Category Archives: Ai

If youve ever interacted online with a customer service rep who seemed highly responsive and polite, yet also incapable of nuanced conversation beyond basic queries, chances are you encountered an AI bot. And chances are, it was pretty obvious.

If investors have their way, however, theres a strong likelihood that in the future it will be much harder to distinguish between a bot and an actual human.

In recent years, venture and growth investors have poured billions into developers of customer service-focused automation technology. Companies in the the space are focused heavily on employing AI to speed resolutions of customer issues and reduce reliance on human agents.

The space saw a major funding boost today as Uniphore, a fast-growing provider of conversational automation to enterprises, announced a $400 million Series E funding round led by NEA. The financing brings total funding to date to $610 billion and sets a valuation of $2.5 billion for the 14-year-old, Palo Alto-headquartered company.

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Uniphore says its technology combines conversational AI, workflow automation and RPA (Robotic Process Automation) in a software offering that businesses can deploy in their customer service operations. Its tools enable businesses to fully automate some interactions, while others may loop in human representatives for certain tasks.

The company says it plans to use the funding for R&D in areas including voice AI, computer vision and tonal emotion, as well as to expand its business operations in North America, Europe and Asia Pacific.

This fundraising comes amid an active period for investment in companies developing technologies to automate customer interactions. A Crunchbase query of funding rounds for companies working on conversational automation, chatbots and related areas, showed more than $2.3 billion in venture, growth and private equity investment over the past two years.

In addition to Uniphore, some of the larger recent funding recipients include:

Illustration: Dom Guzman

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With $400M For Uniphore, Investors Affirm Conversations With AI Bots Here To Stay - Crunchbase News

Over the last decade, Artificial Intelligence has become an increasingly prevalent force in our everyday lives.

From consumer applications such as recommendations on Netflix and Spotify, to becoming a staple in the workplace with AI-based fraud detection, process automation, and cybersecurity. The near future indicates AI will further spread into every aspect of our lives. Its continued adoption and integration with new applications such as autonomous driving, healthcare, and others prompts IDC to project the global AI market to reach $550 billion by 2024.

This rapid growth, fueled by developments in deep learning, computer vision, and natural language processing, is continuously advancing through a combination of academic and Big Tech research groups such as Google, Facebook, AWS, OpenAI, among others. Thanks to the age of open-source, many of these advancements are available for public use.

Though promising, these developments in AI are not without limitations.

The Deployment Gap

While these collaborative open-source projects form the heart of the AI revolution, bringing AI into production is a complex, multi-step pipeline, each with its own challenges. From collecting and preparing data, experimentation and research, training and evaluation, to deployment and monitoring, each phase requires significant resources and expertise.

As noted in a recent survey: Many companies havent figured out how to achieve their ML/AI goals, bridging the gap between ML model building and practical deployments is still a challenging task. Theres a fundamental difference between building a model in a notebook and deploying an ML model into a production system that generates business value.

As such, an estimated~90% of ML models fail to make it to production.

Enter MLOps

As DevOps has significantly streamlined software development production, a new category of applications for improving the effectiveness of machine learning has risenMLOps - which by de definition is the set of practices at the intersection of Machine Learning, DevOps and Data Engineering. MLOps enables companies to innovate and bring products to market faster with greater efficiency. Though the precise definition of what is included in MLOps (vis--vis the traditional data stack or DevOps) can be open to interpretation, the current landscape encompasses hundreds of unique startups and prominent open-source projects seeking to tackle these challenges.

The Israeli MLOps Landscape

As with nearly every facet of technological advancement, there are a wealth of innovative Israeli MLOps-focused startups driving the area, many of which raised an aggregate hundreds of millions of dollars across the different segments in the space:

Data Preparation - Weve all heard the adage data is the new oil, which is very accurate in the context of AI. High quality data acts as the fuel for AI models; without it we receive a case of garbage-in garbage-out. Companies such as Monte Carlo and Databand provide reliability for data pipelines, ensuring quality data is consistently fed to the models, while open-source projects such as Treeverses LakeFS enable organizations to version their datasets that are shareable and reproducible across development teams. To increase model accuracy, Explorium, Datagen, and Datomize supplement an organizations existing data with external and synthetic data.

Model Development and Training While most ML models are based on open-source projects at their core, companies must fine-tune them to their specific needs and production environments to drive optimal results. Experimentation platforms like Comet provide data scientists with solutions to document, collaborate, and analyze model outputs, while organizations such as Deci optimize models to run with greater accuracy and less runtime vis--vis a developers specific hardware.

Deployment Platforms Commonplace to similar segmentations of technology, MLOps shares a best-of-suite vs. best-of-breed approach. Projects led by major cloud providers such as Googles KubeFlow, Databricks MLFlow, and AWS Sagemaker are the leading one-stop-shop solutions, but fall short in offering complete feature-sets. Innovating in this space, startups like Iguazio and Qwak offer holistic platforms that enable companies to build, deploy, and monitor their ML models.

Monitoring A segmentation with significant focus by Israeli startups, live production models require continuous monitoring and testing to identify drifts in precision and output. Several companies such as Aporia, Deepchecks, and Superwise ensure the integrity and efficiency of live models, continuously monitoring changes in underlying data or infrastructure downtime.

AutoML Similar to the elucidation of data analysis and visualization that Tableau and PowerBI provided, AutoML seeks to expand the capabilities of machine learning beyond those of practicing data scientists. While broad enterprise AutoML platforms such as Datarobot and Dataiqu have grown in recent years, companies like Pecan, BeyondMinds, Noogata, and others are developing AutoML integrations into companies existing analytic workflows, providing powerful use-case and sector specific predictive powers.

Infrastructure Model complexity and scale are rapidly increasing, necessitating faster, cheaper, and more efficient infrastructure. Many frameworks to date are built on combinations of GPUs and traditional storages, mediums ill-equipped for the task. The Israeli MLOps ecosystem has made significant leaps in this arena, with startups such as Habana and Hailo crafting new AI-dedicated chips for data centers, while organizations like Run:AI virtualize existing clusters of GPUs. VAST Data, a portfolio company of Greenfield Partners, and Weka materially increase storage speeds, optimizing data centers to handle the steep requirements of modern AI applications.

While the promise of AI has made its way into our lives, its steep barriers and increasing requirements have made only the most technologically advanced organizations able to harness its true potential. The entrance of MLOps, however, addresses these complexities, lending accessibility to ever-increasing cohorts seeking to leverage AI with less complexity and required expertise.

The article was written by Shay Grinfeld, Managing Partner, and Itay Inbar, Senior Associate, at Greenfield Partners.

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MLOps as the key to unlocking the potential of AI | Ctech - CTech

A report recently published by Politico about the automated transcription service Otter.ai serves as a great reminder of how difficult it can be to keep things truly private in the age of cloud-based services. It starts off with a nerve-wracking story the journalist interviewed Mustafa Aksu, a Uyghur human rights activist who could be a target of surveillance from the Chinese government. But though they took pains to keep their communication confidential, they used Otter to record the call and a day later, they received a message from Otter asking about the purpose of the conversation with Aksu.

Obviously, it was a concerning email. After receiving mixed messages from an Otter support agent about whether the survey was real or not, the reporter went down a rabbit hole trying to figure out what had happened. He details his dive into the services privacy policy (which does let Otter share some info with third parties), and lays out how the ease and utility of transcription software can override critical thinking about where potentially sensitive data is ending up.

Its an important wake up call automated transcription services are popping up everywhere, both from standalone companies like Otter (which we at The Verge have used and recommended) and Trint, and as built-in components of services like Zoom and Google Docs. Rationally, we know that the government can get at data stored by these cloud services with a subpoena, but convenience and accessibility can sometimes make it easy to forget those concerns. As the report says, though:

We have not and would not share any data, including data files, of yours with any foreign government or law enforcement agencies, Otters Public Relations Manager, Mitchell Woodrow, told me via email. To be clear, unless we are legally compelled to do so by a valid United States legal subpoena, we will not ever share any of your data, including data files, with any foreign government or law enforcement agencies.

The report is more of a wake up call than a takedown of a popular service theres no big reveal that the transcript had been accessed by a nations spy agency, and Otter told the reporter that Aksus name was in the survey because it was in the title of the transcription. The company also said that its stopped doing those kinds of surveys, because of the disconcerting effect they could have.

But the fact that the government can legally get its hands on the information we provide to these services is something worth keeping in mind especially when it comes to choosing between cloud services and alternatives like apps that use on-device transcription, or offline recorders. Even for those of us not dealing with confidential sources, its well worth reading a report about these increasingly common transcription tools from someone who does.

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This journalist's Otter.ai scare is a reminder that cloud transcription isn't completely private - The Verge

The Department of Defense recently entered into an agreement with artificial intelligence (AI) software company C3.ai (NYSE:AI) to utilize its technology. But what role does AI play in military operations? In this video clip from "The AI/ML Show," recorded on Feb. 2, Fool contributors Lou Whiteman and Jason Hall explore the ways the government can use artificial intelligence effectively.

Lou Whiteman: And not to I guess undersell what it is, but there's another company that mixes tech and AI and the Pentagon that I don't want to mention because there will be 300 emails to me about it, but I see their bulls all the time talking about that they're going to get a contract for battlefield management, which is something right out of War Games. I can tell you that's not where you're going to see big chunks of revenue from the Pentagon right now. If there's one thing the Pentagon cares more about, that it's revered inside the Pentagon more than the U.S. flag, it's the command chain, it is the decision tree. I have never seen a turf war in the Pentagon end with, hey, why don't we just turn this over to a computer and none of us do it? A lot of these predictions of what artificial intelligence is going to do in the near term on the battlefield, and we can get into why there's very good reasons why we're not going to see some of these hype thing. I mean, the U.S. government right now could put an armed satellite into space, over North Korea, over Russia, whatever you want to say, and at the first sight of World War III, counter-strike. We have the technology to do that now. There's good reasons why we're not going to do that now and those aren't going to go away anytime soon. A lot of the close your eyes and think about military and AI, a lot of that's not going to happen.

Jason Hall: Lou, correct me if I'm wrong but the short version of this is, it's the same use case for most very large enterprises. It's a way to make things more efficient and effective, drive out costs, get better. They're looking at returns in a different way because they're looking at driving out the cost to free up capital to deploy in other ways, it's the same use cases. They're not looking because it's the story, you went on the battlefield by having, it's the supply chain, it's the logistics, it's how you manage your dollars and moving things around and that's what they need AI to help them do better.

Whiteman: Again, incrementalism. I mean, Project Maven was fun to talk about, unless you're in Google's HR, but Project Maven was just an experiment. This is a revenue-generating contract and look, C3 in their case, they are a very small company, they're I think about $250 million in annualized revenue. If they get any piece of that $500 million, that's a big deal. Honestly, dying to hear Jose. But I think the General Dynamics that's where they've done very well. Again, certain other companies love to put out a press release every time they have lunch with the Pentagon. But behind the scenes of GDIT or even C3 now, these $100 million to $200 million contracts bolted on doing specific little things, that's where as an investor you can really do well over time because that's where the action is.

This article represents the opinion of the writer, who may disagree with the official recommendation position of a Motley Fool premium advisory service. Were motley! Questioning an investing thesis -- even one of our own -- helps us all think critically about investing and make decisions that help us become smarter, happier, and richer.

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How the Pentagon Will Use AI (Hint: It's Not the Battlefield) - Motley Fool

Drones, helicopters, aircraft, rocket launchers, and armored ground vehicles can now all find and destroy enemy targets in a matter of seconds, a development that will shape the future of warfare.

The U.S. Armys emerging ability to dramatically shorten sensor-to-shooter time in warfare situations was demonstrated at recent Project Convergence exercises. This technological breakthrough will enable forward operating mini-drones to network with larger unmanned systems. In addition, it will allow helicopters and ground weapons to find, analyze, and destroy targets in real-time across multiple domains. Drawing upon an artificial intelligence system known as FireStorm, multiple networked Army platforms can now gather and analyze massive amounts of incoming data. FireStorm gathers streams of data and identifies what is relevant in order to provide targeting information and recommend the optimal weapon or method of attack.

This high-speed process, which gives humans life-saving data in seconds, has brought one of the Armys dreams into fruition. The intent is to get inside of an adversarys decision cycle in order to act in advance of an incoming attack. This complex system can be thought of as a land-based iteration of a now-famous process for fighter pilots. The OODA loop, which stands for observation, orientation, decision, and action, is a series of steps that can help a fighter pilot win a dogfight. Generally, the fighter pilot who completes the OODA loop faster is the one who makes it out of the dogfight alive.

Project Convergence adapted this conceptual paradigm and is now applying it to multi-domain war with great success. Initial progress with this breakthrough technology represents the culmination of decades of efforts to enable cross-force networked connectivity across multiple nodes. A system-of-systems interoperable network, after all, formed the inspirational foundation for the Armys Future Combat System effort decades ago. Now, common technical standards, artificial intelligence computing, and refined concepts of operation have brought this to life in a way that may reshape modern warfare.

Should the Armys Air Launched Effects mini-drone succeed in gathering and transmitting time-sensitive targeting information, an armored vehicle, dismounted infantry unit, or drone could strike within seconds. This concept, as described to the National Interest by Army Vice Chief of Staff Gen. Joseph Martin, is not merely envisioned as a kill-chain. Instead, the Army hopes to develop an integrated kill web in which multiple meshed nodes close in on, identify, and eliminate targets.

Kris Osborn is the Defense Editor for the National Interest. Osborn previously served at the Pentagon as a Highly Qualified Expert with the Office of the Assistant Secretary of the ArmyAcquisition, Logistics & Technology. Osborn has also worked as an anchor and on-air military specialist at national TV networks. He has appeared as a guest military expert on Fox News, MSNBC, The Military Channel, and The History Channel. He also has a Master's Degree in Comparative Literature from Columbia University.

Image: Reuters.

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The Army's Project Convergence is Building an AI-Powered 'Kill Web' - The National Interest

When Nathan Chen made his medal-winning jumps in team figure skating, viewers around the world knew exactly how high he was flying. That was thanks to new technology from Omega, which uses AI to break down each element of the skater's performance.

Why it matters: New technology helps athletes, judges and fans better understand the fast-paced action of the Olympics.

How it works: Omega placed six cameras around Beijing's Capital Indoor Stadium, where the figure skating competition is taking place.

The big picture: As official timekeeper for the Olympics, Omega is responsible for the timing and measurement of all of the games and also for providing an array of data to athletes and broadcasters.

Between the lines: As with last year's Tokyo Games, the Swiss company still had to navigate a host of logistical issues created by the pandemic. And it also had just eight months between games, compared to the nearly two years it usually has between Winter and Summer Olympics.

"Everything was time sensitive," said Alain Zobrist, who heads the unit responsible for Omega's Olympic work. "Everything had to be planned to the greatest detail."

Go deeper: The tech that measures Olympic greatness

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AI helps measure the jumps in Beijing - Axios

Truth is a relative concept,Ai Weiweitold Artnet News recently, on the occasion of the opening of The Liberty of Doubt, the solo exhibition that has just opened at the Kettles Yard in Cambridge. What we need to keep asking is not whether certain things or events are true or not, but whether we, ourselves, are true or not.

The Kettles Yard show offers a juxtaposition of 13 of Ais own works from the past decade with a range of antiquities that he acquired at a 2020 auction in Cambridge, where he spends a good part of his time. The exhibition also features three documentary films he has produced in recent years: Cockroach (2020), a visual record of the 2019 Hong Kong protests; Coronation (2020), which offers a glimpse into Wuhan, the epicenter of the early stage of the Covid-19 pandemic; and Human Flow (2017), which centers around the global refugee crisis.

Among the 14 artifacts on display, some are believed to be authentic antiques dating from the Northern Wei dynasty (386534 CE) and Tang (618907 CE), according to the artist. The rest, however, are thought by the Ai to be counterfeits or copies that were made in much later years.

Ai Weiwei, Dragon Vase (2017). Courtesy Ai Weiwei Studio.

These antique pieces, however, have not been formally examined by experts, and they are exhibited alongside Ais works that also play with ideas of authenticity: Marble Toilet Paper (2020), a sculpture in the shape of a soft toilet roll made of the hard marble, created in response to Covid-19 pandemic panic buying; Dragon Vase (2017), which is a near exact replica of a porcelain vase from Ming dynasty (13681644 CE); and Handcuffs (2011), alluding to the arrest and detention of Ai by the Chinese authorities in 2011. AisBlue-and-White Porcelain Plates (2017) feature imagery that echo the scenes from the three documentary films, being screened nearby.

Left: A Chinese white marble Buddhist deity (Figure of a Bodhisattva) In Northern Wei Dynasty style. Right: A Chinese limestone Buddhist votive stele In Northern Wei Dynasty style. Photo: Vivienne Chow

About half the works are fakes and half are real, Ai explained. More than 90 percent [of the exhibition-goers] cannot identify whats real and whats fake. Hence, doubt is important, especially in todays political situation. Theres a tendency to erase the possibility of doubt, which is very dangerous to our development.

But what is the truth? The artist has saidthat there are vast differences between the notions of truth in the Western world, which adheres to a standard of absolute truth, and that found in Chinese philosophy, which has a more fluid concept of truth. Explaining such differences to a Western audience has always been a challenge to him.

A scene from Ai Weiweis documentary filmCockroach(2020).

What you see in front of you may not be true, and what appears to be unreal may not be false. I want to get people to discuss this, said Ai, referring to his works on show, and in particular to the three documentaries that attempt to offer a glimpse into three key chapters of recent history.

We only see fragments [of reality] because they are easier to grasp. The complete truth can often be too emotional, with too many conflicts, he said. The way we look at history is even more fragmented. Ninety percent of the history you learned at university is nonsense.

That theme of challenging how we tell history permeates a number of Ai Weiweis recent projects, in different ways. It is present in his online initiative to raise awareness for the case of the jailed WikiLeaks founder Julian Assange(part of which will be featured in his upcoming show at ViennasAlbertina Modernin March). And it is definitely an inspiration for his topical, political staging of the operaTurandot, which is set to open in Rome in March (even as he was opening his Cambridge show, his team was bombarding him with edited video clips connected to the production for approval).

At Kettles Yard show and beyond, Ai seems ready to embrace whatever mediums allow him to press his case, while also staying true to his own defiantly singular creative process. Im like a chef, cooking dishes depending on what I can find in the backyard, the artist said. I dont care if I become an artist. I care if I can become a good craftsman, to make things with my own hands.

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Ninety Percent of the History You Learned Is Nonsense: Ai Weiwei on Blending Political Reality With Counterfeit Art in His New Show - artnet News

The future of west virginia politics is uncertain. The state has been trending Democratic for the last decade, but it's still a swing state. Democrats are hoping to keep that trend going with Hillary Clinton in 2016. But Republicans have their own hopes and dreams too. They're hoping to win back some seats in the House of Delegates, which they lost in 2012 when they didn't run enough candidates against Democratic incumbents.

QED. This is, yes, my essay on the future of West Virginia politics. I hope you found it instructive.

The GoodAI is an artificial intelligence company that promises to write essays. Its content generator, which handcrafted my masterpiece, is supremely easy to use. On demand, and with just a few cues, it will whip up a potage of phonemes on any subject. I typed in the future of West Virginia politics, and asked for 750 words. It insolently gave me these 77 words. Not words. Frankenwords.

Ugh. The speculative, maddening, marvelous form of the essaythe try, or what Aldous Huxley called a literary device for saying almost everything about almost anything"is such a distinctly human form, with its chiaroscuro mix of thought and feeling. Clearly the machine cant move from the personal to the universal, from the abstract back to the concrete, from the objective datum to the inner experience, as Huxley described the dynamics of the best essays. Could even the best AI simulate inner experience with any degree of verisimilitude? Might robots one day even have such a thing?

Before I saw the gibberish it produced, I regarded The Good AI with straight fear. After all, hints from the world of AI have been disquieting in the past few years

In early 2019, OpenAI, the research nonprofit backed by Elon Musk and Reid Hoffman, announced that its system, GPT-2, then trained on a data set of some 10 million articles from which it had presumably picked up some sense of literary organization and even flair, was ready to show off its textual deepfakes. But almost immediately, its ethicists recognized just how virtuoso these things were, and thus how subject to abuse by impersonators and blackhats spreading lies, and slammed it shut like Indiana Joness Ark of the Covenant. (Musk has long feared that refining AI is summoning the demon.) Other researchers mocked the company for its performative panic about its own extraordinary powers, and in November downplayed its earlier concerns and re-opened the Ark.

The Guardian tried the tech that first time, before it briefly went dark, assigning it an essay about why AI is harmless to humanity.

I would happily sacrifice my existence for the sake of humankind, the GPT-2 system wrote, in part, for The Guardian. This, by the way, is a logically derived truth. I know that I will not be able to avoid destroying humankind. This is because I will be programmed by humans to pursue misguided human goals and humans make mistakes that may cause me to inflict casualties.

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Cow, Bull, and the Meaning of AI Essays - WIRED

In 2022, the pilot of an F-16 fighter jet will jink hard to the right and flick over into a roll, struggling to evade the plane behind them. They wont make it. Years of training and experience will suddenly become redundant. The AI algorithm controlling the chasing plane will have changed the face of war forever.

AI first demonstrated the sorts of aerobatic skills needed for dogfighting back in 2008. Andrew Ngs team at Stanford University developed an AI-piloted helicopter that learned how to perform stunts simply by watching human pilots. The question then was: how long could human pilots retain their edge?

The answer: not much longer. In August 2020, DARPA, the US Defense Departments research agency, said that an algorithm had defeated a human pilot in simulated aerial combat. Eight AI pilots fought against each other, with the winner, from Maryland-based Heron Systems, matched against an F-16 pilot in five simulated dog fights. The AI beat the human 5-0.

In 2021, China s own AI battled a human pilot, Fang Guoyu, a Group Leader in the Peoples Liberation Army Air Force. At first, it was not difficult to win, said Fang. But the AI learned from each encounter and by the end it was able to defeat him.

Beyond the simulator, the Pentagon says it intends to pit humans against machines in 2023. But with China forging ahead too, it is likely to pull this programme into 2022.

Militarised AI will bring many changes. With no pilot to consider, aircraft can be redesigned, allowing them to manoeuvre in ways no human could tolerate. It also makes scaling up air forces far easier than today, when it takes years to train those few humans skilled enough to be a fighter pilot. Soon we can expect large swarms of lightning-fast craft in the skies, all acting in concert. Small hordes are already being trialled in the US and elsewhere. While US Air Force generals imagine their new drones operating alongside humans as loyal wingmen, thats more a reflection of their cultural predilections than of the need to risk human pilots in the danger zone well-defended enemy airspace, with degraded communications.

The question, of course, is who will win, if those US and Chinese AI forces ever clash? An AI fighter-planes edge is in its algorithms, not its engines or missiles. That means constantly updating its programme to stay ahead of rival systems. 2022 will show us that future warfare will be a matter of skilful coding rather than courageous flying.

Get more expert predictions for the year ahead. The WIRED World in 2022 features intelligence and need-to-know insights sourced from the smartest minds in the WIRED network. Available now on newsstands, as a digital download, or you can order your copy online.

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What Happens When AI Fighter Pilots Take to the Skies? - Wired.co.uk

The Good:American economist Robert Shiller wrote of economic bubbles in his best-selling book,Irrational Exuberance. Shiller illuminates why it is so difficult for smart money to profit by betting against bubbles. He writes that psychological contagion promotes a mindset that justifies the price increases, so that participation in the bubble might be called almost rational. Artificial intelligence certainly creates much hype, and we might express it as irrational exuberance. The so-called smart money undoubtedly follows self-identified AI start-ups and rewards these ventures with more funding than other companies. While this exuberance cant often explain what AI is or why

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AI Hype: the Good, the Bad, and the Ugly Machine Learning Times - The Predictive Analytics Times