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Finally, AI is ready for the mainstream.

When your enterprise is handling transactions between 25 million sellers and 182 millionbuyers, supporting 1.5 billion listings, manual decision-making processes just wont cut. Such is the case with eBay, the mega commerce site, that has been employing artificial intelligence for more than a decade. As Forbes contributor Bernard Marr points out, eBay employs AI across a broad range of functions, in personalization, search, insights, discovery and its recommendation systems along with computer vision, translation, natural language processing and more.

As part of a massive operation with so much experience with AI, Mazen Rawashdeh, CTO of eBay, has plenty to say about the current state of enterprise AI. He recently shared his views on AIs progress across the business landscape, and where work is still needed.

How far has AI moved beyond proofs of concept?

Rawashdeh: The technology behind AI has progressed way beyond proofs of concept in many organizations. AI is at the front and center of technology strategy and execution, driving compelling customer experiences, improving business growth, and managing and reducing risk across almost every industry finance, healthcare, transportation, security, e-commerce. In a way, it is beginning to touch several aspects of human life in a practical manner. Computer vision, natural language processing, recommender systems, and anomaly detection capabilities, for example, are fundamentally shaping the future of commerce in general, and e-commerce in particular.

Is AI being narrowly applied to specific tasks, or are there broader applications underway?

Rawashdeh: AI is currently being applied both wide and deep across industries. For example, solutions are deployed in production at scale for specific tasks such as language translations, intelligent searches, personalized experiences, fraud detections, recommender systems, across e-commerce industries.

These are foundational capabilities and quickly becoming table stakes; however, AI is emerging and aspiring to have broader applications when it is leveraged to augment human tasks. For example, a combination of AI and human evaluation is being used for fraud detection of prohibited and counterfeit items in the e-commerce industry. As AI is deployed to manage more human tasks, it raises the critical policy, regulatory and ethical considerations that need to evolve as well.

What are the structural roadblocks that inhibit AI efforts and utilization?

Rawashdeh: In order to democratize AI in an enterprise, there has to be an effective and efficient enterprise-to-enterprise machine learning platform that helps the full machine learning lifecycle along with providing higher level AI services, including computer vision, natural language processing and personalization, in easy-to-use modalities. Building these capabilities and services is not an easy undertaking and requires a strong commitment of support from executive leadership, along with an internal open source engineering model and the mindset to develop it collaboratively.

The fundamental roadblocks to successful adoption of AI at the enterprise level is as much about culture as it is about technology. Companies that establish a culture where AI is blended as part of the unified strategy, design and development process, have a higher chance of successful adoption of AI, and in turn, a greater return from that AI. When AI is thought of an ecosystem across the organization business, policy, product, technology, experience then the ROI can be maximized.

What kind of infrastructure is providing the best support for broader AI initiatives at the enterprise level?

Rawashdeh: There are three key pillars to build successful AI initiatives in any enterprise from a hardware and software infrastructure perspective.

First is to have an easy discoverability, transformation and cleaning framework for data;

second is to have an extensive high-performance compute, storage, network to train, validate, and deploy complex machine learning and deep learning AI models; and

third is the availability of a control plane for AI that includes various software frameworks and utilities for end-to-end management of AI modeling lifecycle from exploration, training, experimentation, learning and iteration.

What changes are required within the data infrastructure to support scaling AI?

Rawashdeh: Data infrastructure and the teams and processes behind scaling AI need to provide a data as service type capability for any successful deployment. This enables data scientists and developers in an enterprise to discover, create, manage, deploy and share best-of-breed data features in a quick and seamless self-service manner.

To support AI scaling, the data infrastructure should look beyond traditional data warehouses or extract transform load, to provide simplistic and appropriate AI specific abstractions for data discovery, data preparation, model training and serving. For AI to be effective, the infrastructure should provide data for models in batch as well as real-time.

Most importantly, AI is an iterative, continuous learning process, requiring automated and continuous feedback data for model iterations. The data infrastructure should evolve to support such a continuous feedback cycle from AI systems and human-in-the-loop.

Read the rest here:
Artificial Intelligence Is Ready For Prime Time, But Needs Full Executive Support - Forbes

Rokt's advanced machine learning technology enables users to deliver the next best action and experience for each customer in the Transaction Moment. The Rokt algorithms analyze over 1 billion transactions per year, getting smarter with each customer interaction. Clients can then optimize and personalize their offers in real time as the AI manages the tradeoffs between objectives, choosing the offers that will drive the most value per transaction. Customers receive the most relevant offers that they are likely to engage with, and companies forge deeper relationships with their clients, acquire new buyers, and generate new revenue opportunities.

"We are constantly looking to improve the accuracy of our models through the addition of new features from internal and third-party sources," said Rokt CEO, Bruce Buchanan. "Our machine learning takes features into account to predict and deliver the most value to both our clients, and their customers. This technology allows non-tech savvy teams the ability to utilize advanced machine learning algorithms, analyze results, and make simple updates to optimize their campaign success. It ensures customers are receiving relevant offers, and a personalized experience, and that brands are not wasting marketing spend by showing offers to users unlikely to convert."

Rokt's Machine Learning includes 12 unique proprietary models that cover both classification problems, where the outcome is yes or no, and regression problems, where the outcome has a continuous value. Multiple data inputs are collected to understand each customer on an individual level so AI can determine their most relevant offer and experience. These data points include customer data such as age, gender, device, and more, as well as transaction data and Interaction data. With this, Rokt then optimizes to serve the most relevant offers. Customization features include placements, design, messaging, creative, offer position, and more.

The mission of the MarTech Breakthrough Awards is to honor excellence and recognize the innovation, hard work and success in a range of marketing, sales and advertising technology-related categories, including marketing automation, market research and customer experience, AdTech, SalesTech, marketing analytics, content and social marketing, mobile marketing and many more. This year's program attracted more than 2,750 nominations from over 15 different countries throughout the world.

"Customers expect personalized and relevant experiences when they shop online, Rokt's AI brings them this customized experience," said James Johnson, Managing Director at MarTech Breakthrough. "Rokt certainly breaks through the MarTech space with their advanced machine learning technology that has been used across more than 4 billion e-commerce transactions to date, and we want to congratulate them on winning our 'Best Use of AI in MarTech' award for 2020."

About Rokt

Rokt makes e-commerce smarter, faster and better. When customers are buying online, they increasingly expect more personalized and relevant experiences. Rokt uses real-time data and decisioning to deliver the next best action for each person in each Transaction Moment.

Founded in Sydney, Rokt now operates in the US, Canada, UK, France, Germany, Australia, New Zealand, Singapore, The Netherlands, Spain, Japan, Ireland, Sweden, Norway, Denmark, and Finland. Our clients include Live Nation, Staples, Groupon, GoDaddy, Expedia, Vistaprint and HelloFresh. Rokt unlocks the hidden potential in every single Transaction Moment.

About MarTech Breakthrough

Part of Tech Breakthrough, a leading market intelligence and recognition platform for global technology innovation and leadership, the MarTech Breakthrough Awards program is devoted to honoring excellence in marketing, ad and sales technology companies, products and people. The MarTech Breakthrough Awards provide a platform for public recognition around the achievements of breakthrough marketing technology companies and products in categories including marketing automation, AdTech, SalesTech, marketing analytics, CRM, content and social marketing, website, SEM, mobile marketing and more. For more information, visitMarTechBreakthrough.com.

SOURCE Rokt

https://rokt.com/

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Rokt Recognized for Innovation in Artificial Intelligence with 2020 MarTech Breakthrough Award - PRNewswire

U.S. Chief Technology Officer Michael Kratsios and Energy Secretary Dan Brouillette shed a little light on how the Energy Department and Trump administration are thinking about ethics, regulatory approaches, and broader societal implications as they push the rollout of artificial intelligence and other emerging technologies.

During a fireside chat in Pittsburgh Tuesday, Brouillette reflected on similar-but-as-serious considerations previously made when the agency was developing nuclear technologies many years ago. He noted that now, when focusing on ethics, his mind tends to hone in on negative aspects and bad results that could arise with tech adoption.

I haven't thought this through with great depth, but there seems to be some positive aspects of AI, too, on the ethics front that we need to explore, Brouillette told the chats moderator Carnegie Mellon University Vice President of Research Michael McQuade. And perhaps through that process we can speed the adoption of some of these technologies, he said, adding that hed like to give it all more thought.

Piggybacking off the point, Kratsios noted that while there's often a tendency to immediately start looking at the lenses of the negative, government officials should conduct a trade-off analysis in their tech-driven pursuits. President Trump signed an executive order on the American AI Initiative earlier in his term, he said, which called for a set of regulatory guidelines for agencies to lean on when implementing or overseeing the use of AI-powered technologies.

So, think about the [Food and Drug Administration] approving an AI medical diagnostic, or think about [the Federal Aviation Administration] approving a droneand what they should be considering in their regulatory approach, Kratsios explained.

A draft of the first set of regulatory guidelines was released earlier this year, which at the time were deemed by administration officials to make up a light-touch regulatory approach.

I think one of the core underpinnings of the way that the White House is directing agencies to think about this is to do that actual cost-benefit analysis, Kratsios said. The same cost-benefit analysis that is required by statute for any other regulation should also be done in the context of AI.

Noting that its something that is very hard to do, the CTO articulated that the guidelines would help provide clarity on how to see the benefits that these technologies can provide, weighed against some of the potential risks to ultimately create better regulatory solutions to providing the technology to the American public.

Brouillette also pointed out that other agencies such as the Homeland Security and Health and Human Services departments are already applying AI technologies to help find redundancies and duplications, and address other issues within their own regulatory processes. Now, the Energy Department aims to follow suit.

One of the questions that my predecessor asked me, Secretary Rick Perry, was are we going to apply this to ourselves? he said. And I think that's a very important common sense, fundamental first stepbut it's important that we do it as a regulatory agency.

The federal officials also touched on a range of other topics during the conversation, which was one part of several events Energy led in Pennsylvania this week.

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Agencies Should Consider the Pros and Cons of Artificial Intelligence - Nextgov

VODA.aiis pleased to announce we are working with Mueller Water Products to provide machine learning software to power PipeRank virtual condition assessment technology delivered by Echologics.

VODA.ais machine learning engine is designed to deliver remarkably accurate predictions for future water pipe failures, both in the near term (the next twelve months) as well as longer term. Many utilities choose to replace pipes based on pipe material, age and history of prior failures. These methods are significantly less accurate than the PipeRankTMtechnology, powered by VODA.ai.

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Current industry best practice leverages some data to identify trends and generate data-supported decisions for failure planning and capital deployment. The PipeRank technology combines pipe degradation factors with VODA.ais machine learning model to enable utilities to prioritize every pipe segment by likelihood and consequences of failure, saidEric Stacey, Vice President and General Manager of Echologics.

The PipeRankTMtechnology identifies pipes likely to fail in the near future and assigns a business risk score to every segment. With this data, a condition assessment can then be performed using the Echologics ePulsetechnology to diagnose specific problems. This makes it easy for utilities to plan their operating and engineering programs by guiding actions and focusing resources on the highest risk assets.

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The relationship with the Echologics team is exciting for us. Their industry leadership will introduce VODA.ai to more utilities. Via this relationship, we will work with the Echologics team to support smarter decision making and continue to serve the water industry, saidGeorge Demosthenous, CEO at VODA.ai.

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VODA.ai and Mueller Form a Relationship to Support Decision-Making Through Artificial Intelligence - AiThority

Education can be defined as a process where teachers and students give and receive systematic instructions, respectively. Learning can take place in either a formal or informal setting. More commonly, students receive education in a formal setting such as a high-school, college, university, etc. Education is often considered as a significant determinant of an individuals future success rate. Justifiably, there are various efforts to improve the current education systems in multiple countries worldwide.

Among the many methods employed by various countries to improve the education sector includes the use of AI (Artificial intelligence). AI systems are defined by the use of computers to accomplish tasks that had previously required human intellect. AI utilizes algorithms that collect, classify, organize, and analyze information to conclude it, which is also called machine learning. As such, the use of machine learning has the potential to bring about several benefits for the various industries, including the education system.

Traditional education systems are fast changing to adapt to the technological advancements of todays world. This is especially true with the widespread access to various educational sources of information online. The implementation of educational AI systems has the potential to help students develop their skills and acquire more knowledge in multiple subjects. Therefore, as artificial intelligence continues to evolve, it is our hope that it can help fill the gaps in the education system.

The implementation of AI can improve the efficiency and personalization of learning tasks, as well as streamline administrative tasks. These are benefits enjoyed by students and teachers alike. The implementation of artificial intelligence also helps students to get more time with their respective teachers. This is where unique human qualities are required to supplement where AI would struggle.

AI has altered the students way of learning as they do not need to physically attend classes since they have access to learning material via the internet. As previously mentioned, AI allows educators to spend more time with students by taking over some administrative tasks. However, AI has done so much for education. Below are a few more effects of artificial intelligence on the education industry. They include:

Education should be accessible by everyone regardless of their geographical location. Learning through Artificial intelligence has long been considered as the deciding factor for eliminating geographical boundaries through the facilitation of flexible learning environments globally.

The availability of smart content is a highly debated topic, whereby AI systems can be utilized to offer quality content that is similar to what students buy from some of the best research paper writers online.

AI learning environments can adapt to a students level of skill, mastery of coursework, etc. thus identifying the challenges they face. Accordingly, they provide relevant materials and activities to boost your knowledge base in a specific subject.

You probably already realized that most streaming services offer you a list of shows you are probably going to like, which is an excellent example of AI personalization of your favorite genre of shows. Various other systems can be used in education to cater to the different needs of various students.

Teachers often spend time on administrative duties such as marking exams, reading students assignments, planning the timetable, etc. all of which can be completed by AI systems such as automated assignment processing and grading systems. Thus, teachers get to spend more time with their students.

AI usage, at the very least, reduces the chances of human error delaying specific processes in the learning environment. An excellent example of AI used in school is through the collection of data from various sources and the creation of an accurate forecast to plan for the future effectively.

Besides, the system offers opportunities for international students who either speak different languages or have visual/ hearing defects. For instance, an artificial intelligence system that forms captions in real-time during a presentation. As you can see, the education sector has a lot to gain from the implementation of AI into various systems.

AI systems bring about a world of opportunities to share information globally. Today there are quite a few artificial intelligence systems that help provide a conducive learning environment for all students. The use of AI in learning is quite promising and should be exploited for the benefits it has to offer.

Also read: 9 ways Artificial Intelligence (AI) is impacting education

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Impact of Artificial Intelligence on the current education system - Latest Digital Transformation Trends | Cloud News - Wire19