2020 is going to be a big year for artificial intelligence, that is.

At least, that was the message LivePerson CEO Robert LoCascio delivered to CNBC's Jim Cramer on Friday.

"When we think about 2020, I really think it's the start of everyone having [AI]," LoCascio said on "Mad Money." "AI is now becoming something that's not just out there. It's something that we use to drive everyday life."

LivePerson, based in New York City, provides the mobile and online messaging technology that companies use to interact with customers.

Shares of LivePerson closed up just more than 5% on Friday, at $38.32. While it sits below its 52-week high of $42.85, it is up more than 100% for the year.

It reported earnings last week, with total revenue at $75.2 million for the third quarter, which is up 17% compared with the same quarter in 2018.

More than 18,000 companies use LivePerson, including four of the five largest airlines, LoCascio said. Around 60 million digital conversations happen through LivePerson each month, he said.

"You can buy shoes with AI on our platform. You can do airlines. You can do T-Mobile, change your subscription with T-Mobile," he said. "That's the stuff in everyday life."

The world has entered a point where technology has transformed all aspects of communication, LoCascio said.

"Message your brand like you message your friends and family," he said, predicting a day where few people want to pick up the phone and call a company to ask questions. "We're powering all that ... for some of the biggest brands in the world."

LoCascio said LivePerson, which he founded in 1995, now uses AI to power about 50% of the conversations on its platform.

"We're one of the few companies where it's not a piece of the puzzle. It's the entire puzzle," he said.

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Artificial intelligence has become a driving force in everyday life, says LivePerson CEO - CNBC

Department of Defense officials routinely talk about the need to more fully embrace machine learning and artificial intelligence, but one leader in the Marine Corps said those efforts are falling short.

Were not serious about AI. If we were serious about AI we would put all of our stuff into one location, Lt. Gen. Eric Smith, commander of the Marine Corps Combat Development Command and the Deputy Commandant for Combat Development and Integration, said at an AFCEA Northern Virginia chapter lunch Nov. 15.

Smith was broadly discussing the ability to provide technologies and data thats collected in large quantities and pushed to the battlefield and tactical edge. Smith said leaders want the ability to send data to a 50-60 Marine cell in the Philippines that might be surrounded by the Chinese. That means being able to manage the bandwidth and signature so that those forces arent digitally targeted. That ability doesnt currently doesnt exist, he said.

He pointed to IBMs Watson computer, noting that the system is able to conduct machine learning and artificial intelligence because it connects to the internet, which allows it to draw from a much wider data pool to learn from. Military systems arent traditionally connected to the broader commercial internet, and thus are limited from a machine learning sense.

We have stovepipes of excellence everywhere from interagency, CIA, NSA. The Navys got theirs, Marine Corps got theirs, everybodys got theirs. You cant do AI when the machine cant learn from one pool of data, he said.

Brown noted that he was not speaking on behalf of the entire department.

Pentagon leadership has come to similar conclusions. Top officials have noted that one of the critical roles the Joint Enterprise Defense Infrastructure cloud program will do is provide a central location for data.

The warfighter needed enterprise cloud yesterday. Dominance in A.I. is not a question of software engineering. But instead, its the result of combining capabilities at multiple levels: code, data, compute and continuous integration and continuous delivery. All of these require the provisioning of hyper-scale commercial cloud, Lt. Gen. Jack Shanahan, director of the Joint AI Center, said in August. For A.I. across DOD, enterprise cloud is existential. Without enterprise cloud, there is no A.I. at scale. A.I. will remain a series of small-scale stovepipe projects with little to no means to make A.I. available or useful to warfighters. That is, it will be too hard to develop, secure, update and use in the field. JEDI will provide on-demand, elastic compute at scale, data at scale, substantial network and transport advantages, DevOps and a secure operating environment at all classification levels.

Overall, Smith said that industry should start calling out DoD when policies or technical requirements hinder what it can offer.

If were asking for something that is unobtanium or if our policies are keeping you from producing something we can buy, youve got to tell us, he said

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One way for the Pentagon to prove it's serious about artificial intelligence - C4ISRNet

News Release

Thursday, November 14, 2019

Technique key to scale up manufacturing of therapies from induced pluripotent stem cells.

Researchers used artificial intelligence (AI) to evaluate stem cell-derived patches of retinal pigment epithelium (RPE) tissue for implanting into the eyes of patients with age-related macular degeneration (AMD), a leading cause of blindness.

The proof-of-principle study helps pave the way for AI-based quality control of therapeutic cells and tissues. The method was developed by researchers at the National Eye Institute (NEI) and the National Institute of Standards and Technology (NIST) and is described in a report appearing online today in the Journal of Clinical Investigation. NEI is part of the National Institutes of Health.

This AI-based method of validating stem cell-derived tissues is a significant improvement over conventional assays, which are low-yield, expensive, and require a trained user, said Kapil Bharti, Ph.D., a senior investigator in the NEI Ocular and Stem Cell Translational Research Section.

Our approach will help scale up manufacturing and will speed delivery of tissues to the clinic, added Bharti, who led the research along with Carl Simon Jr., Ph.D., and Peter Bajcsy, Ph.D., of NIST.

Cells of the RPE nourish the light-sensing photoreceptors in the eye and are among the first to die from geographic atrophy, commonly known as dry AMD. Photoreceptors die without the RPE, resulting in vision loss and blindness.

Bhartis team is working on a technique for making RPE replacement patches from AMD patients own cells. Patient blood cells are coaxed in the lab to become induced pluripotent stem cells (IPSCs), which can become any type of cell in the body. The IPS cells are then seeded onto a biodegradable scaffold where they are induced to differentiate into mature RPE. The scaffold-RPE patch is implanted in the back of the eye, behind the retina, to rescue photoreceptors and preserve vision.

The patch successfully preserved vision in an animal model, and a clinical trial is planned.

The researchers AI-based validation method employed deep neural networks, an AI technique that performs mathematical computations aimed at detecting patterns in unlabeled and unstructured data. The algorithm operated on images of the RPE obtained using quantitative bright-field absorbance microscopy. The networks were trained to identify visual indications of RPE maturation that correlated with positive RPE function.

Those single-cell visual characteristics were then fed into traditional machine-learning algorithms, which in turn helped the computers learn to detect discrete cell features crucial to the prediction of RPE tissue function.

The method was validated using stem cell-derived RPE from a healthy donor. Its effectiveness was then tested by comparing iPSC-RPE derived from healthy donors with iPSC-RPE from donors with oculocutaneous albinism disorder and with clinical-grade stem cell-derived RPE from donors with AMD.

In particular, the AI-based image analysis method accurately detected known markers of RPE maturity and function: transepithelial resistance, a measure of the junctions between neighboring RPE; and secretion of endothelial growth factors. The method also can match a particular iPSC-RPE tissue sample to other samples from the same donor, which helps confirm the identity of tissues during clinical-grade manufacturing.

Multiple AI-methods and advanced hardware allowed us to analyzeterabytesandterabytesof imaging data for each individual patient, and do it more accurately and much faster than in the past, Bajcsy said.

This work demonstrates how a garden variety microscope, if used carefully, can make a precise, reproducible measurement of tissue quality,Simon said.

The work was supported by the NEI Intramural Research Program and the Common Fund Therapeutics Challenge Award. The flow cytometry core, led by the National Heart, Lung and Blood Institute, also contributed to the research.

NEI leads the federal governments research on the visual system and eye diseases. NEI supports basic and clinical science programs to develop sight-saving treatments and address special needs of people with vision loss. For more information, visit https://www.nei.nih.gov.

About the National Institutes of Health (NIH):NIH, the nation's medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit http://www.nih.gov.

NIHTurning Discovery Into Health

Schaub NJ, Hotaling NA, Manescu P, Padi S, Wan Q, Sharma R, George A, Chalfoun J, Simon M, Ouladi M, Simon CG, Bajcsy P, Bharti K. Deep learning predicts function of live retinal pigment epithelium from quantitative microscopy. In-press preview published online November 14, 2019 in J. Clin. Investigation.

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NIST researchers use artificial intelligence for quality control of stem cell-derived tissues - National Institutes of Health

Chinas push to become a world tech power, which has paid off with advances in telecommunications, batteries and robots, is yielding further results its taken a leadership position in A.I. powered education investments.

The country is among the most-active regions for artificial intelligence-powered edtech investments, with K-12 classes, foreign language study and high-quality education getting the most attention, according to a Deloitte report released this week. Almost 20% of all education deals in the country last year involved AI compared with 4% in 2016, Deloitte said.

Although China is a late-comer in AI-based education, its unique advantages in application and implementation make it one of the most popular regions for investment in AI-based education globally, the report said.

The report singled out what it sees as the next big opportunity in edtech: so-called soft skill-focused quality education, similar to the wests STEAM model, which adds art to the original emphasis on science, technology, engineering and math. The segment has developed with state support, the report said.

AI-based quality education is becoming the blue ocean for investment, the report said.

Globally, the U.S. and China are the two hotspots of edtech investments. More than 95% of AI edtech investments from 2016 through the first quarter of this year were in the two countries, according to the report. Chinese companies scored $63.2 billion in 170 deals, compared with $154.2 billion in 54 U.S. investments.

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China Sees Surge of Edtech Investments With Focus on Artificial Intelligence - Karma

Climate Change, Development & Aid, Editors' Choice, Environment, Featured, Food & Agriculture, Food Security and Nutrition, Food Sustainability, Global, Headlines, TerraViva United Nations

Credit: Food Tank

UNITED NATIONS, Nov 14 2019 (IPS) - When UN Secretary-General Antonio Guterres made a global appeal for zero hunger on World Food Day last month, he provided some grim statistics rich in irony: more than 820 million people do not have enough to eat, he said, while two billion people are overweight or obese.

It is unacceptable that hunger is on the rise at a time when the world wastes more than one billion tonnes of food every year.Still, the United Nations is hoping for the eradication of extreme hunger by 2030 as part of its Sustainable Development Goals (SDGs).

How realistic is this? And can Artificial Intelligence (AI), touted as the new panacea for some of the worlds ills, help facilitate increased agricultural crops and farm output?

In a New York Times article titled Harvesting Corn, Wheat and a Profit October 13, Tim Gray points out that as the worlds population rises, from the current 7.6 billion to nearly 10 billion in 2050, the United Nations has estimated that 70 percent more food will be needed by then, but it will have to be produced on just five percent of arable land.

But AI, meanwhile, is on the move with farmers operating self-guided tractors guided by GPS navigation systems, drones being used to monitor crops, AI being employed in irrigation and robots likely to take cow hands jobs.

Asked if there is a role for AI in agriculture, Sonja Vermeulen, Director of Programs, CGIAR System Organization, told IPS: Absolutely. CGIARs role in this is creating and scaling up affordable AI and big data solutions so they are relevant and accessible to a wide diversity of farmers regardless of gender, culture, wealth or literacy.

For example, CGIAR (described as a global partnership that unites international organizations engaged in research for a food-secured future and formerly known as the Consultative Group for International Agricultural Research ) won former UN secretary-general Ban Ki Moons innovation prize for work using big data to better predict rice harvests from weather patterns so farmers can match planting places and times (and save a lot of money), she said.

Danielle Nierenberg, President, Food Tank, described as a think tank for food, told IPS while AI, Big Data, and other technologies can hold a lot of potential for farmers of all sizes, they are not a silver bullet for solving hunger.

The question we need to ask with all technologies is what problem are they trying to solve and who will they help?

Unfortunately, she said, many high-tech innovations are not helping farmers who need it the mostthe worlds small and medium sized farmers who produce much of the food on the globe.

Those farmers need to be part of the research and development of new technologies so that they actually solve the challenges those farmers face, she added.

And there needs to be an emphasis on combining high and low tech innovations and making sure that farmers indigenous and traditional knowledge is respected, said Nierenberg.

An article titled Artificial Intelligence: What AI Can do for Smallholder Farmers in the Food Tank website, says Imagine one hundred years ago if farmers had access to huge volumes of information about the soil profile of their land, the varieties of crops they were growing, and even the fluctuations of their local climate?. This kind of information could have prevented an environmental crisis like the Dust Bowl of the 1920s in the American Midwest. But even ten years ago, the idea that farmers could have access to this kind of information was unrealistic.

For the team behind the CGIAR Platform for Big Data in Agriculture, farming is the next frontier for using artificial intelligence (AI) to efficiently solve complex problems. The teamwhich includes biologists, agronomists, nutritionists, and policy analysts working with data scientistsis using Big Data tools to create AI systems that can predict the potential outcomes of future scenarios for farmers.

By leveraging massive amounts of data and using innovative computational analysis, the CGIAR Platform is working to help farmers increase their efficiency and reduce the risks that are inherent in farming, according to the article.

Asked for her comments, Ruth Richardson, Executive Director, Global Alliance for the Future of Food, told IPS: When it comes to the future of food in the climate emergency, we need to go beyond just looking to technology as a silver bullet solution. Instead, we need to broaden the discussion to be about wider food system transformation and interrogate whether technology is the end or a means to an end?

After all, she said, some farmers operate using advanced technology but many globally are still reliant on small scale operations and tools. Its important to also note that technology and innovation, more broadly, are important tools to achieve sustainable food systems but technology itself especially the access to it is not neutral.

Richardson pointed out that one of the biggest challenges related to technology is related to governance.

A concentration of power and highly unequal power relations are a deep problem in todays broken food system so we need to ensure that technology and its implementation is managed in a way that promotes equity and environmental sustainability. Any developments need to be assessed holistically with a focus on risks and trade-offs, she declared.

Anuradha Mittal, Executive Director at the San Francisco-based Oakland Institute, told IPS today nearly 800 million people are hungry and this number is expected to grow, despite grand declarations by the governments at UN summits.

But we already produce enough food to feed at least 10 billion people (the current population is around 7.6 billion). It is therefore essential to understand the true causes of hungerwhen there is no shortage of food.

Focus on technology driven industrial agricultural system as a solution to hunger, has created a food system that is upside down and backwards. Denying family farmers their basic rights to land, seeds, markets, and food sovereignty has rendered food producers hungry, argued Mittal.

Take the case of India primarily an agrarian economy with 60% of its population employed in agriculture. India is worlds 14th largest agricultural, fishery, and forestry product exporter in 2018, India accrued a $14.6 billion trade surplus of agricultural, fishery, and forestry goods.

And yet, she pointed out, farmer suicides continue to dominate newspaper headlines nationally while the country is home to the largest number of hungry people in the world. In 2017 Global Hunger Index, India ranked 100 out of 119 ranked countries.

The last Intergovernmental Panel on Climate Change (IPCC) report focused on climate change and land, makes it clear that fixing our food system is imperative. Industrial food production has led to increased greenhouse gas emissions; monopoly of a few corporations over seeds to chemical inputs; monoculture production which threatens biodiversity globally; and more.

This has made our agricultural system both a major driver of climate changeand majorly vulnerable to its effects..

Instead of seeing artificial intelligence as the next silver bullet solution to hunger, we need a food system that respects and protects the intelligence of family farmers, traditional knowledge and agroecological principles of farming, declared Mittal..

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Will Artificial Intelligence Help Resolve the Food Crisis? - Inter Press Service