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2020 AI Predictions From IBM, Others See Focus on Performance, IT Measurement


Predictions for AI in 2020 see emphasis on trust, advances in natural language processing and text generation. (GETTY IMAGES)

By AI Trends Staff

IBM Research AI has released its five AI predictions for 2020, in a research blog post from Sriram Raghavan, VP of IBM Research AI. He identified three themes that will shape the advancement of AI in 2020: automation, natural language processing (NLP), and trust. More automation will help AI systems work more quickly and for data scientists, businesses, and consumers. NLP will play a key role in enabling AI systems to converse, debate, and solve problems using everyday language. “And with each of these advances, we’ll see more transparent and accountable practices emerge for managing AI data, through tools ranging from explainability to bias detection,” Raghavan stated.

Sriram Raghavan, VP of IBM Research AI

Among the five IBM Research predictions:

AI will understand more, so it can do more: The more data AI systems have, the faster they will get better. But AI’s need for data can pose a problem for some businesses and organizations that have less data than others. During the coming year, more AI systems will begin to rely on “neuro-symbolic” technology that combines learning and logic. Neuro-symbolic is the ticket to breakthroughs in technologies for NLP, helping computers better understand human language and conversations by incorporating common sense reasoning and domain knowledge.

AI won’t take your job, but it will change how you work: The fear that humans will lose their jobs to machines is unjustified. Rather, AI will transform the way people work, through automation. New research from the MIT-IBM Watson AI Lab shows that AI will increasingly help us with tasks such as scheduling, but will have a less direct impact on jobs that require skills such as design expertise and industrial strategy. Employers have to start adapting job roles, while employees should focus on expanding their skills.

AI will engineer AI for trust: To trust AI, the systems have to be reliable, fair, and accountable. Developers need to ensure that the technology is secure and that conclusions or recommendations are not biased or manipulated.” During 2020, components that regulate trustworthiness will be interwoven into the fabric of the AI lifecycle to help us build, test, run, monitor, and certify AI applications for trust, not just performance,” Raghavan predicted. Researchers will explore the use of AI to govern AI and to create trust workflows across industries, especially those that are heavily-regulated.

Pytorch Founder Sees Model Compiler Advances

Soumith Chintala, director, principal engineer, and creator of PyTorch, offered some 2020 predictions in an account in VentureBeat. He expects “an explosion” in the importance and adoption of tools such as PyTorch’s JIT compiler and neural network hardware accelerators like Glow. “With PyTorch and TensorFlow, you’ve seen the frameworks sort of converge,” he stated. “The reason quantization comes up, and a bunch of other lower-level efficiencies come up, is because the next war is compilers for the frameworks — XLA, TVM, PyTorch has Glow, a lot of innovation is waiting to happen,” he said. “For the next few years, you’re going to see … how to quantize smarter, how to fuse better, how to use GPUs more efficiently, [and] how to automatically compile for new hardware.”

Thus more value will be placed in 2020 on AI model performance and not only accuracy, how output can be explained and how AI can reflect the society people want to build.

Celeste Kidd, a developmental psychologist at the UC Berkeley, says 2020 may spell the end of the “black box” references to neural networks inability to explain themselves. She predicts the end of the perception that neural networks cannot be interpreted. “The black box argument is bogus… brains are also black boxes, and we’ve made a lot of progress in understanding how brains work,” she stated.

Kidd and her team explore how babies learn, seeking insights to help neural network model training. From studying baby behavior, she sees that they understand some things and that they are not perfect learners. “Human babies are great, but they make a lot of errors,” she stated. “It’s likely there’s going to be an increased appreciation for the connection between what you currently know and what you want to understand next.”

Anima Anandkumar, machine learning research director at NVIDIA, sees more advances coming in text generation, noting that in 2019 text generation at the length of paragraphs was made possible, an advance. In August 2019, NVIDIA introduced the Megatron natural language model, with 8 billion parameters, believed to be the world’s largest Transformer-based AI model. She looks forward to seeing more industry-specific text models. “We are still not at the stage of dialogue generation that’s interactive, that can keep track and have natural conversations. So I think there will be more serious attempts made in 2020 in that direction,” she stated to VentureBeat.

Anima Anandkumar, machine learning research director, NVIDIA

She sees this next advance as a technical challenge. “The development of frameworks for control of text generation will be more challenging than, say, the development of frameworks for images that can be trained to identify people or objects,” she stated.

IT Seen Getting Better at Measuring AI’s Impact

Among trends to watch in 2020 cited in an account in  The Enterprisers Project, is a prediction that IT leaders will get real about measuring AI’s impact. A new MIT AI Survey showed fewer than two of five companies reported business gains from AI in the past three years. Given the investments being made in AI, more emphasis will be put on measuring results.

Fewer than two out of five companies reported business gains from AI in the past three years, according to the MIT AI survey. That will need to change in the new year, given the significant investment organizations are continuing to make in AI capabilities. Measurements will be attempted on gains in ease of use, improved processes and customer satisfaction.  “CIOs will also need to continue to put more of their budgets against understanding how AI can benefit their organizations and implement solutions that provide real ROI,” stated Jean-François Gagné, CEO and co-founder of software provider Element AI, “or risk falling behind competitors.”

Read the source posts from IBM Research AI, VentureBeat and  The Enterprisers Project.

Source: AI Trends
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Which Comes First, the AI or the Business Strategy?


To pursue an AI project that may not be able to be deployed, is a path to avoid in the process of aligning AI and business strategy. (GETTY IMAGES)

By John P. Desmond, AI Trends Editor

Companies need to align the AI strategy with the business strategy.

First the company needs a business strategy. Can the AI help with that?

Maybe so. AI is being applied to the model decision-making of governments and corporations. A recent article in Forbes described the Real Time Strategy (RTS) technology involved in Google DeepMind’s gaming software that works with “imperfect information.” RTS is a deep neural network trained on past games and which evolves by playing itself to get better.

“The core of the approach is a system that uses a mash-up of supervised learning and reinforcement learning, but the key concept to absorb is that a complicated RTS game only humans could play is now being solved very comprehensively by an artificial intelligence system,” states author Dan Shapiro, PhD CTO and co-founder of AuditMap.ai, offering compliance AI for the enterprise.

Most AI projects today are limited in scope to model one part of corporate strategy, such as credit risk models, recommender systems, and trading algorithms. “Corporate strategy development is going to benefit from more automation, and specifically from artificial intelligence,” Shapiro states. “Companies are organized a certain way, with specific Key Performance Indicators (KPIs), and plans for improving the KPIs. Expect artificial intelligence to creep into corporate strategy discussions.”

Identify the Business Benefit of the AI Project and Make it Achievable

Beware of Bad AI strategy.

“Bad AI strategies are hype-driven, focus on technology over impact, and employ 2–3 data scientists scrambling for projects. Try to stray away from the latter,” suggests Jan Zawadzki in a recent article in Towards Data Science. Zawadzki is a management consultant and data scientist currently working as a Project Lead Data & AI for Carmeq GmbH, the innovation arm of Volkswagen AG.

Each business will have its own AI strategy that might not look like another company’s AI Strategy, but all AI strategies need to answer similar questions. “The core components of any AI strategy concern its holy trinity of data, infrastructure and algorithms, surrounded by the pillars of skills and organization,” Zawadzki suggests.

How to organize for AI is another set of decisions to be made. Do you build an in-house team or outsource tasks?  Do you invest in educating management and employees about AI? Andrew Ng, the renowned computer scientist who founded Coursera, recommends building an in-house team, Zawadzki stated. Outside consultants would not know the data, infrastructure, and issues at the company as well as its employees. And learning about AI makes employees more enthusiastic.

Ng also recommends establishing a separate unit which becomes the central enabling point of AI across the company. This unit works with existing departments to find high-impact AI projects and support their development.

Align your organization development processes with machine learning workflow, for best results. Machine learning follows an iterative process, with outcomes far from certain at the outset. Its exploratory nature make it difficult to fit into company-wide goal measurements.

“You can’t promise a working model without thoroughly evaluating the data. Thus, it is difficult to estimate the concrete business impact of AI projects without first investing in ETL and initial data analysis,” stated Rachel Berryman, Co-Founder of todoku.ai, The company offers workshops for non-technical decision-makers to exploit the benefits of data science.

Include a Plan for Putting the Machine Learning Model into Operation

This process of bringing machine learning into the operations requires a shrewd business perspective, suggests Eric Siegel in an article in Machine Learning Times, where he is an editor.

“The most wicked and pervasive pitfall of predictive analytics is organizational in nature, not technical: Predictive models often fail to launch,” he states. Many ideas that seem appealing in the lab can never actually be deployed in the operation. The team may be able to create an elegant predictive model, but the business may not be ready to act on it. Maybe it was lack of management buy-in or unforeseen business constraints.

This is where being shrewd comes in. The team needs to aim at prediction goals that are both achievable and usable.

Oh, and before you get to that, you need to fix the corporate culture and get it focused on succeeding with AI. From the blog of KungFu.ai,  a company focused on helping organizations adopt AI, comes a suggestion to invest in six areas: reskilling, incentivized learning, identifying and removing silos, clear communication, ongoing conversations on ethics, and prioritizing diverse teams.

Known reskilling efforts include an investment of $700 million by Amazon to retool a third of its workforce; an experiment by Walmart to offer college for “$1 per day,” an investment by PwC of $3 billion globally to train employees, and a collaboration between San Jose State University and IBM to train students in high tech skills needed for jobs that may not yet exist.

Practices to accelerate learning are being explored; some call it a “scalable learning model.”

The authors outline the choices they see: “You can defend your current position and cease to be relevant. You can try to change the future—but exponential technology is here, like it or not. Or you can create the future you want to see. You get to choose which to accept—but there’s no sitting on the sidelines anymore.”

Read the source articles in Forbes, Towards Data Science, Machine Learning Times and in the blog of KungFu.ai.

Source: AI Trends
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AI in the Lead of US Emerging Jobs in 2020, says LinkedIn


The top emerging US job for 2020 is AI specialist, according to the third annual Emerging Jobs report from LinkedIn. (GETTY IMAGES)

By AI Trends Staff

Major trends shown in the LinkedIn’s third annual Emerging Jobs Report recently released by LinkedIn, the professional network company, include show a strong showing for AI, that professionals are moving to the most attractive regions, and demand for soft skills around communication, creativity and collaboration are increasing as automation becomes more widespread.

In top US job trends, data science is booming and starting to replace legacy roles. “Data science is seeing continued growth on a tremendous scale,” the report states. It also shows data scientists are taking on responsibilities that had been in the domain of statisticians.

Engineering roles are seeing tremendous growth; more than 50% of emerging jobs on this year’s list are made of up roles in engineering or development, with robotics engineering jobs appearing on the LinkedIn list for the first time.

Online learning is here to stay, with the multibillion-dollar e-learning industry staffing up to prepare for growth.

Leading the LinkedIn list is artificial intelligence specialist, or someone who focuses on machine learning and ways to incorporate AI technology into business environments, as summarized in a report in the Boston Herald. AI specialist jobs have grown annually by 74% over the last five years as demand in computer software, internet, information technology and consumer electronics industries has increased. AI specialist salaries average $136,000 a year.

Top US regions for AI specialist jobs include San Francisco, New York, Boston, Seattle, and Los Angeles.

Second place went to robotics engineer, with a 40% growth rate and average annual salary of $85,000, while data scientist, and its growth rate of 37% and $143,000 average salary came in third. Fourth place went to full stack engineer, a job that pays $82,000 a year on average and has a 35% growth rate.

Site reliability engineer ($130,000 a year), customer success specialist ($90,000 annually) and sales development representative ($60,000 a year, on average) all tied for fifth place with an annual growth rate of 33%.

The rest of the top 15 emerging jobs included data engineer (33% annual growth rate, $100,000 a year salary), behavioral health technician (growth rate of 32% and an average salary of $33,000 a year), cybersecurity specialist ($103,000 a year) and back end developer ($88,000 a year) both with a 30% growth rate, chief revenue officer (28% growth rate and a $330,000 average salary), cloud engineer (growth rate of 27%, $100,000 in average salary), Javascript developer (growth rate of 25%, $83,000 annual salary) and product owner (24% growth rate with a $100,000 annual salary).

People Skills in Demand

Other big trends include: increasing demand for jobs requiring strong people skills. The new roles include product owner, customer success specialist and sales development representative. For Software as a Service (SaaS), a $278 billion industry, to be successful requires people skills not yet automated.

Also, the competition in self-driving cars has the automotive industry searching for AI talent among robotics engineers, data scientists and AI specialists.

Edge computing is expected to attract more development resources, with a shift occurring to cloud-edge hybrid strategies to overcome limitations of a cloud-only architecture. “Being able to analyze high-fidelity, high-resolution, raw machine data in the cloud is often expensive and does not happen in real-time due to transport and ecosystem considerations,” says Senthil Kumar, VP of software engineering for FogHorn, quoted in a piece in The Enterprisers Project. Many organizations to date have settled for a smaller sample size or time-deferred data for their edge projects, which can provide an incomplete or inaccurate picture.

Guy Berger, Principal Economist, LinkedIn

AI is having an impact on the entire workforce, the LinkedIn report suggests. “Artificial intelligence will require the entire workforce to learn new skills, whether it’s to keep up to date with an existing role, or pursuing a new career as a result of automation,” stated LinkedIn’s Principal Economist Guy Berger.

Read the source report from LInkedIn at Emerging Jobs Report,  summary at the Boston Herald, and an account in The Enterprisers Project.

Source: AI Trends
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2020 AI Predictions: Wholistic Adoption, AI-Enabled Business Analysts, Applied AI on Rise


AI Trends Readers See Wholistic Adoption, AI-Enabled Business Analysts and Applied AI as being prominent in 2020. (GETTY IMAGES)

By AI Trends Staff

We invited readers to submit their top predictions for the impact of AI on business in 2020. Here is a selection of responses:

Dawn Fitzgerald is Director of Digital Transformation, Data Center Operations for Schneider Electric. The French multinational corporation is focused on sustainability and efficiency with its software and services offerings to a range of customers, many in the oil and gas, utilities, and manufacturing industries.

Dawn Fitzgerald, Director of Digital Transformation, Data Center Operations for Schneider Electric

Wholistic Adoption on the Horizon

2020 is the year for Wholistic Adoption. Wholistic Adoption is the realization that it is the people, process, and tools combination that create the massive benefit of the AI Shangri-La.  Adoption comes with experiencing Real Value and those that experience Value when using AI in their work will continue to use it… and this value will spread in the organization. But to realize the value we frequently need to redesign our business processes to be digital and AI ready. And to engage the digital ecosystem the user must be trained, encouraged, and their engagement measured. Simply put, a new AI algorithm or application with new AI-enabled features is not enough.

By way of example, the technician of the Data Center world may have only used a PC to check their email, sum numbers in excel, or log their hours.  If we now ask them do their daily rounds on a tablet and respond to predictive analytics then we must understand that new training is required.  Their progress of adoption must be tracked with continuous improvement on training and use. We must encourage a Digital Cultural which is an evolution not an instantaneous response. We must look for the need to redesign non-digital process; reordering or eliminating entire steps due to the digital efficiencies.

This is Wholistic Adoption and it will be a key focus and success factor in 2020.  The AI community will embrace the fact that, when it comes to adoption success, the whole is greater than the sum of its parts.

Per Nyberg is CCO of Stradigi AI, an AI services company based in Montreal. Stradigi offers applied AI from a team of experienced and well-qualified practitioners. The firm offers the Kepler AI platform. Nyberg joined the company in July 2019, after working for many years at Cray, the supercomputer company, where he helped to grow Cray’s global AI business.

Per Nyberg, CCO, Stradigi AI

Rise of the AI-Enabled Business Analyst; AI is No Longer for the Precious Few ML Experts and Data Scientists

“Businesses have been working to break through the logjam of AI projects that have been placed on the back-burner in the face of machine learning skills shortages. However, we’re seeing the real world reach of AI expand as companies look for ways to foster collaboration, gain economies of scale, and accelerate their AI paths from concept to production with maturing tools. AI is no longer for the small minority of machine learning experts and data scientists. With data at their core, business analysts are also eager for a slice of the pie. With AI and ML tools at their disposal, the skills of business analysts are expanding toward data science to explore insights from more diverse and richer data sets through the use of machine learning.

Technology and automated machine learning techniques will begin shifting the use of data and AI to a greater proportion of a company’s business analysts. The demand for these skills are also starting to shape higher-ed curriculums to contend with this new wave of expectations.”

A big concern of customers and prospects is that the shortage of machine learning skills is holding up AI projects. Also, many ML implementations continue to be focused on developing the pipelines and proving the applicability of ML with projects and models from scratch. This approach simply doesn’t scale in many ways—from efficient reuse of learnings to accelerating the ideation cycle. This is one area where an AI platform really makes sense. The key is for businesses to start scaling their specialists’ skills and focus them on the most important tasks. Also, day-to-day AI adoption needs to extend beyond specialized data scientists. Companies can support data scientists, business analysts and other critical roles with intuitive AI platforms that can help take projects from the ideation phase and into production throughout the organization.”

John Desmond is Editor of AI Trends. He has worked as a journalist for seven entrepreneurs in the publishing business, including Eliot Weinman, founder of the AI World Conference & Expo and the AI Trends newsletter.

Applied AI and AI DevOps to Rise

As AI gets more real in business, the practicality of rolling out an application that relies on the processing demands and data requirements of an AI application becomes much more real. These are unprecedented platform requirements. They will require investments in hardware and networking. And companies will always need to assess the business benefit, and quantify whether the investment in AI is worth it, or whether the investment is being made the right way. Recent accounts of massive AI models maxing out hardware are a cause for concern. Will the new AI applications be too brittle to be of lasting practical business value? Will the target of the data science be the right one or a misfire?  Or will there be enough hits to make it worth it?

Re-skilling the workforce will also be a major theme. Young and mid-career workers in all fields without question need to become conversant in AI. If they are not taking courses from the likes of Coursera or a university, they need to bootstrap their way to AI competence somehow.  Otherwise, the threat of job loss is real. If you are asleep at the switch, the AI might come and get you. Workers need to take advantage of the opportunity AI provides to further their careers, and not get victimized by it. In whatever role they play in their companies, workers should be trying to assess how AI could be helpful and how they could be involved in putting AI to work. Those that succeed in adding AI competence to their resumes will benefit greatly. The value of experience in business combined with competence in AI is high.

Source: AI Trends
Continue reading 2020 AI Predictions: Wholistic Adoption, AI-Enabled Business Analysts, Applied AI on Rise

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2020 AI Predictions: Wholistic Adoption, AI-Enabled Business Analysts, Applied AI on Rise


AI Trends Readers See Wholistic Adoption, AI-Enabled Business Analysts and Applied AI as being prominent in 2020. (GETTY IMAGES)

By AI Trends Staff

We invited readers to submit their top predictions for the impact of AI on business in 2020. Here is a selection of responses:

Dawn Fitzgerald is Director of Digital Transformation, Data Center Operations for Schneider Electric. The French multinational corporation is focused on sustainability and efficiency with its software and services offerings to a range of customers, many in the oil and gas, utilities, and manufacturing industries.

Dawn Fitzgerald, Director of Digital Transformation, Data Center Operations for Schneider Electric

Wholistic Adoption on the Horizon

2020 is the year for Wholistic Adoption. Wholistic Adoption is the realization that it is the people, process, and tools combination that create the massive benefit of the AI Shangri-La.  Adoption comes with experiencing Real Value and those that experience Value when using AI in their work will continue to use it… and this value will spread in the organization. But to realize the value we frequently need to redesign our business processes to be digital and AI ready. And to engage the digital ecosystem the user must be trained, encouraged, and their engagement measured. Simply put, a new AI algorithm or application with new AI-enabled features is not enough.

By way of example, the technician of the Data Center world may have only used a PC to check their email, sum numbers in excel, or log their hours.  If we now ask them do their daily rounds on a tablet and respond to predictive analytics then we must understand that new training is required.  Their progress of adoption must be tracked with continuous improvement on training and use. We must encourage a Digital Cultural which is an evolution not an instantaneous response. We must look for the need to redesign non-digital process; reordering or eliminating entire steps due to the digital efficiencies.

This is Wholistic Adoption and it will be a key focus and success factor in 2020.  The AI community will embrace the fact that, when it comes to adoption success, the whole is greater than the sum of its parts.

Per Nyberg is CCO of Stradigi AI, an AI services company based in Montreal. Stradigi offers applied AI from a team of experienced and well-qualified practitioners. The firm offers the Kepler AI platform. Nyberg joined the company in July 2019, after working for many years at Cray, the supercomputer company, where he helped to grow Cray’s global AI business.

Per Nyberg, CCO, Stradigi AI

Rise of the AI-Enabled Business Analyst; AI is No Longer for the Precious Few ML Experts and Data Scientists

“Businesses have been working to break through the logjam of AI projects that have been placed on the back-burner in the face of machine learning skills shortages. However, we’re seeing the real world reach of AI expand as companies look for ways to foster collaboration, gain economies of scale, and accelerate their AI paths from concept to production with maturing tools. AI is no longer for the small minority of machine learning experts and data scientists. With data at their core, business analysts are also eager for a slice of the pie. With AI and ML tools at their disposal, the skills of business analysts are expanding toward data science to explore insights from more diverse and richer data sets through the use of machine learning.

Technology and automated machine learning techniques will begin shifting the use of data and AI to a greater proportion of a company’s business analysts. The demand for these skills are also starting to shape higher-ed curriculums to contend with this new wave of expectations.”

A big concern of customers and prospects is that the shortage of machine learning skills is holding up AI projects. Also, many ML implementations continue to be focused on developing the pipelines and proving the applicability of ML with projects and models from scratch. This approach simply doesn’t scale in many ways—from efficient reuse of learnings to accelerating the ideation cycle. This is one area where an AI platform really makes sense. The key is for businesses to start scaling their specialists’ skills and focus them on the most important tasks. Also, day-to-day AI adoption needs to extend beyond specialized data scientists. Companies can support data scientists, business analysts and other critical roles with intuitive AI platforms that can help take projects from the ideation phase and into production throughout the organization.”

John Desmond is Editor of AI Trends. He has worked as a journalist for seven entrepreneurs in the publishing business, including Eliot Weinman, founder of the AI World Conference & Expo and the AI Trends newsletter.

Applied AI and AI DevOps to Rise

As AI gets more real in business, the practicality of rolling out an application that relies on the processing demands and data requirements of an AI application becomes much more real. These are unprecedented platform requirements. They will require investments in hardware and networking. And companies will always need to assess the business benefit, and quantify whether the investment in AI is worth it, or whether the investment is being made the right way. Recent accounts of massive AI models maxing out hardware are a cause for concern. Will the new AI applications be too brittle to be of lasting practical business value? Will the target of the data science be the right one or a misfire?  Or will there be enough hits to make it worth it?

Re-skilling the workforce will also be a major theme. Young and mid-career workers in all fields without question need to become conversant in AI. If they are not taking courses from the likes of Coursera or a university, they need to bootstrap their way to AI competence somehow.  Otherwise, the threat of job loss is real. If you are asleep at the switch, the AI might come and get you. Workers need to take advantage of the opportunity AI provides to further their careers, and not get victimized by it. In whatever role they play in their companies, workers should be trying to assess how AI could be helpful and how they could be involved in putting AI to work. Those that succeed in adding AI competence to their resumes will benefit greatly. The value of experience in business combined with competence in AI is high.

Source: AI Trends
Continue reading 2020 AI Predictions: Wholistic Adoption, AI-Enabled Business Analysts, Applied AI on Rise

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Savvy Marketers Using AI Need to Know about New CCPA in Effect on Jan. 1, 2020


The California Privacy Act (CCPA), in effect on Jan. 1, 2020; has a profound effect on marketers collecting data on customers in California. (GETTY IMAGES)

By John P. Desmond, AI Trends Editor

Just as marketers are increasingly pursuing the use of AI tools to help personalize marketing to website visitors, the new California privacy law goes into effect. Now savvy marketers have to try to protect their companies from any exposure to violations.

That the march of AI is traversing online marketing is not in doubt. A survey of 3,000 consumers in North America conducted by the Harris Poll and RedPoint Global found that 63% expected personalized services and seek promotions and offers tailored to their needs, according to an account in Martech Advisor.

The authors cited these trends:

Embedding AI-powered chatbots to collect data for personalization. The chatbot can engage in a conversation with the consumer site visitor, and thereby feed data into forms on the CRM platform, enabling later personalization.

Use AI to personalize micro-elements of content. Tweak the greeting, the subject line to emails and follow ups to personalize the conversation. The Einstein AI engine from Salesforce is an example tool that can help improve micro-elements of content, such as by recommending subject lines to enhance targeting.

Target customers in real-time as they click through your site. As the visitor browses products, clicks on links, explores different catalog pages, the screen is adding and removing visual elements and retargeting ads accordingly. Match2One offers a tool to help predict which visitors would be interested in your product based on their website behavior.

Results are coming in for early experience with AI marketing personalization tools. For example, Gap built their own customer data platform (CDP) and implemented AI techniques to help with customer resolution, segmentation, and clustering. Gap partnered with AI startup Amperity to strengthen its technology. Efficiency of digital marketing is said to have increased by some 50% from 2018 as a result.

The benefits of AI for targeted marketing include the ability to deliver product recommendations, according to a recent account in Forbes. On many online retailer websites, a view of a product will result in suggestions of additional related items at the time of purchase. Retailers are also exploring how to improve the user experience (UX), how the site functions, in order to improve sales. The use of chatbots to enhance customer support is also becoming more prevalent, with the bot available to answer questions about product features, shipping time or where to make a purchase instantly.

CCPA Took Effect on Jan. 1, 2020

Now comes the California Consumer Privacy Act, approved in 2018 but going into effect on Jan. 1, 2020. The law gives consumers more protection over how their information can be used by for-profit companies that do business in California. The businesses in the purview of CCPA need to have more than $25 million in revenue, receive information from over 50,000 consumers or derive 50% or more of revenue from selling personal consumer information.

If the CCPA applies to your business, whenever you collect personal information on your site visitor, you need to disclose what information you are collecting and how you will be using it, according to an account in the blog of Hubspot, a supplier of marketing software. Also, you now need to give consumers the right to opt-out of having their information sold to third-parties, and you need to give consumers view access and the ability to delete the information you have collected about them.

CCPA penalties are up to $2,500 per violation or $7,500 per intentional violation, plus an additional $100 to $750 per incident to the affected persons. The CCPA applies only to data collected directly from and about California consumers. The European GDPR in contrast, applies to all data collected about EU citizens.

Awareness of CCPA is low. A recent ESET survey cited by Hubspot found that 44% of respondents had never heard of CCPA, 12% did not know if the law applied to them, and 34% were not sure if they needed to change how they capture and process data to comply with the law.

Tony Anscombe, global security evangelist, ambassador for ESET

“It’s clear that businesses are confused about this upcoming regulation, they do not know whether they are subject to the law and what they need to do to become compliant,” stated Tony Anscombe, global security evangelist and industry ambassador for ESET, an IT security and services company, in a press release.. “This is a serious situation, as the penalties will be severe, and the financial harm could be grave to these firms. Businesses should particularly focus on the ‘reasonable security’ aspect of the law by ensuring they have stringent processes and practices in place, including strong endpoint protection and encryption, throughout their organization.”

Look for the savvy AI marketing tool suppliers to be offering assistance in staying in compliance with evolving consumer data privacy laws.

Read the source posts in Martech Advisor, Forbes. on the Hubspot blog and on BusinessWire.

Source: AI Trends
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Delivery Robots With AI On the March


Delivery robots incorporating AI are motoring on more public sidewalks and on college campuses, testing rules of good citizenship. (GETTY IMAGES)

By AI Trends Staff

Delivery robots incorporating AI are on the march, being deployed more widely on the ground, sometimes crowding sidewalks. Here is an update.

Delivery robot providers include Starship Technologies, a startup created by Janus Friis and Ahti Heinla, founders of Skype. The company offers a general-purpose home delivery robot that today is an array of cameras and GPS sensors, but in the future will include microphones, speaker, and the ability via AI-driven natural language processing, to talk to customers. Since 2016, Starship has carried out 50,000 delivers in over 100 cities across 20 countries, according to an account in SingularityHub written by Dr. Peter H. Diamandis, the founder of Singularity University and the founder and executive chairman of the XPrize Foundation.

Dr. Peter Diamandis, Founder of Singularity University and founder and executive chairman of the XPrize Foundation

Another startup delivery provider is Nuro, co-founded by Jiajun Zhu, an engineer who helped develop Google’s self-driving car. The Nuro, looking like a toaster on wheels, is designed to carry about 12 bags of groceries in version 1. The company has been working in select Kroger stories since 2018, and partnered with Domino’s in 2019.

Retailers will be under pressure to save money on labor by using robots, Diamandis asserts. The US minimum wage is projected to be $15/hour by 2024; on Jan. 1, 2020, federal minimum wage will increase to $12/hour. Most states have historically set higher minimum wages than the federal standard, which was at $7.25/hour for many years.

The numbers make it tough for retailers to avoid employing more robots. “Robots work 24-7. They never take a day off, never need a bathroom break, health insurance, or parental leave,” Diamandis stated.

This is altering our relationship with commerce.

Starship Technologies Has Raised $85 Million So Far

Starship Technologies in August 2019 raised $40 million in private funding that they plan to use to deploy thousands of its autonomous six-wheeled delivery robots on college campuses around the country over the next two years, according to an account in The Verge. The San Francisco startup’s robots have been tested in over 100 cities in 20 countries, traveling 350,000 miles, crossing 4 million streets and recently completing delivery number 100,000. College campuses have many walking paths, well-defined boundaries and students with smartphones.

Starship works closely with college administrations, including at George Mason University, Northern Arizona University, the University of Pittsburgh, and Purdue University. The company plans to deploy 25 to 50 robots at each campus, which implies more than 5,000 robots running around the schools by 2021.

The trunk of the electric robot can fit about 20 pounds of cargo and has a suite of cameras around the outside that guide the robot. The delivery radius is three to four miles; the maximum speed is 4 mph, slower than delivery by a human on a bike or in a car. Starship CEO Lex Bayer stated students might even prefer the robot over a fellow student making deliveries. “There’s no guilt or shame,” he stated.

Starship has now raised a total of $85 million. The company charges $1.99 per delivery. Competition is from human delivery firms including DoorDash and Postmates.

Postmates is not standing still however. The company is identified in an account in TechRepublic as among the top five of delivery companies, along with Starship. The Postmates Serve robot has humanoid eyes that change to help people on the sidewalk understand where it wants to go next. The robot was being tested in Los Angeles in 2019; the expectation is that it could be used to replace human delivery workers.

Another top five entry is startup Nuro, which offers an autonomous small delivery van that drives on regular roads. It has heated and chilled compartments for delivering groceries. Testing started at a grocery store in Scottsdale in 2019 and later expanded to two grocery stores in Houston.

The rest of the top five is populated by the majors: Amazon with Scout and FedEx with the SameDay Bot, designed by Dean Kamen, inventor of the Segway. Testing was happening at the FedEx headquarters in Memphis in 2019. Amazon Scout conducted testing in 2019 in Washington state.

Robots Need to Be Well-Behaved on Crowded Sidewalks

While delivery robots vie for space on public sidewalks, they may free up the roadways a bit, suggests an account in Scientific American.  A study by Mobility Lab, a transportation policy research center in Arlington, Va. and George Mason University found 73% of freight and delivery vehicles in Arlington were parked outside authorized areas, often blocking bike lanes, fire hydrants, and crosswalks. By moving the last leg of deliveries from the road to the sidewalk, cities could reduce congestion and eliminate the parking problem entirely, suggested Paul Mackie, director of research at Mobility.

Renia Ehrenfeucht, chair of the Community and Regional Planning Department, University of New Mexico

That assumes the sidewalk will have room for the delivery robots. To gain trust, the robots need to demonstrate they can safely share pedestrian spaces, suggests Renia Ehrenfeucht, chair of the Community and Regional Planning Department at The University of New Mexico in Albuquerque, and co-author of the book Sidewalks: Conflict and Negotiation in Public Space.

“It’s actually really hard to navigate crowded sidewalks and not bump into people, and do it smoothly,” Ehrenfeucht says. “Until delivery robots are that skilled, if they could be, they will be disruptive.”

Read the source articles in SingularityHub, The Verge, TechRepublic and  Scientific American.

Source: AI Trends
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State of the Market: What AI Implementations Are in Place and Underway


IT Automation was the leading AI project to be implemented among readers of AI Trends, chosen by 39% of readers surveyed. (GETTY IMAGES)

In a broad market survey conducted recently by AI Trends, respondents outlined which AI solutions they have implemented and which they expect to implement in the coming months. Together, the results reveal which areas are ripe for the most growth in short-term AI implementation.

AI Trends surveyed readers representing 28 different industries, though IT services, computer software, and government made up well over a third of the responses. We asked which AI solutions have been implemented so far, and which are coming in less than 12 months.

Among the solutions already implemented, IT automation leads the pack with 39% of responses, though cybersecurity, customer services, and virtual assistants each accounted for more than 25% of the responses. Sales optimization and workforce management accounted for the fewest votes at only 13% each.

In the coming 12 months, IT automation is still the leader, though forecasting is a close second with 27% of respondents mentioning that as an area of emphasis. Workforce management remains a fairly low priority with only 14% of respondents choosing that option. Sales optimization, though, was included as a priority for 19% of survey-takers.

Nearly one-third of respondents said implementing AI has given the company a slight lead over competitors, while just slightly fewer said the technology has “allowed us to remain competitive.”

For the “why” within the business, the most respondents chose enhancing customer experience and enhancing existing products as reasons to implement AI. Optimizing internal operations including reducing headcount through automation and streamlining employee roles were chosen less frequently by respondents, but all three options earned at least 17% of answers.

With implementations underway and new ones on the horizon, AI skills continue to be a challenge. Data scientists (24%) and AI software developers (23%) were the top skill sets that companies found lacking in house when they began to implement AI solutions. But AI researchers, project managers, and user experience designers are also needed.

See the full survey results.

Source: AI Trends
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AI Bouncing Off the Walls as Growing Models Max Out Hardware


The growing size of AI models is bumping into the limits of hardware needed to process it, meaning current AI may be hitting the wall. (GETTY IMAGES)

By John P. Desmond, AI Trends Editor

Has AI hit the wall? Recent evidence suggests it might be the case.

At the recent NeurIPS event in Vancouver, software engineer Blaise Aguera y Arcas, the head of AI for Google, recognized the progress in the use of deep learning techniques to get smartphones to recognize faces and voices. And he called attention to limitations of deep learning.

Blaise Aguera y Arcas, the head of AI for Google

“We’re kind of like the dog who caught the car,” Aguera y Arcas said in an account reported in Wired. Problems that involve more reasoning or social intelligence, like sizing up a potential hire, may be out of reach of today’s AI. “All of the models that we have learned how to train are about passing a test or winning a game with a score, [but] so many things that intelligences do aren’t covered by that rubric at all,” he stated.

A similar theme was struck in an address by Yoshua Bengio, director of Mila, an AI institute in Montreal, known for his work in artificial neural networks and deep learning. He noted how today’s deep learning systems yield highly specialized results. “We have machines that learn in a very narrow way,” Bengio said. “They need much more data to learn a task than human examples of intelligence, and they still make stupid mistakes.”

Both speakers recommended AI developers seek inspiration from the biological roots of natural intelligence, so that for example, deep learning systems could be flexible enough to handle situations different from the ones they were trained on.

A similar alarm was sounded by Jerome Pesenti, VP of AI at Facebook, also in a recent account in Wired on AI hitting the wall. Pesenti joined Facebook in January 2018, inheriting a research lab created by Yann Lecun, a French-American computer scientist known for his work on machine learning and computer vision. Before Facebook, Pesenti had worked on IBM’s Watson AI platform and at Benevolent AI, a company applying the technology to medicine.

Jerome Pesenti, VP of AI at Facebook

“Deep learning and current AI, if you are really honest, has a lot of limitations. We are very very far from human intelligence, and there are some criticisms that are valid: It can propagate human biases, it’s not easy to explain, it doesn’t have common sense, it’s more on the level of pattern matching than robust semantic understanding. But we’re making progress in addressing some of these, and the field is still progressing pretty fast. You can apply deep learning to mathematics, to understanding proteins, there are so many things you can do with it,” Pesenti stated in the interview.

The compute power hardware requirement, the sheer volume of equipment needed, continues to grow for advanced AI. This continuation of this growth rate appears to be unrealistic. “Clearly the rate of progress is not sustainable. If you look at top experiments, each year the cost it going up 10-fold. Right now, an experiment might be in seven figures, but it’s not going to go to nine or ten figures, it’s not possible, nobody can afford that,” Pesenti stated. “It means that at some point we’re going to hit the wall. In many ways we already have.”

The way forward is to work on optimization, getting the most out of the available compute power.

Similar observations are being made by Intel’s Naveen Rao VP and general manager of Intel’s AI Products Group. He suggested at the company’s recent AI Summit, from an account in datanami, that the growth in the size of neural networks is outpacing the ability of the hardware to keep up. Solving the problem will require new thinking about how processing, network, and memory work together.

Naveen Rao, VP and general manager of Intel’s AI Products Group

“Over the last 20 years we’ve gotten a lot better at storing data,” Rao stated. “We have bigger datasets than ever before. Moore’s Law has led to much greater compute capability in a single place. And that allowed us to build better and bigger neural network models. This is kind of a virtuous cycle and it’s opened up new capabilities.”

More data translates to better deep learning models for recognizing speech, text, and images. Computers that can accurately identify images and chatbots that can carry on fairly natural conversations, are primary examples of how deep learning is having an impact on daily life. However this cutting edge AI is only available to the biggest tech firms—Google, Facebook, Amazon, Microsoft. Still, we might be at the max.

It could be application-specific integrated circuits (ASIC) could help move more AI processing to the edge. Discrete graphics processing units (GPUs) are also being planned at Intel and a vision processing unit (VPU) chip was recently unveiled.

“There’s a clear trend where the industry is headed to build ASICS for AI,” Rao stated. “It’s because the growth of demand is actually outpacing what we can build in some of our other product lines.”

Facebook AI researchers recently published a report on their XLM-R project, a natural language model based on the Transformer model from Google.  XLM-R is engineering to be able to perform translations between 100 different languages, according to an account in ZDNet.

XLM-R runs on 500 of NVIDIA’s V100 GPUs, and it is hitting the wall, running into resource constraints. The application has 24 layers, 16 “attention heads” and 500 million parameters. Still, it has a finite capacity and reaches its limit.

“Model capacity (i.e. the number of parameters in the model) is constrained due to practical considerations such as memory and speed during training and inference,” the authors wrote.

The experience exemplifies two trends in AI on a collision course. One is the intent of scientists to build bigger and bigger models to get better results; the other is roadblocks in computing capacity.

Read the source articles in Wireddatanami and ZDNet.

Source: AI Trends
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Startup Spotlight: AX Semantics Uses AI to Automate Content Generation


Natural language generation software from startup AX Semantics is said to automate writing for websites, including news, weather and sports. (GETTY IMAGES)

By AI Trends Staff

AX Semantics launched globally on Dec. 12 but with more than 500 customers, the company is not exactly a startup. The company is offering an AI-powered application for natural-language generation, a software process that transforms structured data into natural language.

Customers of AX Semantics include Deloitte, Porsche, and Nestle. Headquartered in Stuttgart, Germany, the company opened an office in Sunnyvale, California, as part of its launch. The firm’s software is said to allow companies, news organizations, e-commerce, and social media providers to publish high-quality content in 100 languages, within minutes in every vertical and category.

“AI-powered content generation tools are a must for businesses who want to succeed and scale amidst the perpetual business and cultural shifts arriving with Industry 4.0,” stated Saim Rolf Alkan, CEO and founder of AX Semantics, in a press release. “Businesses simply cannot hire the sheer volume of people needed to produce the massive amounts of content required for them to meet their goals and keep pace in the market.”

Robert Weissgraeber, CTO and Managing Director, AX Semantics

AX Semantics’ natural-language generation (NLG) software creates content that can populate an entire website, fill a news section with earnings reports, create weather reports and sports scores, or generate unique descriptions for e-commerce products. The product is offered as a subscription service with fees between $279 and $1,599 per month. The software is offered as SaaS via a web browser.

Robert Weissgraeber, CTO and Managing Director of AX Semantics, responded to some queries from AI Trends about the launch.

AI Trends: What business problem are you trying to solve?

Robert Weissgraebar: AX Semantics is solving a pressing problem for businesses in every vertical: businesses are simply not able to create enough content for the digital age. Our natural language generation (NLG) software powered by AI and natural language processing (NLP) makes this simple. Using formatted data, the software creates content that can populate an entire website, fill a news section with earnings reports, generate descriptions for retail items in e-commerce, produce social media content, and more. Our software can do this in more than 110 languages, in a manner of minutes with a  translation process that makes it easy to scale and enter new markets.

How does your solution address the problem?

AX software is 100% SaaS. Everything is available from your desk via your web browser, no programming or IT departments required. Our self-service with integrated e-learning allows customers to start automating text within 48 hours—nearly 500 customers have already done this successfully.

How are you getting to the market?

AX Semantics has been available in Europe for some time but we launched globally on December 13, 2019, to offer our services to a larger business audience.

Who are your users and customers?

We have 450+ global customers spanning multiple verticals, including e-commerce, BSS reporting, media publishing, pharmaceutical, and more. Our customers include large global brands such as Porsche, Deloitte, Mytheresa, and Nivea.

How is the company funded?

AX Semantics has received $6M in funding to date, including Series A funding from Airbridge Equity Partners in Amsterdam and Plug & Play Ventures, a Silicon Valley accelerator.

Learn more at AX Semantics.

Source: AI Trends
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