Adelaide Tuesday March 24, 2020

Australian Institute of Machine Learning creates an AI keyboard that creates its own lyrics.

Dr Jamie Sherrah, a researcher based at the University of Adelaide institute in South Australia, created the singing keyboard as a way to demonstrate machine learnings capacities outside the usual sectors.

Named Voog the voice version of an old synthesizer brand called Moog the keyboard uses machine learning for singing synthesis from text as a melody is played.

Dr Sherrah said he planned to eventually commercialise a prototype that creates meaningful lyrics.

He also hoped to attract musicians already experimenting with voice in their music to begin using Voog.

Its a human driven performance, you still choose the pitch and the timing of the notes by playing, usually you would play those notes and then sing but with this, you are playing and its singing, Dr Sherrah said.

For awhile Yamaha has had a software product to do that offline but I havent seen a live keyboard like this before.

Dr Sherrah said while there was extensive AI work happening at the Australian Institute of Machine Learning within the health, defence and education sectors, the institute was also exploring an alternative arts stream.

This is a way of trying to connect with people and to try and show them the breadth of applications for machine learning and the kinds of things were working on, Dr Sherrah said.

Dr Sherrah, who plays guitar, based his PhD on using genetic programming to automatically learn features for pattern recognition and is now also working with several startup companies on other machine learning projects.

Based from Adelaide, Dr Sherrah is also chief scientist with Canadian startup FTSY that has created an app where a mobile phone can be used to 3D reconstruct a users feet so they can then find the right shoe size and shape when buying online.

Another of his Australian Institute of Machine Learning projects is a self-help guru Froyd AI designed as a Twitter bot.

Froyd AI has been busy delivering a thought-provoking online message based on data from hundreds of millions of web pages.

The bot was developed through the institutes arts space program, which recently hosted avant-garde New York artist Laurie Anderson as its first artist-in-residence for a arts meets AI hackathon.

Each day the contemplative bot uses machine learning to deliver one pithy observation crafted with a grain of truth.

One of the big developments in machine learning in the last two years or so has been on this model trained on lots of English natural language text that is able to generate very realistic looking text, Dr Sherrah said.

Among the pearls of artificial intelligence are I am a mind trapped in a computer and there is no way around it and The meaning of life as we know it is a great one. It is a place where all things are created and consumed and must be continually transformed.

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Making music with Voog the singing keyboard - The Lead South Australia

If not the most deadly, the novel coronavirus (COVID-19) is one of the most contagious diseases to have hit our green planet in the past decades. In little over three months since the virus was first spotted in mainland China, it has spread to more than 90 countries, infected more than 185,000 people, and taken more than 3,500 lives.

As governments and health organizations scramble to contain the spread of coronavirus, they need all the help they can get, including from artificial intelligence. Though current AI technologies arefar from replicating human intelligence, they are proving to be very helpful in tracking the outbreak, diagnosing patients, disinfecting areas, and speeding up the process of finding a cure for COVID-19.

Data science and machine learning might be two of the most effective weapons we have in the fight against the coronavirus outbreak.

Just before the turn of the year, BlueDot, an artificial intelligence platform that tracks infectious diseases around the world, flagged a cluster of unusual pneumonia cases happening around a market in Wuhan, China. Nine days later, the World Health Organization (WHO)released a statementdeclaring the discovery of a novel coronavirus in a hospitalized person with pneumonia in Wuhan.

BlueDot usesnatural language processingandmachine learning algorithmsto peruse information from hundreds of sources for early signs of infectious epidemics. The AI looks at statements from health organizations, commercial flights, livestock health reports, climate data from satellites, and news reports. With so much data being generated on coronavirus every day, the AI algorithms can help home in on the bits that can provide pertinent information on the spread of the virus. It can also find important correlations between data points, such as the movement patterns of the people who are living in the areas most affected by the virus.

The company also employs dozens of experts who specialize in a range of disciplines including geographic information systems, spatial analytics, data visualization, computer sciences, as well as medical experts in clinical infectious diseases, travel and tropical medicine, and public health. The experts review the information that has been flagged by the AI and send out reports on their findings.

Combined with the assistance of human experts, BlueDots AI can not only predict the start of an epidemic, but also forecast how it will spread. In the case of COVID-19, the AI successfully identified the cities where the virus would be transferred to after it surfaced in Wuhan. Machine learning algorithms studying travel patterns were able to predict where the people who had contracted coronavirus were likely to travel.

Coronavirus (COVID-19) (Image source:NIAID)

You have probably seen the COVID-19 screenings at border crossings and airports. Health officers use thermometer guns and visually check travelers for signs of fever, coughing, and breathing difficulties.

Now,computer vision algorithmscan perform the same at large scale. An AI system developed by Chinese tech giant Baidu uses cameras equipped with computer vision and infrared sensors to predict peoples temperatures in public areas. The system can screen up to 200 people per minute and detect their temperature within a range of 0.5 degrees Celsius. The AI flags anyone who has a temperature above 37.3 degrees. The technology is now in use in Beijings Qinghe Railway Station.

Alibaba, another Chinese tech giant, has developed an AI system that candetect coronavirus in chest CT scans. According to the researchers who developed the system, the AI has a 96-percent accuracy. The AI was trained on data from 5,000 coronavirus cases and can perform the test in 20 seconds as opposed to the 15 minutes it takes a human expert to diagnose patients. It can also tell the difference between coronavirus and ordinary viral pneumonia. The algorithm can give a boost to the medical centers that are already under a lot of pressure to screen patients for COVID-19 infection. The system is reportedly being adopted in 100 hospitals in China.

A separate AI developed by researchers from Renmin Hospital of Wuhan University, Wuhan EndoAngel Medical Technology Company, and the China University of Geosciences purportedly shows 95-percent accuracy on detecting COVID-19 in chest CT scans. The system is adeep learning algorithmtrained on 45,000 anonymized CT scans. According to a preprint paperpublished on medRxiv, the AIs performance is comparable to expert radiologists.

One of the main ways to prevent the spread of the novel coronavirus is to reduce contact between infected patients and people who have not contracted the virus. To this end, several companies and organizations have engaged in efforts to automate some of the procedures that previously required health workers and medical staff to interact with patients.

Chinese firms are using drones and robots to perform contactless delivery and to spray disinfectants in public areas to minimize the risk of cross-infection. Other robots are checking people for fever and other COVID-19 symptoms and dispensing free hand sanitizer foam and gel.

Inside hospitals, robots are delivering food and medicine to patients and disinfecting their rooms to obviate the need for the presence of nurses. Other robots are busy cooking rice without human supervision, reducing the number of staff required to run the facility.

In Seattle, doctors used a robot to communicate with and treat patients remotely to minimize exposure of medical staff to infected people.

At the end of the day, the war on the novel coronavirus is not over until we develop a vaccine that can immunize everyone against the virus. But developing new drugs and medicine is a very lengthy and costly process. It can cost more than a billion dollars and take up to 12 years. Thats the kind of timeframe we dont have as the virus continues to spread at an accelerating pace.

Fortunately, AI can help speed up the process. DeepMind, the AI research lab acquired by Google in 2014, recently declared that it has used deep learning to find new information about the structure of proteins associated with COVID-19. This is a process that could have taken many more months.

Understanding protein structures can provide important clues to the coronavirus vaccine formula. DeepMind is one of several organizations who are engaged in the race to unlock the coronavirus vaccine. It has leveraged the result of decades of machine learning progress as well as research on protein folding.

Its important to note that our structure prediction system is still in development and we cant be certain of the accuracy of the structures we are providing, although we are confident that the system is more accurate than our earlier CASP13 system, DeepMinds researchers wroteon the AI labs website. We confirmed that our system provided an accurate prediction for the experimentally determined SARS-CoV-2 spike protein structure shared in the Protein Data Bank, and this gave us confidence that our model predictions on other proteins may be useful.

Although its too early to tell whether were headed in the right direction, the efforts are commendable. Every day saved in finding the coronavirus vaccine can save hundredsor thousandsof lives.

This story is republished fromTechTalks, the blog that explores how technology is solving problems and creating new ones. Like them onFacebookhere and follow them down here:

Published March 21, 2020 17:00 UTC

Excerpt from:
Why AI might be the most effective weapon we have to fight COVID-19 - The Next Web

The latest report titled, Global Machine Learning in Retail Market 2019 by Company, Regions, Type and Application, Forecast to 2024 unveils the value at which the Machine Learning in Retail industry is anticipated to grow during the forecast period, 2019 to 2024. The report estimates CAGR analysis, competitive strategies, growth factors and regional outlook 2024. The report is a rich source of an exhaustive study of the driving elements, limiting components, and different market changes. It states market structure and then further forecasts several segments and sub-segments of the global market. The market study is provided on the basis of type, application, manufacturer as well as geography. Different elements such as opportunities, drivers, restraints, and challenges, market situation, market share, growth rate, future trends, risks, entry limits, sales channels, distributors are analyzed and examined within this report.

Exploring The Growth Rate Over A Period:

Business owners want to expand their business can refer to this report as it includes data regarding the rise in sales within a given consumer base for the forecast period, 2019 to 2024. The research analysts have mentioned a comparison between the Machine Learning in Retail market growth rate and product sales to allow business owners to discover the success or failure of a specific product or service. They have also added the driving factors such as demographics and revenue generated from other products to offer a better analysis of products and services by owners.

DOWNLOAD FREE SAMPLE REPORT: https://www.magnifierresearch.com/report-detail/7570/request-sample

Top industry players assessment: IBM, Microsoft, Amazon Web Services, Oracle, SAP, Intel, NVIDIA, Google, Sentient Technologies, Salesforce, ViSenze,

Product type assessment based on the following types: Cloud Based, On-Premises

Application assessment based on application mentioned below: Online, Offline

Leading market regions covered in the report are: North America (United States, Canada and Mexico), Europe (Germany, France, UK, Russia and Italy), Asia-Pacific (China, Japan, Korea, India and Southeast Asia), South America (Brazil, Argentina, Colombia), Middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria and South Africa)

Main Features Covered In Global Machine Learning in Retail Market 2019 Report:

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Moreover in the report, supply chain analysis, regional marketing type analysis, international trade type analysis by the market as well as consumer analysis of Machine Learning in Retail market has been covered. Further, it determines the manufacturing plants and technical data analysis, capacity, and commercial production date, R&D Status, manufacturing area distribution, technology source, and raw materials sources analysis. It also depicts to depict sales, merchants, brokers, wholesalers, research findings and conclusion, and information sources.

Customization of the Report:This report can be customized to meet the clients requirements. Please connect with our sales team (sales@magnifierresearch.com), who will ensure that you get a report that suits your needs. You can also get in touch with our executives on +1-201-465-4211 to share your research requirements.

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Emerging Trend of Machine Learning in Retail Market 2019 by Company, Regions, Type and Application, Forecast to 2024 - Bandera County Courier

PHOTO:Charles

Lets say you have a customer who has taken a certain action: downloaded an ebook, filled out an application, added a product to their cart, called into your call center or walked into your branch office, to name a few. What content, offer or message should you deliver to them next? What next step should you recommend? How can you best add value for that individual, while nurturing the person, wherever they are in their relationship with your business?

Based on your history (or even lack of history) with a given individual, you and your company might also have questions such as: Whats the best product to upsell to this particular client? (and should I even try to upsell that person?); Whats the right promotion to show an engaged shopper on my ecommerce site? and Whats the right item to promote to someone logged into my application? The list goes on.

These types of questions are all important to businesses today, who often talk about next best actions. This customer-centric (often 1-to-1) approach and sequencing strategy can take a number of forms. But at a basic level, the concept means what it sounds like: determining the most relevant or appropriate next action (or offer, promotion, content, etc.) to show a person in the moment, based on their current and previous actions or other information youve gathered about them across your online and offline channels. Next best actions can also include triggering messages to call center agents or sales reps to alert them of important activity, or to suggest the next best action they should take with a customer.

Companies put awide variety of thought, time and effort into establishing sequencing paths from none at all (with a one-size-fits-all message, promotion, offer, etc.) to a lot. At a majority of organizations, though, determining the next best action for their customers is very important, involving multiple teams of people across functions and divisions.

There are teams of marketers and designers, for instance, who create elaborate promotions and offers with different media for different channels. And there are customer experience teams who devote many cycles to thinking about call-center scripts and next best actions.

So when it comes to deploying those next best actions, it can devolve into an inter-departmental war about who gets the prime real estate. For example, when new visitors hit the homepage or when customers log into the app, what gets displayed in the hero area?

Why all the effort and involvement? Its because next best actions are strategically important to engagement and the bottom line. Present the right, relevant offer or action to a customer or prospect, and youre helping elicit interest and drive conversions. Present the wrong (e.g., outdated, irrelevant, mismatched to sales cycle stage, etc.) one, and youre losing customer interest or even turning them off your brand.

Related Article: Good Personalization Hinges on Good Data

For many years, organizations have taken a rule-based approach to determining the right next best action for a particular customer in a particular channel or at a particular stage in their journey. Rules are manually created and structured with if-then logic (e.g., IF a person takes this action or belongs to this group, THEN display this next). They govern the experiences and actions for audience segments which can be broad or get very narrow.

Three types of rules are the most frequently applied to next-best-action decisioning. These can be used on their own or, typically, in concert:

Related Article: Why Personalization Efforts Fail

But one problem with rules is the more targeted and relevant you want to get, the greater the number of rules you need to make. With rules, personalization of the next best action is inversely correlated to simplicity. In other words, to deliver truly relevant and highly specific actions and experiences using rules only, you quickly enter a world of nearly unmanageable complexity.

Theres also the time factor to consider. As you have likely experienced, it takes a lot of hours to create and prescribe sequencing via rules for the multitude of scenarios customers can encounter and the paths they can take. And unraveling a heavily nested set of rules in order to make minor adjustments (and make them correctly) can take many more hours.

Another problem with rules is that they are just a human guessing. Suppose youre wrong in the next best action youve set up for a customer to receive in fact, it may actually be hurting revenues or customer loyalty.

So while rules do play a vital role in determining and displaying next best actions, a rules-only-based approach generally isnt optimal or scalable in the long-term.

Related Article: Refine Your Personalization Efforts by Ditching Tech-First Tendencies

Machine learning, a type of artificial intelligence (AI), can supplement rules and play a powerful role in prioritization and other next-best-action decisions: pulling in everything known about an individual in the channel of engagement and across channels, factoring in data from similar people, and then computing and displaying the optimal, relevant next best action or offer at the 1-to-1 level. Typically, this all occurs in milliseconds faster than you can blink an eye.

Across industries, theres an enormous amount of behavioral data to parse through to uncover trends and indicators of what to do next with any given individual. This can be combined with attribute and transaction data to build a rich profile and predictive intelligence. Machine-learning algorithms automate this process, make surprising discoveries and keep learning based on ever-growing data: from studying both the individual customer and customers with similar attributes and behaviors, and from learning from how customers are reacting to the actions being suggested to them.

In addition, when multiple promotions or next actions are valid, you can apply machine learning to decide on and display the truly optimal one, balancing whats best for the customer with whats best for your business.

Optimized machine-learning-driven next best actions outperform manual ones, even when what they suggest might seem counter-intuitive. For example, a banking institution might promote its most popular cash-back credit card offer to all new site visitors. But for return visitors located in colder climate regions, a continuous learning algorithm might determine that the banks travel rewards card offer performs much better. Only machine learning can pick up on behavioral signals and information at scale (including seemingly unimportant information) in a way that humans simply cannot.

Related Article: 5 Drivers of Personalized Experiences: A Walk Through the AI Food Chain

Determining and displaying next best actions involve integrations and interplay across channels. One system is informing another of an action a customer has taken and what to do next. For example: a customer who joined the loyalty program could be eligible to receive a certain promotion in their email. Or a shopper who browsed purses online can be push-notified a coupon code to use in-store, thanks to beacon technology. An alert might get triggered to a call center agent based on a customers unfinished loan application letting the agent know to provide information on interest rates or help set up an appointment at the customers local branch as that person is calling in.

Given the wide range of activity and vast quantities of data, its important to have a single system that can arbitrate all these actions, apply prioritization and act as the central brain. This helps keep customer information unified and up-to-date, and aids in real-time interaction management and experience delivery.

In the end, everything organizations do when communicating and relating to their customers could be viewed as next best actions. In fact, personalization and next best actions are closely intertwined, as two sides of the same coin. Its hard to separate a next best action from the personalization decisioning driving it, which is why the two areas should be (and sometimes are) tied together from a strategy and systems perspective.

By effectively determining and triggering personalized next steps, you can tell a cohesive and consistent cross-channel story that bolsters brand perception, improves the buyer journey and turns next best actions into must-take ones.

Karl Wirth is the CEO and co-founder of Evergage, a Salesforce Company and a leading real-time personalization and interaction management platform provider. Karl is also the author of the award-winning book One-to-One Personalization in the Age of Machine Learning.

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The Power of AI in 'Next Best Actions' - CMSWire

By Davide Zilli, Client Services Director at Mind Foundry

Today in so many industries, from manufacturing and life sciences to financial services and retail, we rely on algorithms to conduct large-scale machine learning analysis. They are hugely effective for problem-solving and beneficial for augmenting human expertise within an organization. But they are now under the spotlight for many reasons and regulation is on the horizon, with Gartner projecting four of the G7 countries will establish dedicated associations to oversee AI and ML design by 2023. It remains vital that we understand their reasoning and decision-making process at every step.

Algorithms need to be fully transparent in their decisions, easily validated and monitored by a human expert. Machine learning tools must introduce this full accountability to evolve beyond unexplainable black box solutions and eliminate the easy excuse of the algorithm made me do it!"

Bias can be introduced into the machine learning process as early as the initial data upload and review stages. There are hundreds of parameters to take into consideration during data preparation, so it can often be difficult to strike a balance between removing bias and retaining useful data.

Gender for example might be a useful parameter when looking to identify specific disease risks or health threats, but using gender in many other scenarios is completely unacceptable if it risks introducing bias and, in turn, discrimination. Machine learning models will inevitably exploit any parameters such as gender in data sets they have access to, so it is vital for users to understand the steps taken for a model to reach a specific conclusion.

Removing the complexity of the data science procedure will help users discover and address bias faster and better understand the expected accuracy and outcomes of deploying a particular model.

Machine learning tools with built-in explainability allow users to demonstrate the reasoning behind applying ML to a tackle a specific problem, and ultimately justify the outcome. First steps towards this explainability would be features in the ML tool to enable the visual inspection of data with the platform alerting users to potential bias during preparation and metrics on model accuracy and health, including the ability to visualize what the model is doing.

Beyond this, ML platforms can take transparency further by introducing full user visibility, tracking each step through a consistent audit trail. This records how and when data sets have been imported, prepared and manipulated during the data science process. It also helps ensure compliance with national and industry regulations such as the European Unions GDPR right to explanation clause and helps effectively demonstrate transparency to consumers.

There is a further advantage here of allowing users to quickly replicate the same preparation and deployment steps, guaranteeing the same results from the same data particularly vital for achieving time efficiencies on repetitive tasks. We find for example in the Life Sciences sector, users are particularly keen on replicability and visibility for ML where it becomes an important facility in areas such as clinical trials and drug discovery.

There are so many different model types that it can be a challenge to select and deploy the best model for a task. Deep neural network models, for example, are inherently less transparent than probabilistic methods, which typically operate in a more honest and transparent manner.

Heres where many machine learning tools fall short. Theyre fully automated with no opportunity to review and select the most appropriate model. This may help users rapidly prepare data and deploy a machine learning model, but it provides little to no prospect of visual inspection to identify data and model issues.

An effective ML platform must be able to help identify and advise on resolving possible bias in a model during the preparation stage, and provide support through to creation where it will visualize what the chosen model is doing and provide accuracy metrics and then on to deployment, where it will evaluate model certainty and provide alerts when a model requires retraining.

Building greater visibility into data preparation and model deployment, we should look towards ML platforms that incorporate testing features, where users can test a new data set and receive best scores of the model performance. This helps identify bias and make changes to the model accordingly.

During model deployment, the most effective platforms will also extract extra features from data that are otherwise difficult to identify and help the user understand what is going on with the data at a granular level, beyond the most obvious insights.

The end goal is to put power directly into the hands of the users, enabling them to actively explore, visualize and manipulate data at each step, rather than simply delegating to an ML tool and risking the introduction of bias.

The introduction of explainability and enhanced governance into ML platforms is an important step towards ethical machine learning deployments, but we can and should go further.

Researchers and solution vendors hold a responsibility as ML educators to inform users of the use and abuses of bias in machine learning. We need to encourage businesses in this field to set up dedicated education programs on machine learning including specific modules that cover ethics and bias, explaining how users can identify and in turn tackle or outright avoid the dangers.

Raising awareness in this manner will be a key step towards establishing trust for AI and ML in sensitive deployments such as medical diagnoses, financial decision-making and criminal sentencing.

AI and machine learning offer truly limitless potential to transform the way we work, learn and tackle problems across a range of industriesbut ensuring these operations are conducted in an open and unbiased manner is paramount to winning and retaining both consumer and corporate trust in these applications.

The end goal is truly humble, honest algorithms that work for us and enable us to make unbiased, categorical predictions and consistently provide context, explainability and accuracy insights.

Recent research shows that 84% of CEOs agree that AI-based decisions must be explainable in order to be trusted. The time is ripe to embrace AI and ML solutions with baked in transparency.

About the author:

Davide Zilli, Client Services Director at Mind Foundry

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Originally posted here:
Keeping Machine Learning Algorithms Humble and Honest in the Ethics-First Era - Datamation

If you are a major manufacturer in 2020 and you have employed the likes of Deloitte, McKinsey or PWC, it is safe to assume that they have advised you to invest big in artificial intelligence and machine learning.

According to reports by Deloitte and McKinsey, machine learning improves product quality and has the potential to double cash flow. Lets take a look at three global manufacturers who are already on board.

SEE ALSO: Introduction to machine learning in Node.js

Siemens is the largest industrial manufacturer in Europe, and whether they are putting together planes, trains or automobiles, their goal is to solve production challenges efficiently and sustainably. One of the ways they are able to do this is by using machine learning (ML) to enhance additive manufacturing, otherwise known as AM.

The process involves putting together parts that make objects from 3D model data. The idea is to streamline the manufacturing process into one printing stage. Machine learning plays a crucial part in achieving this goal.

Lets take a look at the recent creation of the AM Path Optimizer, part of its NX software offering. Its designed to eliminate overheating during production, an issue that stands in the way of the industrialization of AM. According to Siemens, the path optimizer combines simulation technology and ML to analyze a full job file minutes before execution on the machine. With this they hope to achieve reduced scrap and increased production yields. In short, they want to minimize trial and error and get it right the first time around.

Although still in the beta stage, the AM Path Optimizer has had some early adopters. TRUMPF, a German industrial machine manufacturing company based in Stuttgart, has been singing its praises, pointing to improved geometrical accuracy, more homogenous surface quality and a significant reduction in the scrap rate expected.

Machine learning and artificial intelligence do not just influence how companies manufacture but also help them decide what they manufacture. American packaged-food company ConAgra is one such company. They are using AI to identify consumer preferences.

The vegan market, for example, is growing rapidly: by 2026 it is projected to be worth just over $24 billion (the vegan cheese market alone will be worth $4 billion). And ConAgra, despite being over a century old, is aware of consumer preferences moving towards healthier options and away from things like processed meat. This awareness comes in part from their AI platform, which analyses data from social media and consumer food purchasing behavior.

This has led the company to produce alternative meat products like veggie burgers and even cauliflower rice. Its also helped speed up the manufacturing process, so rather than planning for next year, they can design, make, and release a new product in as little as a few weeks.

The major appliance manufacturer Bosch is a great believer in AI and has committed substantial resources to making it a central part of its business. In 2016, it launched a $30,000 competition on Kaggle, an online community of data scientists and machine learning practitioners. Competitors were asked to predict internal failures, with the aim of improving Bosch production line performance.

They described the assembly process as much like a souffle, delicious, delicate and a challenge to prepare; if it comes out of the oven sunken, you are going to retrace your steps to see where things went wrong. In order to identify and predict where its souffles go wrong, Bosch records data at every step of the manufacturing process and assembly line.

This is where the Kagglers come in. With access to advanced data analytics and using thousands of tests and measurements for each component on the assembly line, the winners Ash and Beluga were able to so solve internal failures using their own fault detection method.

In 2017, the Bosch Center for AI was founded with the tagline Solutions created for life. This is part of a broader effort to put AI and machine learning at the heart of the business. What they are working on now is reducing reliance on human expert knowledge base and deploying AI algorithms in safety-critical applications.

More recently, Bosch has been working on preventing increasingly advanced hackers from compromising their cars. According to CTO Michael Bolle: In the area of machine learning and AI, products and machines learn from data, and so the data itself can be part of the attack surface.

SEE ALSO: How machine learning is changing business communications

What Bosch, ConAgra, and Siemens realize is that their business is increasingly reliant on data, and the best way to harness that data is to invest heavily in AI and ML. According to McKinsey, not investing in AI or ML is not really an option, especially if you are a manufacturer with heavy assets: Manufacturers with heavy assets that are unable to read, interpret, and use their own machine-generated data to improve performance by addressing the changing needs of customers and suppliers will quickly lose out to their competitors or be acquired.

See more here:
3 global manufacturing brands at the forefront of AI and ML - JAXenter

With businesses everywhere being disrupted by the coronavirus outbreak, it seems like a tough time to be an entrepreneur starting a new venture.

Yet the co-founders of the Richmond-based startup company Forestry Machine Learning say they are keeping a positive long-term outlook.

The startup specializes in helping clients implement a cutting-edge type of artificial intelligence called machine learning to improve their business strategies and operations, and the co-founders say they foresee demand only increasing for that service.

It is an interesting time to be launching a company, said David Der, the startups CEO. Co-founder Brian Forrester is chief revenue officer.

Overall, I am optimistic, Der said. Sure, there might be some setbacks nobody is really taking in-person meetings right now but a lot of the value we can deliver can be done virtually anyway.

Our sales strategy remains the same, he said. We are still prospecting and in business development stages, full speed ahead.

Machine learning is a subset of artificial intelligence that involves using computer algorithms to quickly analyze large amounts of data and learn from it. The tools can be used to make better predictions about how people and systems behave.

The Forestry part of the companys name is a nod to lingo within the artificial intelligence industry.

Machine learning, artificial intelligence, and the larger ecosystem around that, is really just coming of age, said Forrester, who is also co-founder of Workshop Digital, a Richmond-based digital marketing firm where he continues to work.

For the last three or four years, we have had access to more data than we have ever had before, Forrester said. Computing power has caught up to be able to process that. A lot of the companies I work with over 100 companies across the U.S. and Canada are still trying to figure out how to leverage that data to inform business strategy, reduce risk and increase profitability.

Machine learning can be used to improve financial forecasting, cybersecurity and fraud prevention, among other things, said Der, who brings to the startup a background in computer science.

Der was among a group of co-founders of Notch, a technology consulting company founded in Richmond in 2014 that specialized in data engineering and machine learning. In late 2017, Notch was acquired by financial services giant Capital One Financial Corp.

Der said he left Capital One in December after a two-year commitment and started working on creating the new business.

Entrepreneurship is really a passion of mine, Der said. In a way, we are picking up the torch where Notch left off two years ago. I also want to bring to the table my experience now from the financial services industry.

While machine learning can be utilized by many organizations, Der said the startup is targeting three primary industries: financial services, health care and digital marketing.

The goal of machine learning in digital marketing is to deliver the right message to the right person through the right medium at the right time, Der said.

Forrester brings deep experience in digital marketing through his company, Digital Workshop.

I have spent 11 years building a company, and we have been fairly successful, Forrester said. My role in this company [Forestry] is to build our sales and marketing strategy as we grow and follow Davids lead.

Will Loving and Scott Walker, both with Richmond-based Consult360, also are investing partners in the startup.

Forrester said he has experience navigating a startup during a time of economic disruption.

I dont think the problems that machine learning is trying to solve are going to go away just because of this, he said, referring to the coronavirus disruptions. In fact, they are more pervasive now than ever. Leveraging more computing power to tackle bigger problems is not going to go away.

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Startup Spotlight: Forestry Machine Learning wants to help clients use artificial intelligence to improve business - Richmond.com

Deep learning chips are customized Silicon chips that integrate AI technology and machine learning. Deep learning and machine learning, which are the sub-sets of Artificial Intelligence (AI) sub-sets, are used in carrying out AI related tasks.

New York, March 20, 2020 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Global Deep Learning Chipset Market By type By Technology By End User By Region, Industry Analysis and Forecast, 2019 - 2025" - https://www.reportlinker.com/p05876895/?utm_source=GNW Deep learning technology has entered many industries around the world and is accomplished through applications like computer vision, speech synthesis, voice recognition, machine translation, drug discovery, game play, and robotics.

The widespread adoption of artificial intelligence (AI) for practical business applications has brought in a range of complexities and risk factors in virtually every industry, but one thing is certain: in todays AI industry, hardware is the key to solving many of the main problems facing the sector, and chipsets are at the heart of that hardware solution. Considering AIs widespread applicability, its almost certain that every chip will have some kind of AI system embedded in future. The engine could make a wide range of forms, from a basic AI library running on a CPU to more complex, custom hardware. The potential for AI is better fulfilled when the chipsets are designed to provide the adequate amount of computing capacity for different AI applications at the right power budget. This is a trend that leads to increased specialization and diversifying of AI-optimized chipsets.

The factors influencing the development of the deep learning chipset market are increased acceptance of cloud-based technology and profound use of learning in big data analytics. A single-chip processor generates lighting effects and transforms objects each time a 3D scene is redrawn, or a graphic processing unit turns out to be very meaningful and efficient when applied to computation styles needed for neural nets. This in turn fuels the growth of the market for deep learning chipsets.

Based on type, the market is segmented into GPU, ASIC, CPU, FPGA and Others. Based on Technology, the market is segmented into System-on-chip (SoC), System-in-package (SIP) and Multi-chip module & Others. Based on End User, the market is segmented into Consumer Electronics, Industrial, Aerospace & Defense, Healthcare, Automotive and Others. Based on Regions, the market is segmented into North America, Europe, Asia Pacific, and Latin America, Middle East & Africa.

The major strategies followed by the market participants are Product Launches. Based on the Analysis presented in the Cardinal matrix, Google, Inc., Microsoft Corporation, Samsung Electronics Co., Ltd., Intel Corporation, Amazon.com, Inc., and IBM Corporation are some of the forerunners in the Deep Learning Chipset Market. Companies such as Advanced Micro Devices, Inc., Qualcomm, Inc., Nvidia Corporation, and Xilinx, Inc. are some of the key innovators in Deep Learning Chipset Market. The market research report covers the analysis of key stake holders of the market. Key companies profiled in the report include Samsung Electronics Co., Ltd. (Samsung Group), Microsoft Corporation, Intel Corporation, Nvidia Corporation, IBM Corporation, Google, Inc., Amazon.com, Inc. (Amazon Web Services), Qualcomm, Inc., Advanced Micro Devices, Inc., and Xilinx, Inc.

Recent strategies deployed in Deep Learning Chipset Market

Partnerships, Collaborations, and Agreements:

Jan-2020: Xilinx collaborated with Telechips, a leading Automotive System on Chip (SoC) supplier. The collaboration would provide a comprehensive solution for addressing the integration of in-cabin monitoring systems (ICMS) and IVI systems.

Dec-2019: Samsung Electronics teamed up with Baidu, a leading Chinese-language Internet search provider. Under the collaboration, the companies announced that the development of Baidu KUNLUN, its first cloud-to-edge AI accelerator has been completed. KUNLUN chip provides 512 gigabytes per second (Gbps) memory bandwidth and offers up to 260 Tera operations per second (TOPS) at 150 watts.

Oct-2019: Microsoft announced technology collaboration with Nvidia, a technology company. The collaboration was focused on intelligent edge computing, which is designed for helping the industries in gaining and managing the insights from the data created by warehouses, retail stores, manufacturing facilities, urban infrastructure, connected buildings, and other environments.

Oct-2019: Microsoft launched Lakefield, a dual-screen device powered by Intels unique processor. This device combines a hybrid CPU with Intels Foveros 3D packaging technology. This provides more flexibility to device makers for innovating designs, experience, and form factor.

Jun-2019: AMD came into partnership with Samsung following which, the former company is licensing its graphics technology to Samsung for use in future mobile chips. Under this partnership, Samsung paid AMD for getting access to its RDNA graphics architecture.

Jun-2019: Nvidia collaborated with Volvo for developing artificial intelligence that is used in self-driving trucks.

May-2019: Samsung Electronics came into partnership with Efinix, an innovator in programmable product platforms and technologies. Under this partnership, the companies were aimed at developing Quantum eFPGAs on Samsungs 10nm silicon process.

Dec-2018: IBM extended its partnership with Samsung for developing 7-nanometer (nm) microprocessors for IBM Power Systems, LinuxONE, and IBM Z. The expansion was aimed at driving the performance of the unmatched system including encryption and compression speed, acceleration, memory, and I/O bandwidth, as well as system scaling.

Jun-2018: AWS announced its collaboration with Cadence Design Systems. The collaboration was aimed at delivering a Cadence Cloud portfolio to electronic systems and semiconductor design.

Mar-2018: Nvidia came into partnership with Arm for bringing deep learning interface to billions of consumer electronics, mobile, and Internet of Things devices.

Acquisition and Mergers:

Aug-2019: Xilinx took over Solarflare, a provider of high-performance, low latency networking solutions. The acquisition helped in generating more revenues and enabled new marketing and R&D funds for the future.

Apr-2019: Intel completed the acquisition of Omnitek, a provider of video and vision field-programmable gate array (FPGA). Through the acquisition, the FPGA processor business of the company has been doubled.

Jul-2018: Intel took over eASIC, a fabless semiconductor company. The acquisition bolstered the companys business in providing chips.

Apr-2017: AMD acquired Nitero, a company engaged in providing technology to connect VR headsets wirelessly to PCs. The acquisition helped the company in getting control over VR experiences.

Product Launches and Product Expansions:

Dec-2019: Nvidia launched Drive AGX Orin, a new Orin AI processor or system-on-chip (SoC). This processor improves power efficiency and performance. This processor is used in evolving the automotive business.

Dec-2019: AWS unveiled Graviton2, the next-generation of its ARM processors. It is a custom chip that is designed with 7nm architecture and based on 64-bit ARM Neoverse cores.

Nov-2019: AMD launched two new Threadripper 3 CPUs with 24 and 32 cores. Both these CPUs will be integrated into AMDs new TRX40 platform using the new sTRX4 socket.

Nov-2019: Intel unveiled Ponte Vecchio GPUs, a graphics processing unit (GPU) architecture. This chip was designed for handling the artificial intelligence loads and heavy data in the data center.

Nov-2019: Intel launched Stratix 10 GX 10M, a new FPGA. This consists of two large FPGA dies and four transceiver tiles and has a total of 10.2 million logic elements and 2304 user I/O pins.

Oct-2018: Google launched TensorFlow, the popular open-source artificial intelligence framework. This framework runs deep learning, machine learning, and other predictive and statistical analytics workloads. This simplifies training models, the process of acquiring data, refining future results, and serving predictions.

Sep-2019: AWS released Amazon EC2 G4 GPU-powered Amazon Elastic Compute Cloud (Amazon EC2) instances. It delivers up to 1.8 TB of local NVMe storage and up to 100 Gbps of networking throughput to AWS custom Intel Cascade Lake CPUs and NVIDIA T4 GPUs.

Aug-2019: Xilinx released Virtex UltraScale+ VU19P, a 16nm device with 35 billion transistors. It has four chips on an interposer. It is the worlds largest field-programmable gate array (FPGA) and has 9 million logic cells.

May-2019: Nvidia introduced NVIDIA EGX, an accelerated computing platform. This platform was aimed at allowing the companies in performing low-latency AI at the edge for perceiving, understanding, and acting in real-time on continuous streaming data between warehouses, factories, 5G base stations, and retail stores.

Nov-2018: AWS introduced Inferentia and Elastic Inference, two chips and 13 machine learning capabilities and services. Through these launches, the company aimed towards attracting more developers.

Sep-2018: Qualcomm unveiled Snapdragon Wear 3100 chipset. This chipset is used in smartwatches and has extended battery life.

Aug-2018: AMD introduced B450 chipset for Ryzen processors. The chip runs about 2 watts lower in power than B350 chipset.

Jul-2018: Google introduced Tensor Processing Units or TPUs, the specialized chips. This chip lives in data centers of the company and simplifies the AI tasks. These chips are used in enterprise jobs.

Apr-2018: Qualcomm launched QCS605 and QCS603 SoCs, two new system-on-chips. These chips combine image signal processor, CPU, AI, GPU technology for accommodating several camera applications, smart displays, and robotics.

Scope of the Study

Market Segmentation:

By Compute Capacity

High

Low

By Type

GPU

ASIC

CPU

FPGA

Others

By Technology

System-on-chip (SoC)

System-in-package (SIP)

Multi-chip module & Others

By End User

Consumer Electronics

Industrial

Aerospace & Defense

Healthcare

Automotive

Others

By Geography

North America

o US

o Canada

o Mexico

o Rest of North America

Europe

o Germany

o UK

o France

o Russia

o Spain

o Italy

o Rest of Europe

Asia Pacific

o China

o Japan

o India

o South Korea

o Singapore

o Malaysia

o Rest of Asia Pacific

LAMEA

o Brazil

o Argentina

o UAE

o Saudi Arabia

o South Africa

o Nigeria

o Rest of LAMEA

Companies Profiled

Samsung Electronics Co., Ltd. (Samsung Group)

Microsoft Corporation

Intel Corporation

Nvidia Corporation

IBM Corporation

Google, Inc.

Amazon.com, Inc. (Amazon Web Services)

Qualcomm, Inc.

Advanced Micro Devices, Inc.

More:
The Global Deep Learning Chipset Market size is expected to reach $24.5 billion by 2025, rising at a market growth of 37% CAGR during the forecast...

Engineers at the AMRC have researched the use of the cloud to capture data from machine tools with Tier 2 member Amido

Cloud data solutions being trialled at the University of Sheffield Advanced Manufacturing Research Centre (AMRC) could provide a secure and cost-effective way for SME manufacturers to explore how machine learning and Industry 4.0 technologies can boost their productivity.

Jon Stammers, AMRC technical fellow in the process monitoring and control team, says: Data is available on every shopfloor but a lot of time it isnt being captured due to lack of connectivity, and therefore cannot be analysed. If the cloud can capture and analyse that data then the possibilities are massive.

Engineers in the AMRCs Machining Group have researched the use of the cloud to capture data from machine tools with new Tier Two member Amido, an independent technical consultancy specialising in assembling, integrating and building cloud-native solutions.

Mr Stammers adds: Typically we would have a laptop sat next to a machine tool capturing its data; a researcher might do some analysis on that laptop and share the data on our internal file system or on a USB stick. There is a lot of data generated on the shopfloor and it is our job to capture it, but there are plenty of unanswered questions about the analysis process and the cloud has a lot to bring to that.

In the trial, data from two CNC machines in the AMRCs Factory of the Future: a Starrag STC 1250 and a DMG Mori DMU 40 eVo, was transferred to the Microsoft Azure Data Lake cloud service and converted into a parquet format, which allowed Amido to run a series of complex queries over a long period of time.

Steve Jones, engagement director at Amido, explains handling those high volumes of data is exactly what the cloud was designed for: Moving the data from the manufacturing process into the cloud means it can be stored securely and then structured for analysis. The data cant be intercepted in transit and it is immediately encrypted by Microsoft Azure.

Security is one of the huge benefits of cloud technology, Mr Stammers comments. When we ask companies to share their data for a project, it is usually rejected because they dont want their data going offsite. Part of the work were doing with Amido is to demonstrate that we can anonymise data and move it off site securely.

In addition to the security of the cloud, Mr Jones says transferring data into a data lake means large amounts can be stored for faster querying and machine learning.

One of the problems of a traditional database is when you add more data, you impact the ability for the query to return the answers to the questions you put in; by restructuring into a parquet format you limit that reduction in performance. Some of the queries that were taking one of the engineers up to 12 minutes to run on the local database, took us just 12 seconds using Microsoft Azure.

It was always our intention to run machine learning against this data to detect anomalies. A reading in the event data that stands out may help predict maintenance of a machine tool or prevent the failure of a part.

Storing data in the cloud is extremely inexpensive and that is why, according to software engineer in the process monitoring and control team Seun Ojo, cloud technology is a viable option for SMEs working with the AMRC, part of the High Value Manufacturing (HVM) Catapult.

He says: SMEs are typically aware of Industry 4.0 but concerned about the return on investment. Fortunately, cloud infrastructure is hosted externally and provided on a pay-per-use basis. Therefore, businesses may now access data capture, storage and analytics tools at a reduced cost.

Mr Jones adds: Businesses can easily hire a graphics processing unit (GPU) for an hour or a quantum computer for a day to do some really complicated processing and you can do all this on a pay-as-you-go basis.

The bar to entry to doing machine learning has never been lower. Ten years ago, only data scientists had the skills to do this kind of analysis but the tools available from cloud platforms like Microsoft Azure and Google Cloud now put a lot of power into the hands of inexpert users.

Mr Jones says the trials being done with Amido could feed into research being done by the AMRC into non-geometric validation.

He concludes: Rather than measuring the length and breadth of a finished part to validate that it has been machined correctly; I want to see engineers use data to determine the quality of a job.

That could be really powerful and if successful would make the process of manufacturing much quicker. That shows the value of data in manufacturing today.

AMRCwww.amrc.co.uk

Amidowww.amido.com

Michael Tyrrell

Digital Coordinator

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Proof in the power of data - PES Media

CampaignTester was awarded Best Application of Artificial Intelligence to Optimize Creative at the 2020 Campaigns & Elections Reed Awards.

CampaignTester is a cutting-edge mobile-based platform that utilizes emotion analytics and machine learning to detect a users emotion and engagement level while watching video content. Their proprietary platform aims to deliver key audience insights for organizations to validate, revise and perfect their video content messaging.

Campaigns & Elections Reed Award winners represent the best-of-the-best in the political campaign and advocacy industries. The 2020 Reed Awards honored winners across 16 distinct category groups, representing the different specialisms of the political campaign industry, with distinct category groups for International (non-US) work, and Grassroots Advocacy work.

It was particularly meaningful being recognized among some of the finest marketers and technologists in the world. Bill Lickson, CampaignTesters Chief Operating Officer affirmed. I was thrilled and honored to accept this prestigious award on behalf of our entire talented team.

Aaron Itzkowitz, Chief Executive Officer and Founder of CampaignTester added, This award is a great start to what looks to be a wonderful year for our client-partners and our company. While our technology was recognized for excellence in political marketing, our technology is for any industry that uses video in marketing

If you have an interesting article / experience / case study to share, please get in touch with us at [emailprotected]

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Express Computer is one of India's most respected IT media brands and has been in publication for 24 years running. We cover enterprise technology in all its flavours, including processors, storage, networking, wireless, business applications, cloud computing, analytics, green initiatives and anything that can help companies make the most of their ICT investments. Additionally, we also report on the fast emerging realm of eGovernance in India.

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Innovative AI and Machine-Learning Technology That Detects Emotion Wins Top Award - Express Computer