligence hype going on now, and many managers become aware of the benefits that machine learning can bring to their companies. On the other hand, many can take AI for a solution for almost everything but thats how buzzword-driven projects are born, not real-world use cases.

This hype may end similarly to other tech hypes that we have witnessed before, when a buzzword was popular, but eventually only a limited number of companies applied the technology. Blockchain is a good example many companies have tried using it, for almost everything, and in many cases the technology didnt really prove useful, sometimes even causing new problems.

Blockchain is now being used with success in several industries. Just the same, we can have an AI winter again, if we dont distinguish between the hype and the true value behind an AI solution.

Photo byAaron BurdenonUnsplash

Interested in hearing industry leaders discuss subjects like this and their use cases?Attend the co-locatedAI & Big Data Expoevents with upcoming shows in Silicon Valley, London, and Amsterdam to learn more. Co-located with theIoT Tech Expo,Blockchain Expo, andCyber Security & Cloud Expo.

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Beware the AI winter - but can Covid-19 alter this process? - AI News

CARY SAS wants to help save the worlds No.1 food crop pollinator the honey bee. And its doing so right in the Triangles backyard.

To coincide with World Bee Day, the Cary-base software analytics firm today confirmed it is working on three separate projectswhere technology is monitoring, tracking and improving pollinator populations around the globe.

They include observing real-time conditions of beehives using an acoustic streaming system; working with Appalachian State University on the World Bee Count to visualize world bee population data; and decoding bee communication to maximize their food access.

By applying advanced analytics and artificial intelligence to beehive health, we have a better shot as a society to secure this critically important part of our ecosystem and, ultimately, our food supply, said Oliver Schabenberger, COO and CTO of SAS, in a statement.

Researchers from the SAS IoT Division are developing a bioacoustic monitoring system to non-invasively track real-time conditions of beehives using digital signal processing tools and machine learning algorithms available in SASEvent Stream Processingand SAS Viya software.

By connecting sensors to SAS four Bee Downtown hives at its headquarters in Cary, NC, the team startedstreaming hive datadirectly to the cloud to continuously measure data points in and around the hive, including weight, temperature, humidity, flight activity and acoustics. In-stream machine learning models were used to listen to the hive sounds, which can indicate health, stress levels, swarming activities and the status of the queen bee.

To ensure only the hum of the hive was being used to determine bees health and happiness, researchers used robust principal component analysis (RPCA), a machine learning technique, to separate extraneous or irrelevant noises from the inventory of sounds collected by hive microphones.

The researchers found that with RPCA capabilities, they could detect worker bees piping at the same frequency range at which a virgin queen pipes after a swarm, likely to assess whether a queen was present. The researchers then designed an automated pipeline to detect either queen piping following a swarm or worker piping that occurs when the colony is queenless.

SAS said the acoustic analysis can alert beekeepers to queen disappearances immediately, which is vitally important to significantly reducing colony loss rates. Its estimated the annual loss rates of US beehives exceed 40 percent and between 25-40 percent of these losses are due to queen failure.

With this system, SAS said beekeepers will have a deeper understanding of their hives without having to conduct time-consuming and disruptive manual inspections.

As a beekeeper myself, I know the magnitude of bees impact on our ecosystem, and Im inspired to find innovative ways to raise healthier bees to benefit us all, said Anya McGuirk, Distinguished Research Statistician Developer in the IoT division at SAS.

The researchers said they plan to implement the acoustic streaming system very soon and are continuing to look for ways to broaden the usage of technology to help honey bees and ultimately humankind.

SAS is also launching a data visualization that maps out bees counted around the globe for theWorld Bee Count, an initiative co-founded by theCenter for Analytics Research and Education(CARE) at Appalachian State University.

The goal: to engage citizens across the world to take pictures of bees as a first step toward understanding the reasons for their alarming decline, SAS says.

The World Bee Count allows us to crowdsource bee data to both visualize our planets bee population and create one of the largest, most informative data sets about bees to date, said Joseph Cazier, Professor and Executive Director at Appalachian State Universitys CARE, in a statement.

In early May, the World Bee Count app was launched for users both beekeepers and the general public, aka citizen data scientists to add data points to the Global Pollinator Map. Within the app, beekeepers can enter the number of hives they have, and any user can submit pictures of pollinators from their camera roll or through the in-app camera. Through SAS Visual Analytics, SAS has created avisualization mapto display the images users submit via the app which, it says, could potentially provide insights about the conditions that lead to the healthiest bee populations.

In future stages of this project, SAS said, the robust data set created from the app could help groups like universities and research institutes better strategize ways to save these vital creatures.

Representing the Nordic region, a team from Amesto NextBridge won the 2020 SAS EMEA Hackathon, which challenged participants to improve sustainability using SAS Viya. Their winning project used machine learning to maximize bees access to food, which would in turn benefit mankinds food supply.

In partnership withBeefutures, the team developed a system capable of automatically detecting, decoding and mapping bee waggle dances using Beefutures observation hives and SAS Viya.

Observing all of these dances manually is virtually impossible, but by using video footage from inside the hives and training machine learning algorithms to decode the dance, we will be able to better understand where bees are finding food, said Kjetil Kalager, lead of the Amesto NextBridge and Beefutures team. We implemented this information, along with hive coordinates, sun angle, time of day and agriculture around the hives into an interactive map in SAS Viya and then beekeepers can easily decode this hive information and relocate to better suited environments if necessary.

SAS said this systematic real-time monitoring of waggle dances allows bees to act as sensors for their ecosystems. It may also uncover other information bees communicate through dance that could help us save and protect their population.

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From streaming hive data to acoustics, SAS uses machine learning, analytics to boost bee populations - WRAL Tech Wire

Gartner believes Artificial Intelligence (AI) security will be a top strategic technology trend in 2020, and that enterprises must gain awareness of AIs impact on the security space. However, many enterprise IT leaders still lack a comprehensive understanding of the technology and what the technology can realistically achieve today. It is important for leaders to question exasperated Marketing claims and over-hyped promises associated with AI so that there is no confusion as to the technologys defining capabilities.

IT leaders should take a step back and consider if their company and team is at a high enough level of security maturity to adopt advanced technology such as AI successfully. The organizations business goals and current focuses should align with the capabilities that AI can provide.

A study conducted by Widmeyer revealed that IT executives in the U.S. believe that AI will significantly change security over the next several years, enabling IT teams to evolve their capabilities as quickly as their adversaries.

Of course, AI can enhance cybersecurity and increase effectiveness, but it cannot solve every threat and cannot replace live security analysts yet. Today, security teams use modern Machine Learning (ML) in conjunction with automation, to minimize false positives and increase productivity.

As adoption of AI in security continues to increase, it is critical that enterprise IT leaders face the current realities and misconceptions of AI, such as:

AI is not a solution; it is an enhancement. Many IT decision leaders mistakenly consider AI a silver bullet that can solve all their current IT security challenges without fully understanding how to use the technology and what its limitations are. We have seen AI reduce the complexity of the security analysts job by enabling automation, triggering the delivery of cyber incident context, and prioritizing fixes. Yet, security vendors continue to tout further, exasperated AI-enabled capabilities of their solution without being able to point to AIs specific outcomes.

If Artificial Intelligence is identified as the key, standalone method for protecting an organization from cyberthreats, the overpromise of AI coupled with the inability to clearly identify its accomplishments, can have a very negative impact on the strength of an organizations security program and on the reputation of the security leader. In this situation, Chief Information Security Officers (CISO) will, unfortunately, realize that AI has limitations and the technology alone is unable to deliver aspired results.

This is especially concerning given that 48% of enterprises say their budgets for AI in cybersecurity will increase by 29 percent this year, according to Capgemini.

Read more:Improve Your Bottom Line With Contract Automation and AI

We have seen progress surrounding AI in the security industry, such as the enhanced use of ML technology to recognize behaviors and find security anomalies. In most cases, security technology can now correlate the irregular behavior with threat intelligence and contextual data from other systems. It can also use automated investigative actions to provide an analyst with a strong picture of something being bad or not with minimal human intervention.

A security leader should consider the types of ML models in use, the biases of those models, the capabilities possible through automation, and if their solution is intelligent enough to build integrations or collect necessary data from non-AI assets.

AI can handle a bulk of the work of a Security Analyst but not all of it. As a society, we still do not have enough trust in AI to take it to the next level which would be fully trusting AI to take corrective actions towards those anomalies it identified. Those actions still require human intervention and judgment.

Read more:The Nucleus of Statistical AI: Feature Engineering Practicalities for Machine Learning

It is important to consider that AI can make bad or wrong decisions. Given that humans themselves create and train the models that achieve AI, it can make biased decisions based on the information it receives.

Models can produce a desired outcome for an attacker, and security teams should prepare for malicious insiders to try to exploit AI biases. Such destructive intent to influence AIs bias can prove to be extremely damaging, especially in the legal sector.

By feeding AI false information, bad actors can trick AI to implicate someone of a crime more directly. As an example, just last year, a judge ordered Amazon to turn over Echo recordings in a double murder case. In instances such as these, a hacker has the potential to wrongfully influence ML models and manipulate AI to put an innocent person in prison. In making AI more human, the likelihood of mistakes will increase.

Whats more, IT decision-makers must take into consideration that attackers are utilizing AI and ML as an offensive capability. AI has become an important tool for attackers, and according to Forresters Using AI for Evil report, mainstream AI-powered hacking is just a matter of time.

AI can be leveraged for good and for evil, and it is important to understand the technologys shortcomings and adversarial potential.

Though it is critical to acknowledge AIs realistic capabilities and its current limitations, it is also important to consider how far AI can take us. Applying AI throughout the threat lifecycle will eventually automate and enhance entire categories of Security Operations Center (SOC) activity. AI has the potential to provide clear visibility into user-based threats and enable increasingly effective detection of real threats.

There are many challenges IT decision-makers face when over-estimating what Artificial Intelligence alone can realistically achieve and how it impacts their security strategies right now. Security leaders must acknowledge these challenges and truths if organizations wish to reap the benefits of AI today and for years to come.

Read more:AI in Cybersecurity: Applications in Various Fields

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Key Dynamics of Machine Learning and Intelligent Automation in Contemporary Market

Automation has generated great buzz across many industries globally. And as more and more organizations are shifting their focus to digital transformation and innovation, they are adopting automation technologies to increase their business efficiency by reducing human errors. Moreover, when mixed with machine learning capabilities, automation tends to serve with an attractive proposition to an organization and its services across the market. The combination is popularly known as intelligent automation.

Intelligent automation as a blend of innovative AI capabilities and automation is extensively applicable to the more sophisticated end of the automation-aided workflow continuum. The potential benefits of ML-enabled intelligent automation capabilities, in terms of additional insights and financial impact, can be greatly augmented.

Today, to stay relevant, competitive, and efficient, organizations need to contemplate their business processes with the addition of machine learning and automation. Together they can provide great advantages to organisations. Being substantially different technologies, together they have the ability to evaluate the process and make cognitive decisions.

To make your automation process more dynamic, the successful integration of machine learning is key. Moreover, intelligent automation as an amalgamation is a two-way improvement strategy, where automation tools are exposed to huge amounts of data, and machine learning can be leveraged to determine how robots can be programmed to store and filter useful data.

Individually, both technologies are very fast-growing markets. The global machine learning market size is expected to reach US$96.7 billion by 2025, according to market reports, expanding at a CAGR of 43.8% from 2019 to 2025. Also, the global automation market size is expected to reach US$368.4 billion in 2025, from US$190.2 billion in 2017 growing at a CAGR of 8.8% from 2018 to 2025.

Moreover, the intelligent process automation market was valued at US$6.25 billion in 2017 and is projected to reach US$13.75 billion by 2023, at a CAGR of 12.9% from 2018 to 2023.

Organizations are becoming more open today, allowing their products and technologies to be better integrated and share data and this trend has given rise to innovative technology like intelligent automation.

With the incorporation of machine learning capabilities, intelligent automation possesses the ability to empower humans with advanced smart technologies and agile processes to enable fast and informative decisions. It also caters to a wide array of business operations with key benefits including increasing process efficiency and customer experience, better optimization of back-office operations, reduction in costs, and minimizing risk factors. Intelligent automation also optimizes the workforce productivity with better and effective monitoring and fraud detection. It also enables a more comprehensive product and service innovation.

Being an undeniable catalyst to progress, moreover, intelligent automation is no threat to human jobs. Rather its incorporation in a collaborative manner can help employees reshape their skills and creatives. Intelligent automation has the core benefit to extensively improve and digitalize business processes along with human judgment.

Therefore, the time has arrived when companies should consider investing strategically in automation and ML capabilities in order to understand and meet the expectations of customers which eventually leads to improved productivity and low-cost scalability.

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Machine Learning in Finance Market Provides in-depth analysis of the Machine Learning in Finance Industry, with current trends and future estimations...

In October 2019, an Amazon employee in Melbourne, Australia, bumped into another person while cycling on the road. As she was assuring that person that she would help, she realised he was deaf and mute and had no idea what she was saying.

That awkward situation could have been avoided if assistive technology was on hand to facilitate communication between the two parties. Following the incident, a team led by Santanu Dutt, head of technology for Southeast Asia at Amazon Web Services, got down to work.

Within 10 days or so, Dutts team had built a machine learning model that was trained on sign languages. Using images of a person gesturing in sign language that were captured from a camera, the model could recognise and translate gestures into text. The model also could convert spoken words into text for a deaf-mute person to see.

Dutt said the model can also be customised to translate speech into sign languages as the machine learning services and application programming interfaces (APIs) are available and open although he has not seen that demand yet. But once you write a small bit of code, training the machine learning model is easy, he said.

But there is still work to be done. As the training was performed with signs gestured against a white background, the efficacy of the model in its current form would be limited in actual use.

Our team had limited time to showcase this and we wanted to bump up something to showcase for experimental purposes, said Dutt, adding that organisations can use tools such as Amazon SageMaker to edit and train the model with more images and videos to recognise a larger variety of environments.

As the training process is intensive, Dutt said organisations with limited resources can use Amazon SageMaker Ground Truth to build training datasets for such machine learning models quickly. Besides automatic labelling, Ground Truth also provides access to human labellers through the Amazon Mechanical Turk crowdsourcing service.

This will also help to improve the models accuracy rate. The more data you have, the more accurate the model gets, said Dutt, adding that developers can set confidence levels and reject results that fall below a certain level of accuracy.

Dutt said AWSs public sector team has engaged non-profit organisations in Australia to conduct a proof of concept that makes use of the machine learning model, as well as those in other countries through credits that offset the cost of using AWS services to train and deploy the model.

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How machine learning can bridge the communication gap - ComputerWeekly.com

Machine learning is a subset of artificial intelligence (AI) that gives computer systems the ability to automatically learn and improve from experience, rather than being explicitly programmed its now a hugely powerful tool that has been leveraged across a raft of completely different industries for several years already.

Machine learning is now used by banks to sift through hundreds of millions of transactions to detect fraud; its predictive analytics ability has been used in agriculture to comb through seasonal farming and weather data; machine learning will even help digital marketers to plan budget forecasts and research content trends. And those are just three examples of millions now used each days.

The basic premise of machine learning, in theory, is simple. An algorithm is fed a dataset, and is taught to respond in certain way the next time it encounters similar data.

But in practice, its very difficult, and thats why theres such demand for specialists like data scientists. Creating a machine learning algorithm requires numerous steps from gathering and preparing data, setting evaluation protocols and developing benchmark models, before there is anything near a workable machine learning algorithm ready for deployment.

Even then, they may not work well enough, and that means going back to the drawing board. Machine learning requires an extensive list of skills including computer science and programming, mathematics and statistics, data science, deep learning, and problem-solving.

In short, machine learning is out of reach for many, and yet the rapid boom and endless applications emerging mean more and more businesses now want to get hands-on, whether thats to improve products and services for customers, or to make internal processes more efficient.

That surge of interest has led many to consider off-the-shelf machine learning solutions, and that was how automated machine learning came to be to make ML accessible to non-ML experts.

Automated machine learning, or AutoML, reduces or completely removes the need for skilled data scientists to build machine learning models. Instead, these systems allow users to provide training data as an input, and receive a machine learning model as an output.

AutoML software companies may take a few different approaches. One approach is to take the data and train every kind of model, picking the one that works best. Another is to build one or more models that combine the others, which sometimes give better results.

Despite its name, AutoML has so far relied a lot on human input to code instructions and programs that tell a computer what to do. Users then still have to code and tune algorithms to serve as building blocks for the machine to get started. There are pre-made algorithms that beginners can use, but its not quite automatic.

But now a team of Google computer scientists believe they have come up with a new AutoML method that can generate the best possible algorithm for a specific function, without human intervention.

The new method is dubbed AutoML-Zero, which works by continuously trying algorithms against different tasks, and improving upon them using a process of elimination, much like Darwinian evolution.

AutoML-Zero greatly reduces the human element which had heavily influenced ML programs before, with more complex programs requiring sophisticated code written by hand. Limiting human involvement also helps remove bias and potential errors, especially when multiple iterative developments are involved.

Esteban Real, a software engineer at Google Brain, Research and Machine Intelligence, and lead author of the research, explained to Popular Mechanics: Suppose your goal is to put together a house. If you had at your disposal pre-built bedrooms, kitchens, and bathrooms, your task would be manageable but you are also limited to the rooms you have in your inventory.

If instead you were to start out with bricks and mortar, then your job is harder, but you have more space for creativity.

Instead, Googles AutoML-Zero uses basic mathematics, much like other computer programming languages. AutoML-Zero appears to involve even less human intervention than Googles own ML programming language, Cloud AutoML.

In a basic sense, Google developers have created a system that is able to churn out 100 randomly-generated algorithms and then identify which one works best. After several generations, the algorithms become better and better until the machine finds one that performs well enough to evolve.

New ground can be made here as those surviving algorithms can be tested against standard AI problems for their ability to solve new ones.

The development team is working to eliminate any remaining human bias their method retains, as well as to solve a tricky scaling issue. If they are successful, Google might be able to introduce a full-scale version that provides machine learning capabilities to small-medium enterprises (SMEs) and non-ML developers.

And crucially, those machine learning applications will be free from human input.

Joe Devanesan | @thecrystalcrown

Joe's interest in tech began when, as a child, he first saw footage of the Apollo space missions. He still holds out hope to either see the first man on the moon, or Jetsons-style flying cars in his lifetime.

Link:
Google has found a way for machine learning algorithms to evolve themselves - Tech Wire Asia

Trusted Business Insights answers what are the scenarios for growth and recovery and whether there will be any lasting structural impact from the unfolding crisis for the Machine Learning market.

Trusted Business Insights presents an updated and Latest Study on Machine Learning Market 2019-2026. The report contains market predictions related to market size, revenue, production, CAGR, Consumption, gross margin, price, and other substantial factors. While emphasizing the key driving and restraining forces for this market, the report also offers a complete study of the future trends and developments of the market.The report further elaborates on the micro and macroeconomic aspects including the socio-political landscape that is anticipated to shape the demand of the Machine Learning market during the forecast period (2019-2029).It also examines the role of the leading market players involved in the industry including their corporate overview, financial summary, and SWOT analysis.

Get Sample Copy of this Report @ Global Machine Learning Market 2020 (Includes Business Impact of COVID-19)

Global Machine Learning Market Insights, Ongoing Trends, End-use Applications, Market Size, Growth, and Forecast to 2029 is a research report on the target market, and is in process of completion at Trusted Business Insights. The report contains information and data, and inputs that have been verified and validated by experts in the target industry. The report presents a thorough study of annual revenues, historical data and information, key developments and strategies by major players that offer applications in the market. Besides critical data and information, the report includes key and ongoing trends, factors that driving market growth, factors that are potential restraints to market growth, as well as opportunities that can be leveraged for potential revenue generation in untapped regions and countries, as well as threats or challenges. The global treadmill ergometer market is segmented on the basis of application, end user, and region. Regions are further branched into key countries, and revenue shares and growth rates for each of the segment, and region as well as key countries have been provided in the final report.

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Machine Learning: Overview

Machine Learning (ML) is a sub-segment of Artificial Intelligence (AI) platform. This scientific concept studies computational learning, statistics and algorithms models of computers used to perform specific tasks without input of instructions, and recognition of patterns in AI. Basically, it explores and analysis construction of statistical data and algorithms and estimates forecasts on analyzed data. Various applications of ML include Optical Character Recognition (OCR), e-mail filtering, detection of network intruders, learning to rank, and computer vision.

Machine learning has paved its way across several applications. In advertising sector, ML is implemented to analyze customers behavior, which can help in improving advertising strategies. AI-driven marketing and advertising is based on usage of various models in order to automate and optimize, and to use data into appropriate actions. In case of banking, financial services, and insurance (BFSI), machine learning is used to manage process such as assets management and loan approval, among others. Security, and management and publishing of documents are among other applications of machine learning.

In the recent past, the scope of applications of machine learning technology has widened into certain new aspects. For instance, the US Defense department plans to implement machine learning in combat vehicles for predictive maintenance, to determine when and where the repair and maintenance is required. In stock market, this technology is being used to make estimations and projections about the market with approximately 60% accuracy level.

Dynamics: Global Machine Learning Market

The machine learning market in North America is expected to record dominant share and is projected to continue with its dominance over the 10-year forecast period. This can be attributable to increasing investments and higher adoption of machine learning technology by to numerous organizations in BFSI sector in the region. In 2019 for instance, New York-based financial company, JPMorgan Chase & Co., invested in a startup Limeglass Ltd., which is a service provider of artificial intelligence, machine learning, and Natural Language Processing (NLP) to analyze organizational research. Limeglass Ltd. assists companies in developing technologically advanced products required for banking and finance.

The Asia Pacific machine learning market is projected to register highest growth rate over the 10-year forecast period. This is attributable to increasing adoption of advanced technologies including machine learning, along with a huge talent-base in countries such as China and India. In addition, emerging markets are projected to offer revenue opportunities by allowing entrance into these untapped markets and reach large consumer base that is willing to opt for AI-enabled products and services, which is further projected to drive Asia Pacific market growth. In 2018 for instance, NITI Aayog a policy think-tank of the Government of India, in collaboration with a multinational technology company, Google LLC will train and incubate AI-based firms and start-ups in India.

Global Machine Learning Market Segmentation:

Segmentation by Component:

HardwareSoftwareServices

Segmentation by Enterprise Size:

Small and Medium Enterprises (SMEs)Large Enterprises

Segmentation by End-use Industry:

HealthcareBFSILawRetailAdvertising & MediaAutomotive & TransportationAgricultureManufacturingOthers

Quick Read Table of Contents of this Report @ Global Machine Learning Market 2020 (Includes Business Impact of COVID-19)

Trusted Business InsightsShelly ArnoldMedia & Marketing ExecutiveEmail Me For Any ClarificationsConnect on LinkedInClick to follow Trusted Business Insights LinkedIn for Market Data and Updates.US: +1 646 568 9797UK: +44 330 808 0580

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Machine Learning Market 2020 | Analyzing The COVID-19 Impact Followed By Restraints, Opportunities And Projected Developments - 3rd Watch News

Amongst technologies that have revolutionised industries in the last two decades, Machine Learning holds a significant place. Machine Learning has not only made its way into versatile industry applications but has also allowed businesses to transform their operations by reducing costs, boosting efficiency, and transforming customer experience. Currently, Machine Learning is at a crucial crossroad where research is on to take automation to a stage where it requires no human intervention at all. This will pave the path towards a fully automated workflow which is achievable by integrating it with Cloud Computing. For predictive analysis to take over industries, the vast amount of data that has to be processed in Machine Learning models need a scalable distributed system for storage. This is where the relevance of Cloud comes in. ML, when paired with Cloud, forms an Intelligent Cloud that becomes a suitable destination for all Machine Learning projects and becomes handy for data collection, data optimization, data distribution, and managing a data transport network and deployment of Machine Learning models. With almost every business looking to deploy AI in their operations in the near future, the demand for skilled ML and Cloud professionals is more than ever before. A report by the World Economic Forum also suggests that this industry will create about 58 million new jobs by 2022. This clearly indicates the importance of upskilling oneself with a strongly connected ML and Cloud program.To cater to this growing demand and to help young professionals understand and develop packaged ML solutions, upGrad has collaborated with IIT Madras to develop an Advanced Certification in Machine Learning and Cloud program. The 9-month long program recognises the importance of taking ML to Cloud to realise full-scale AI implementations across verticals. upGrad understands the relevance of data and insights in business operations. The program covers the deployment of advanced Machine Learning models on Cloud, giving individuals an opportunity to cater to data demands across multiple industry domains like e-commerce, retail, healthcare, banking, manufacturing, transport, NBFC, and finance among others.'; var randomNumber = Math.random(); var isIndia = (window.geoinfo && window.geoinfo.CountryCode === 'IN') && (window.location.href.indexOf('outsideindia') === -1 ); //console.log(isIndia && randomNumber A Highly Selective & Exclusive ProgramTo ensure that the program is exciting as well as challenging, upGrads Advanced Certification in Machine Learning and Cloud is highly selective & exclusive and admits only 70 individuals in one cohort to ensure focused learning and individual growth. For this, applicants have to go through the All India Aptitude Test from IIT Madras, a comprehensive entrance test, an interview round, and a final panel selection before they are allowed admittance to the program. This ensures that each academic batch consists of highly skilled individuals who are capable of carrying the IIT batch forward and can later help their employers take high-stake data risks with confidence. The time investment for this program on a weekly basis is about 12-14 hours which further makes it an ideal upskilling programme for working individuals.Learn from the best in the business

With data being the operative word for every sector, every organization is currently scaling up its AI and ML workforce. upGrads Advanced Certification in Machine Learning and Cloud is helping learners become vital to their companys success by training them efficiently. upGrad learners deploy machine learning models using PySpark on Cloud and they get an opportunity to learn from a set of experienced Machine Learning faculty and industry leaders. The prestigious program also has about 300+ hiring partners, ensuring that learners can land up in the industry of their choice by the end of the program. The program has been largely successful in building employability of learners and boosting their annual packages. The current demand for ML engineers is at an all-time high, with even freshers getting hired at astounding pay packages. Considering this shift, upGrads Advanced Program in Machine Learning and Cloud is the best way to flag off ones ML journey.

Specifically designed for data analysts, business analysts, cloud engineers, software engineers, application developers, and product managers among others, the program will be highly beneficial in learning about the following aspects:Programming: Learn core and necessary languages like Python, which is required for ML operations and SQL, which is a vital language of the Cloud along with deployment of Machine Learning models using Cloud.

Machine learning concepts: Learn both basic and advanced subjects within ML. This will help learners to understand the application of appropriate ML algorithms to categorize unknown data or make predictions about it. The program also helps learners modify and craft algorithms of their own.

Foundations of Cloud and Hadoop: Learn about Hadoop, Hive, and HDFS along with the implementation of ML algorithms in the cloud on Spark/ PySpark (AWS/ Azure/ GCP).

Why choose upGrad?upGrads Advanced Certification Program in Machine Learning and Cloud will provide learners with a PG Certification from IIT Madras, one of Indias top IITs. This teaching panel includes faculty from IIT Madras and leading industry experts who seamlessly integrate online lectures, offline engagement, case studies, and interactive networking sessions. It provides 360-degree support to young professionals by taking care of career counselling, dedicated student success mentors, resume feedback, interview preparation, and job assistance. Over the years, the program has seen 500+ career transitions, with an average salary hike of 58%. Many of these learners have been placed in companies like KPMG, Uber, Big Basket, Bain & Co, Pwc, Zivame, Fractal Analytics, Microsoft etc. with impressive salary shifts.

upGrads Advanced Certification in Machine Learning and Cloud is also one of the most cost-effective methods for professionals looking to hop onto the Machine Learning bandwagon. The program fee is 2,00,000 and it is also available at a no cost EMI of 29,166/- per month. By uniting upGrads data expertise with IIT Madras academic excellence, it provides a unique opportunity to learners to scale up.

If you want to fast-track your career and make yourself readily employable, its time you take the All India Test for the Advanced Certification in Machine Learning and Cloud. The program commences on June 30, 2020, with admissions closing on June 7, 2020, owing to a mandatory pre-prep course spanning across 3 weeks before the start of the program. Its time to take the big leap with upGrad. Apply for the All India Aptitude Test today.

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Evolve your career with upGrads Machine Learning and Cloud program in association with IIT Madras - Economic Times

AI has been making some major strides in the computing world in recent years. But that also means they have become increasingly vulnerable to security concerns. Just by examining the power usage patterns or signatures during operations, one may able to gain access to sensitive information housed by a computer system. And in AI, machine learning algorithms are more prone to such attacks. The same algorithms are employed in smart home devices, cars to identify different forms of images and sounds that are embedded with specialized computing chips.

These chips rely on using neural networks, instead of a cloud computing server located in a data center miles away. Due to such physical proximity, the neural networks can perform computations, at a faster rate, with minimal delay. This also makes it simple for hackers to reverse-engineer the chips inner workings using a method known as differential power analysis (DPA). Thereby, it is a warning threat for the Internet of Things/edge devices because of their power signatures or electromagnetic radiation signage. If leaked, the neural model, including weights, biases, and hyper-parameters, can violate data privacy and intellectual property rights.

Recently a team of researchers of North Carolina State University presented a preprint paper at the 2020 IEEE International Symposium on Hardware Oriented Security and Trust in San Jose, California. The paper mentions about the DPA framework to neural-network classiers. First, it shows DPA attacks during inference to extract the secret model parameters such as weights and biases of a neural network. Second, it proposes the rst countermeasures against these attacks by augmenting masking. The resulting design uses novel masked components such as masked adder trees for fully connected layers and masked Rectier Linear Units for activation functions. The team is led by Aydin Aysu, an assistant professor of electrical and computer engineering at North Carolina State University in Raleigh.

While DPA attacks have been successful against targets like the cryptographic algorithms that safeguard digital information and the smart chips found in ATM cards or credit cards, the team observes neural networks as possible targets, with perhaps even more profitable payoffs for the hackers or rival competitors. They can further unleash adversarial machine learning attacks that can confuse the existing neural network

The team focused on common and simple binarized neural networks (an efcient network for IoT/edge devices with binary weights and activation values) that are adept at doing computations with less computing resources. They began by demonstrating how power consumption measurements can be exploited to reveal the secret weight and values that help determine a neural networks computations. Using random known inputs, for multiple numbers of time, the adversary computes the corresponding power activity on an intermediate estimate of power patterns linked with the secret weight values of BNN, in a highly-parallelized hardware implementation.

Then the team designed a countermeasure to secure the neural network against such an attack via masking (an algorithm-level defense that can produce resilient designs independent of the implementation technology). This is done by splitting intermediate computations into two randomized shares that are different each time the neural network runs the same intermediate computation. This prevents an attacker from using a single intermediate computation to analyze different power consumption patterns. While the process requires tuning for protecting specific machine learning models, they can be executed on any form of computer chip that runs on a neural network, viz., Field Programmable Gate Arrays (FPGA), and Application-specific Integrated Circuits (ASIC). Under this defense technique, a binarized neural network requires the hypothetical adversary to perform 100,000 sets of power consumption measurements instead of just 200.

However, there are certain main concerns involved in the masking technique. During initial masking, the neural networks performance dropped by 50 percent and needed nearly double the computing area on the FPGA chip. Second, the team expressed the possibility of attackers avoid the basic masking defense by analyzing multiple intermediate computations instead of a single computation, thus leading to a computational arms race where they are split into further shares. Adding more security to them can be time-consuming.

Despite this, we still need active countermeasures against DPA attacks. Machine Learning (ML) is a critical new target with several motivating scenarios to keep the internal ML model secret. While Aysu explains that research is far from done, his research is supported by both the U.S. National Science Foundation and the Semiconductor Research Corporations Global Research Collaboration. He anticipates receiving funding to continue this work for another five years and hopes to enlist more Ph.D. students interested in the effort.

Interest in hardware security is increasing because, at the end of the day, the hardware is the root of trust, Aysu says. And if the root of trust is gone, then all the security defenses at other abstraction levels will fail.

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New Research Claims to Have Found a Solution to Machine Learning Attacks - Analytics Insight

The chaos and uncertainty surrounding the coronavirus pandemic have claimed an unlikely victim: the machine learning systems that are programmed to make sense of our online behavior.

The algorithms that recommend products on Amazon, for instance, are struggling to interpret our new lifestyles, MIT Technology Review reports.

And while machine learning tools are built to take in new data, they're typically not so robust that they can adapt as dramatically as needed.

For instance, MIT Tech reports that a company that detects credit card fraud needed to step in and tweak its algorithm to account for a surge of interest in gardening equipment and power tools.

An online retailer found that its AI was ordering stock that no longer matched with what was selling. And a firm that uses AI to recommend investments based on sentiment analysis of news stories was confused by the generally negative tone throughout the media.

"The situation is so volatile," Rael Cline, CEO of the algorithmic marketing consulting firm Nozzle, told MIT Tech.

"You're trying to optimize for toilet paper last week, and this week everyone wants to buy puzzles or gym equipment."

While some companies are dedicating more time and resources to manually steering their algorithms, others see this as an opportunity to improve.

"A pandemic like this is a perfect trigger to build better machine-learning models," Sharma said.

READ MORE: Our weird behavior during the pandemic is messing with AI models

This article was originally published by Futurism. Read the original article.

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Our Behaviour in This Pandemic Has Seriously Confused AI Machine Learning Systems - ScienceAlert