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Inworld AI joins metaverse innovation with AI-driven virtual characters

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Inworld AI, a company that aims to develop a platform that enables users to create AI-driven virtual characters which can be used to populate virtual worlds, announced today that it has raised $7 million in seed funding.

In an exclusive interview, Inworld’s cofounder and CEO Ilya Gelfenbeyn explained that “Inworld AI is a platform for building, basically brains for virtual characters” to populate virtual environments, including the metaverse, VR, and AR worlds. “What we provide is a toolset that enables developers to add brains and build these characters for the world, for different types of environments.”

To successfully create immersive characters, Inworld AI attempts to mimic the cognitive abilities of the human by leveraging a mixture of AI technologies like natural language understanding and processing, optical character recognition, reinforcement learning, and conversational AI to develop sophisticated virtual characters — characters that can even respond to questions and carry on conversations.

Inworld AI isn’t developing a solution to design visual avatars, but instead aims to create an AI development platform that enables companies that produce digital avatars and virtual characters to add more advanced communication to their visual designs.

The end goal of the platform is to offer a platform that visual avatar providers and organizations can use to develop “characters that can interact naturally with wide-ranging and completely open dialog,” Gelfenbeyn said. Although, speech is just the tip of the iceberg in terms of the communicative capabilities of these AI characters.

As Gelfenbeyn notes, “Inworld characters should not be limited to speech only, but be able to interact with many of the modalities that humans use, such as facial gestures, body language, emotions, as well as physical interactions.”

Enhancing the metaverse experience with AI brains

“We structure our technology stack based on inspiration from the human brain. We have three main components: perception, cognition, and behavior. Perception is focused on input and understanding of the environment and other agents, using senses like audio and visual,”  Gelfenbeyn said.

To enable virtual characters to perceive the environment audibly and visually, the organization uses a complex mixture of speech-to-text, rules engines, natural language understanding, OCR, and event triggers.

The next component is cognition.“Cognition is about the internal states of the character, such as memory, emotion, personality, goals, and background,” he said. Here Inworld AI will use natural language processing, emotion recognition, reinforcement learning, and goal-directed conversational AI to enhance the cognitive abilities of virtual characters.

Finally, “behavior is about the output or interactions of the character, such as speech gestures, body language, and motion.” Technologies like state-of-the-art generative language models, reinforcement learning, and customized voice and emotion synthesis,” enable virtual characters to replicate human gestures and behaviors.

Together, these three components provide a solid framework for developers to build virtual characters that can respond in detail to natural language, perceive the digital environment, and offer significant interactions for users.

Investors include Kleiner Perkins, CRV, and Meta. Inworld AI’s launch is well-timed, with publicity for the metaverse at an all-time high following Facebook’s rebrand to Meta, and decision-makers eager to identify what solutions are available to interact with customers in the metaverse.

As Izhar Harmony, General Partner of CRV explained, “the team is growing rapidly, so now is an exciting time for people interested in VR, games, and virtual worlds to partner with and join the company, so they can be at the forefront of this rapidly growing space.”

New kid on the block 

Inworld AI is entering into the highly competitive space of AI and machine learning development and competing against established providers like Open AI, and Google AI, that let you create machine learning models, yet Inworld AI fulfills a unique gap in the market, as it provides a highly specialized solution for developing conversational AI for AI-driven virtual characters, rather than generic machine learning models.

At the same time, the AI solutions that Inworld AI is developing will enable virtual character creation that extends well beyond the complexity of AI-driven avatars like Pandora Bots and Soul Machines.

“Many existing companies have solutions that provide limited answers to script triggers and dialog. In fact, our team built one of the largest providers of such services (API.ai, acquired by Google and now known as Google Dialogflow) so we are very familiar with their capabilities,” Gelfenbeyn said.

“Other companies are beginning to experiment with new technologies (such as large language models) but we believe that these parts, while essential, only provide one piece of the stack necessary to really bring characters to life,” he said.

In other words, these solutions have only scratched the surface of human-AI interactions, and Inworld AI’s approach to replicate human cognition is designed to create much more intelligent virtual entities. While Inworld AI’s mission to build AI brains for virtual characters is ambitious, the team’s AI development pedigree speaks for itself.

Inworld AI’s founders include a swath of experts such as Gelfenbeyn who was previously the CEO of API.ai, chief technology officer Michael Ermolenko, who led machine learning development at API.ai and the Dialogflow NLU/AI team at Google, and product director Kylan Gibbs, who previously led product for applied generative language models at DeepMind.

With this experienced team, the organization is in a strong position to set the standard for interactive virtual characters. After all, “Widespread success of the metaverse and other immersive applications depends on how enveloping those experiences can be,” said Ilya Fushman, investment partner at Kleiner Perkins.

“Inworld AI is building the engine that enables businesses to provide that exciting depth of experience and captivate users. With the team’s track record in providing developers with the tools they need to build AI-fueled applications, we’re excited to support the company in building the future of immersive experiences,” Fushman explained.

Virtual characters are key for immersion

With the metaverse boom beginning to pick up steam, Inworld AI also has a unique role to play in providing providers with a toolset that they can use to create sophisticated virtual characters and create more compelling digital experiences for users. The level of immersion offered by these experiences will determine whether the metaverse lives or dies.

The types of experiences that developers can use Inworld AI to build are diverse. As Gelfenbeyn explained, “Immersive realities continue to accelerate, with an increasingly diverse and fascinating ecosystem of worlds and use cases.”

“Virtual spaces like Meta’s Horizon Worlds, Roblox, Fortnite, and others that offer unique experiences and enable users to exist in other worlds will also continue to see quick demand from businesses, offering everything from games to story content to new enterprise applications,” Gelfenbeyn said.

Although Gelfenbeyn noted that the technology is simply to enable providers to create a “native population” for the digital world to offer realistic experiences, the metaverse is also becoming a new channel that technical decision-makers can use to interact with customers in the future.

While complete, immersive realities with sophisticated virtual characters are a long way off, Inworld AI’s team’s knowledge of conversational AI will undoubtedly enable other providers to move closer toward building vibrant, virtually populated, and interactive digital worlds.

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Byju’s debuts innovation hub for edtech ventures

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India’s Byju’s has created a new innovation hub, dubbed Byju’s lab, where it hopes to bringing together AI and machine-learning experts with educational technology researchers to create new edtech solutions.

The Bengaluru, India-based company has grown to more than 100 million students (and 6.5 million paid subscriptions) with its kids educational games platform, and it has been on a buying spree as it expands well beyond India’s borders.

Byjus’s bought U.S.-based AR game maker Osmo for $120 million in 2019. It also raised $150 million (following a $540 million round) for its global expansion that year. This year, Byju’s launched its Disney-based learning app for U.S. children, and it bought the kids online reading platform Epic for $500 million. And it bought Tynker’s for an estimated $200 million. The acquisitions map back to Byju’s goal of investing $1 billion in the U.S. edtech market over the next three years.

The new hub will redefine the role of tech in learning and transform powerful ideas into solutions. It will hire AI and ML specialists in the United Kingdom, the U.S., and India.

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With a vision to propel and shape the future of education, the new venture will incubate new ideas, provide cutting-edge technologies and deliver breakthrough solutions across Byju’s ecosystem of learning products.

The company said that technology in education is not just about automation, but also about harnessing it in the best way possible to empower students into becoming lifelong learners. It believes that by innovating for the future we will sustain the present, and Byju’s Lab stems out of this philosophy to aid in redefining the role of technology in learning and transforming powerful ideas into solutions.

Byju’s Lab is yet another step in the company’s efforts to continue innovating with the aim to transform learning experiences for children around the world. It is doing so by leveraging cutting edge technologies such as augmented reality, artificial intelligence (AI), computer vision capabilities, gamification and more.

Dev Roy, chief innovation and learning officer at Byju’s said in a statement, “The role of online learning is not just to replicate offline classes in digital space but to make it more interactive, engaging, and personalized. By combining the ability of computing, technology, and data, we at Byju’s Lab, want to explore the power of information and technology to create a more personalized, enhanced, and democratized learning. As a global company, we are looking to harness a global talent pool to build innovative tools and leverage new technologies to positively impact the learning experiences of children across the world. As we continue to grow and experiment, we will operate at the intersection of business and technology to make innovation real and relevant for our end customers. We are looking at strengthening our team and look forward to working with bright and curious minds to transform the way children learn.”

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AI

Announcing the AI Innovation Awards winners at Transform 2021

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After hearing from AI executives, scientists, and leaders during the Transform 2021 virtual conference, it is clear innovation in the field abounds. The AI Innovation Awards caps off a week of celebrating companies and individuals pushing the AI boundaries to discover new capabilities.

The third annual AI Innovation Awards honors people and companies engaged in compelling and influential work in five areas: natural language processing and understanding, business applications, edge innovation, Startup Spotlight, and AI for Good.

Natural language processing/understanding

Many things become possible when machines understand the languages people speak and write. Among those gains, smart assistants can handle more tasks across diverse industries.

Hugging Face received the Innovation in Natural Language Process/Understanding Award for 2021 for the team’s work in democratizing NLP.

Business applications

While research is essential, the true impact of AI comes from practical applications tackling real-world problems.

Pilot received the Innovation in Business Applications Award for 2021 for making the back office experience better for small businesses without deep finance teams. The software’s predictive insights help small businesses make better budgeting and spending decisions.

Edge AI

Edge AI is going to become even more important with the boom in the internet of things and near-ubiquitous network capabilities promised by 5G.

SambaNova Systems received the Innovation in Edge Award for 2021 for developing systems that run AI and data-intensive apps from the datacenter to the edge.

Startup Spotlight

The Startup Spotlight focused on companies that work with AI, have raised $35 million or less in funding, have been in operation for no more than two years, and have the potential to make a significant contribution to the field in the years to come.

Parity received the Startup Spotlight Award for 2021 for its tools and services designed to identify and remove bias from AI systems. And after a week of deep conversations about the importance of ethics and responsible AI, it’s clear this is going to be a very important area to focus on.

AI for Good

AI for Good recognizes AI technology, the application of AI, and advocacy or activism in the field of AI that protects or improves human lives or operates to fight injustice, improve equality, and better serve humanity.

Folding@Home, based out of the School of Medicine at Washington University in St. Louis, with support from its other main labs at Memorial Sloan Kettering Cancer Center and Temple University, received the AI for Good Award for 2021. By employing crowdsourced computer-processing power to help run molecular calculations for diseases, Folding@Home helps scientists study how proteins “misfold” and cause disease and develop therapies for the diseases based on the research. Folding@Home solved some basic problems in the research for SAR-CoV-2, helping scientists in their work for the COVID-19 vaccine.

Innovation and influential work

The nominees were selected by a committee consisting of Vijoy Pandey, the VP of engineering and CTO of cloud and distributed systems at Cisco; Raffael Marty, senior VP of product and cybersecurity at ConnectWise and the former chief research and intelligence officer at Forcepoint; and Stacey Shulman, VP of the IoT Group and general manager of Health, Life Sciences, and Emerging Technologies at Intel. Each of the nominees is a trailblazer involved in influential work in AI, and there will be more opportunities to hear their stories in the years to come.

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VentureBeat presents AI Innovation Awards nominees at Transform 2021

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There is a consistent theme that runs through the agenda for VentureBeat’s Transform 2021 virtual conference, and that is artificial intelligence and data analytics are being used in many different areas and in creative ways. AI is being used in the field of fitness, apparel, energy, and real estate. It’s expected that social media companies founded in the past ten or so years will be using AI and other advanced technologies, but it is extremely reassuring when a company that has served generations is still keeping up with the latest technologies — and doing well.

AI is a complex field and the technology is ever-evolving, and VentureBeat has the front-row seat. There is research pushing boundaries of what is considered possible. There are new products transforming how people work, live, and play. Amidst all of that, there are organizations and individuals working on solving certain challenges in ways that are innovative and creative. That is the purpose of the AI Innovation Awards, where emergent, compelling, and influential work gets recognized. For the third annual AI Innovation Awards honored people and companies engaged in compelling and influential work in five areas: natural language processing and understanding, business applications, edge innovation, “Startup Spotlight”, and AI for Good.

A nominating committee helped the editorial team with the selections. The members of the nominating committee for this year’s AI Innovation Awards were: Vijoy Pandey, the vice-president of engineering and CTO of cloud and distributed systems at Cisco; Raffael Marty, senior vice-president of product, cybersecurity, at Connectwise, and the former chief research and intelligence officer at Forcepoint; and Stacey Shulman, vice-president of the IoT Group and general manager of Health, Life Sciences and Emerging Technologies at Intel. They were generous with their time and knowledge, and provided the editorial team with an intriguing list of individuals and organizations to consider.

Natural language processing/understanding

Many things become possible when machines can understand the language people speak and write. Smart assistants can handle more tasks and are responsible for numerous tasks in different industries. Translation services create a more global world. Productivity tools are more effective. There are so many different use cases, and natural language processing — which includes natural language understanding, natural Language Generation, and Natural Language Interaction — makes it all work.

Primer uses machine learning techniques to help parse and collate a large number of documents across several languages in order to facilitate further investigation. Users feed Primer’s software a stream of documents, and it automatically summarizes what it determines to be the most important information out of that haystack of data. Users are then able to filter by topic, event, and other categories to drill down into the information Primer collected so they can go beyond the automatically generated headlines. Primer’s NLP platform is used by a number of United States federal agencies, and recently raised $110 million in funding.

EleutherAI was founded on the idea of making AI technology that would be open source — and the first one on deck was making am open source model replicating the GPT-3 work from OpenAI. This past March, the EleutherAI team released two trained GPT-style language models, GPT-Neo 1.3B and GPT-Neo 2.7B. The code and the trained models are open sourced under the MIT license and can be used for free via Hugging Face’s Transformers platform. This team is pushing the envelope of NLP research through an open source approach. The platform also makes

Dr. Pei Wang of Temple University was nominated for his Nonaxiomatic Reasoning Engine and its application to NLP. The NARS project, which Wang has been working on for approximately 20 years, attempts to uniformly explain and reproduce many cognitive facilities — including reasoning, learning, and planning — to generate a unified theory, model, and system for AI. The nomination is for Wang’s persistence on his symbolic approach to AI, now being absorbed widely in other applications.

Hugging Face is democratizing NLP by building an open source community for sharing models, datasets, and other resources. The team is conducting research, creating NLP libraries such as Transformers and Tokenizers, and releasing tools to leverage models such as BERT, XLNet, and GPT-2. The nominating committee specifically noted that Sasha Rush — Associate Professor at Cornell Tech — is one of the brains behind Hugging Face.

CoPilot, the project launched by GitHub that acts as a pair programmer and helps developers write better code, may be brand new, but it jumped into the nomination lists because of the way it suggests new code and learns the developer’s coding style. Copilot uses OpenAI Codex, which may be more capable than GPT-3 in terms of programming code.

Business applications

It’s interesting to explore new ideas and compelling research, but the true impact of AI comes from the practical applications. Low-code/no-code tools are helping non-developers create applications and data pipelines. Robotic process automation streamlines workflows and makes business operations more efficient. Intelligent software and services help solve real-world problems. This is where life starts to feel like something out of science-fiction.

Incorta offers all-in-one data crunching service for customers to analyze corporate data spread across multiple databases and then render it all into charts and graphics. The company’s service help organizations acquire, enrich, analyze, and act upon business data — upwards of tens of billions of rows of data become “analytics-ready” without the need to pre-aggregate, reshape, or transform the data in any way. Incorta helps reduce data bloat in organizations.

Dr. Sheila Nirenberg, a neuroscientist for Cornell Medical School, has successfully “cracked the code” for how the retina sends signals to the brain. Her work, combined with optogenetics, help blind people see again. While that is impressive on its own, Dr. Nirenberg was nominated because of the way she has taken what she learned in this field and applied them into the AI space. Her company, Nirenberg Neuroscience, applies the “neural code approach” from a mamalian retina to greatly reduce the amount of training data needed for activity based detection models. This new approach will allow very hard-to-train models to become easy to train with supervised learning.

DeepSee.ai automates manual business processes by combining open source and proprietary machine learning, linguistic comparison and prediction techniques, and sentiment analysis. DeepSee’s cloud-hosted platform captures, extracts, normalizes, labels, and analyzes unstructured data, and then surfaces trends and patterns for review. DeepSee provides a pipeline to deliver AI-generated templates, rules, and logic.

Pilot applies AI to the field of financial tech — fintech — and provides context-specific reporting, insights, and expertise for businesses that may not have an in-depth finance team. Pilot’s software provides automated visibility, error management, and predictive insights to help customers make better budgeting and spending decisions.

Indico allows customers to automate the intake and analysis of document- and image-based workflows. The platform, which can be deployed in private cloud or on-premises environments or as a managed service, ingests PDFs, Word documents, and other unstructured text, images, and documents. Once ingested, the data is processed using natural language understanding models and chained together into pipelines to perform data classification, extraction, and comparison. Indico applies transfer learning — where a model developed for one task is used for another task — to deploy unstructured content more effectively.

Edge

Last year’s awards focused on computer vision, but this year, edge AI is becoming a bigger topic of conversation. The pendulum swings regularly between processing all the data in a centralized location and processing data right on the device. A farmer standing in the middle of the field doesn’t have WiFi — making it really difficult to use the data collected by sensors and other smart devices. This is a situation that is going to be familiar across multiple industries, as the Internet of Things and near ubiquitous network capabilities promised by 5G creates new opportunities with real-time data.

Autonomy Institute is a cooperative research consortium focused on advancing and accelerating autonomy and AI at the edge. The consortium announced a pilot program at the Texas Military Department’s Camp Mabry location in Austin, Texas to build out a test smart city environment to optimize traffic management, autonomous cars, industrial robotics, autonomous delivery, 911 drones, and automated road and bridge inspection. The program deploys the Public Infrastructure Network Node (PINN), a unified open standard supporting 5G wireless, edge computing, radar, lidar, enhanced HPS, and intelligent transportation systems (ITS). PINN clusters in a city deployment could be positioned to collect information from the sensors and cameras at a street intersection. Edge computing using PINN is what will make it possible to process all of the signals and do something about it, such as making the traffic lights change as a car approaches the intersection.

DEKA Research’s ROXO bot was built on top of the iBot wheelchair base — a wheelchair that can climb stairs and lift riders to eye level with others — to fill inventor Dean Kamen’s (person behind the Segway) vision to make wheelchairs more affordable. Removing the chair attachment and replacing with a delivery pod turned the robot into a hardened delivery solution that can drive over nearly any terrain and can climb stairs. Under a partnership with FedEx, the ROXO bot provides package delivery.

Edgeworx turns any computing device — regardless of compute and resources, or operating system — into an edge software platform to allow developers to simply and securely deploy, manage, and orchestration applications from cloud to edge. Its technology was designed from the ground up to be the infrastructure layer for edge devices, and to interface with legacy systems and cloud and data center. Edgeworx enables customers to run real software on edge devices with the same level of security and remote control as they would have in a cloud environment.

SambaNova, which was founded by Oracle and Sun Microsystems veteran Rodrigo Liang and Stanford professors Kunle Olukotun and Chris Ré, develops chips for AI workloads. AI accelerators are a type of specialized hardware designed to speed up AI applications such as neural networks, deep learning, and various forms of machine learning from the data center to the edge.

Multiply Labs, founded by two MIT alumni, has helped pharmaceutical companies produce biologic drugs with its robotic manufacturing platform. Operating at the intersection of robotics and pharmaceutical manufacturing, the company makes the production of individualized drugs at industrial scale possible through automation.

Startup Spotlight

There are many players in the field, from small startups working on one specific idea, academic and private research laboratories pushing the boundaries of what we can do, and large well-funded companies exploring the answers to a broad array of questions. This category focuses on companies that work with AI, have raised $35 million or less in funding, and have been in operation for no more than two years. This award spotlights the startup’s potential to make a significant contribution to the field in the years to come.

Apiiro’s Code Risk Platform accelerates development by allowing organizations to identify and prioritize risky code changes before they become part of the development pipeline. Apiiro can identify and fix security problems during the development process because it analyzes the developer’s behavior to identify potentially risky behaviors that could impact the organization. The platfrom can learn historical behavior of application, infrastructure-as-code, open source components.

Medical Informatics Corporation created Sickbay, a technology platform that uses data to help collect information on the patient. The medical environment is awash in data but they aren’t stored in a place that the medical team can access it.

Udyogyantra is focused on food safety and supply chain transparency. The SmartQC system standardizes and improves food quality by providing real-time insights such as temperature of food, quantity, and consistency

Parity analyzes documentation, identifies risk zones, and recommends methods to mitigate harmful model qualities. Parity offers services designed to identify and remove bias from A.I.

TabNine is based around deep learning AI that studies publicly shared code, primarily through scanning GitHub repos, to suggest time-saving code completions, error predictions and generally make coding better. TabNine also plugs into the preferred IDE.

AI for Good

The AI for Good award honors AI-driven technology, applications, and activism goes beyond just making things easier and faster to making a different. This category looks at people and companies that work to protect human lives, fight injustice, and otherwise improve society. There are ways AI is arguably making the world a better place — or if not better, then at least safer. This award is for AI technology, the application of AI, or advocacy or activism in the field of AI that protects or improves human lives or operates to fight injustice, improve equality, and better serve humanity.

Jake Porway, the founder of DataKind, is pushing to help non-profits connect with experts in the field. As more data scientists get involved, they are looking for opportunities to make suggestions about product development or strategy after analyzing data in a business setting. This is a case where the platform exists specifically to harness people’s know-how for causes that go beyond corporate objectives.

The work Carla Gomes has done in AI has benefitted society: her work on hydrodam locations was in coordination with Brazil. The effort made sure that it hit the right balance between methane production / environmental damage and low cost electricity generation.

David Rolnick wrote a big paper about the various ways that AI can help with climate change that was widely circulated and discussed. In nearly every climate change workshop that has come around in recen months, there is now a bit about using AI technologies. That change came about because of David Rolnick.

The Internet Watch Foundation is a team of 21 individuals who work out of Watch Foundation’s office in Cambridgeshire. These individuals spend hours trawling through images and videos containing child sexual abuse. And, each time they find a photo or piece of footage that is dangerous — it needs to be be assessed and labeled. Last year alone the team identified 153,383 web pages with links to child sexual abuse imagery. This creates a vast database that can then be shared internationally in an attempt to stem the flow of abuse. These classifications are also used to work out how long someone convicted of a crime should be sentenced for.

Folding@Home built the first Exascale Edge compute platform performing AI operations. Over a “Million citizen scientists” (which is really just people installing F@H on their computers) — takes part in which essentially is worlds largest supercomputer. The company platform is compiled and optimized for dozens and dozens of architectures, different Intel, AMD, and then dozens of GPU types/models. Folding@Home solved quite a few basic problems for SAR-CoV-2 which made it possible to begin vaccine production.

The winners will be announced in the morning of July 16 as part of the activities wrapping up Transform 2021.

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Trilogy of Data, analytics, AI is accelerating innovation across industries

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Technology industry veterans Tom Davenport and Tom Seibel have seen firsthand how data, analytics and artificial intelligence have changed business models over the past three decades.

In a conversation with ThoughtSpot Chief Data Strategy Officer Cindi Howson at VentureBeat’s Transform 2021 conference on Tuesday, Davenport and Siebel shared their insights into how technology has boosted innovation and how it can transform industries, as well as the dangers it presents.

Analytics and data

Davenport said the biggest change he’s seen in his career has been the democratization of technology.

“There’s been a continual move toward the software being easier to use, and being able to do more things on its own — automated analytics, automated data science, automated machine learning,” Davenport said. “I think we’re poised for even more democratization, which is great. I think overall there’s some issues that it raises, but it really opens up this field to a lot more people who may not have been nerdy enough to study statistics and get into the details of how you create various models.”

Siebel said it’s difficult to overestimate the impact of the cloud.

“Computing used to be expensive. Storage is something we used to move in and out of machinery with a forklift, he said.

Now, infinite computing capacity and storage are essentially free. And that has allowed startups to grow quickly and compete with more established firms.

“They don’t have to build a big IT infrastructure, they can get it pretty much all from the cloud,” Siebel said.

AI and ethics

Technologies like cloud computing and predictive analytics have enabled classes of applications that solve problems that were previously unsolvable, Siebel said.

“We have large banks, the oil and gas companies, Department of Defense, Food and Drug Administration, travel, transportation companies, all kind of reinventing themselves using predictive analytics to fundamentally change the way that they manage their businesses and deal with customers,” Siebel said.

Despite its benefits, AI has some pitfalls, Howson noted. In 2020, for instance, some felt like their models did not help predict the future.

Davenport attributed this to the slow pace in which humans and organizational culture improve.

“Particularly in these legacy companies, we haven’t made much progress in becoming more data-driven in how we make decisions,” Davenport said. “There’s still a lot in organizations, conservatism, failure to experiment with new technologies and new approaches, to analyzing and understanding data. So that to me is the big problem.”

Additionally, AI presents some ethical issues.

Siebel, who believes the largest commercial application of AI will be in precision medicine, said we can build machine learning models that can do meaningful things like disease prediction. But health care providers via the state or private enterprise can misuse that data, he said.

“Are they going to use it to ration health care? Absolutely,” Siebel said. “Are they going to use it to set rates? Absolutely. … This is very scary stuff.”

He described the privacy and ethical implications of AI “very troubling.”

“They need to be addressed,” Siebel said.

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Tech News

Albemarle opens a Battery Materials Innovation Center in North Carolina

Albemarle certainly isn’t a household name, but it’s a major US-based producer of chemicals, particularly those used in the production of lithium batteries. Lithium batteries are key to all manner of electronic devices and are particularly critical for electric vehicles. The company has announced that it opened a Battery Materials Innovation Center (BMIC) and its Kings Mountain, North Carolina site.

The BMIC will be fully operational in July 2021 and will support the company’s lithium hydroxide, lithium carbonate, and advanced energy storage materials platforms. The facility is designed to enable the synthesis of new materials, material property characterization, and analysis. It also supports material scale-up capabilities and material integration into battery cells for performance testing.

The facility has a dry room with a multi-layer pouch cell line that can create cell phone-sized batteries to demonstrate critical aspects of performance and accelerate the transition of new products to customers. BMIC will also develop lithium metal anode technology to increase battery energy density using advanced lithium metal ruling to achieve lithium foils 20 microns thick. Twenty microns is about one-fifth the average thickness of a human hair.

The facility will demonstrate lithium foils even thinner with a thickness of 3 to 5 microns using new technologies currently under development. Albemarle says that its BMIC provides realistic and relevant cell building capability to generate data for next-generation battery material design. The company will leverage the resources to optimize the materials for creating a drop-in solution for customers to help deliver high-performance and cost-effective batteries to the electric vehicle market.

Albemarle is the only US-based producer of lithium metal anodes. The company says novel materials developed in its labs will enable the next frontier of lithium-ion battery performance. Moving from conventional graphite battery anodes to lithium metal offers the potential to double energy density and reduce cost by as much as 50 percent.

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AI

Gartner’s 2021 Magic Quadrant cites ‘glut of innovation’ in data science and ML

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Gartner’s Magic Quadrant report on data science and machine learning (DSLM) platform companies assesses what it says are the top 20 vendors in this fast-growing industry segment.

Data scientists and other technical users rely on these platforms to source data, build models, and use machine learning at a time when building machine learning applications is increasingly becoming a way for companies to differentiate themselves.

Gartner says AI is still “overhyped” but notes that the COVID-19 pandemic has made investments in DSLM more practical. Companies should focus on developing new use cases and applications for DSML — the ones that are visible and deliver business value, Gartner said in the report released last week. Smart companies should build on successful early projects and scale them.

The report evaluates DSML platforms’ scope, revenue and growth, customer counts, market traction, and product capability scoring. Here are some of the notable findings:

  • Responsible AI governance, transparency, and addressing model-based biases are the most valuable differentiators in this market, and every listed vendor is making progress in these areas.
  • Google and Amazon are finally competing with Microsoft for supremacy in terms of DSML capabilities in the cloud. Amazon wasn’t even included in last year’s Magic Quadrant because it hadn’t shipped its core product by the November 2019 cutoff date. The longest-standing big names in this sector — IBM, MathWorks, and SAS — are still holding their ground and innovating with modern offerings and adaptive strategies.
  • Numerous smaller, younger, and mid-size vendors are in sustained periods of hypergrowth. The growing size of the market feeds startups at all phases of the data science lifecycle. Gartner observes that growing at the rate of the market actually means growing slowly.
  • Alibaba Cloud, Cloudera, and Samsung DDS are included in the Magic Quadrant for the first time.
  • The DSML platform software market grew by 17.5% in 2019, generating $4 billion in revenue. It is the second-fastest-growing segment of the analytics and business intelligence (BI) software market behind modern BI platforms, which grew 17.9%. Its share of the overall analytics and BI market grew to 16.1% in 2019.
  • The most innovative DSML vendors support various types of users collaborating on the same project: data engineers, expert data scientists, citizen data scientists, application developers, and machine learning specialists.

There remains a “glut of compelling innovations” and visionary roadmaps, Gartner says. This is an adolescent market, where vendors are heavily focused on innovation and differentiation, rather than pure execution. Gartner said key areas of differentiation include UI, augmented DSML (AutoML), MLOps, performance and scalability, hybrid and multicloud support, XAI, and cutting-edge use cases and techniques (such as deep learning, large-scale IoT, and reinforcement learning).

Gartner Magic Quadrant of Data Science and Machine Learning

Above: Gartner Magic Quadrant for Data Science and Machine Learning Platforms. (Source: Gartner, March 2021)

Image Credit: Dataiku

Data science and machine learning in 2021 and beyond

For most enterprises, the challenge is to keep up with the rapid pace of change in their industries, driven by how fast their competitors, suppliers, and channel partners are digitally transforming their businesses.

  • CIOs and senior management teams want to understand the specifics of how data science and machine learning models work. A top priority for IT executives working with DSML technologies is understanding bias mitigation and how DSML technologies can control for biases on a per-model basis. Designing transparency should start with model and data repositories, providing greater visibility across an entire DSML platform.
  • Enterprises continue to struggle with moving more AI models from pilot to production. According to the 2020 Gartner AI in Organizations Survey, just 53% of machine learning prototypes are eventually deployed to production. Yield rates from the initial model to production deployment show room for improvement. Look for DSML vendors to step up their efforts to deliver modeling apps and platforms that can accept smaller datasets and still deliver accurate results.
  • Open source software (OSS) is a de facto standard with DSML vendors. OSS provides enterprises the opportunity to get DSML projects up and running with little upfront spending. OSS adoption has become so pervasive that most DSML vendors rely on OSS, starting with Python, the most commonly used language. DSML platform providers also help optimize and curate OSS distributions.
  • For any enterprise to invest in a DSML platform, integration and connectivity are essential. DSML vendors are adopting components for their platform architectures because components are more extensible and can be tailored to an enterprise’s specific needs. Packaged models that integrate into a DSML platform using APIs help enterprises customize machine learning models for specific industry challenges they’re facing.
  • Designing more intuitive interfaces and workflows reduces the learning curve for lines of business and data analysts. Improvements in augmented data science and ML help offload all the data science and modeling work from experienced data scientists to business analysts who prefer to iterate models on their own, often changing constraints based on market conditions.
  • Organizations rely on free and low-cost open source, combined with public cloud providers to reduce costs while experimenting with DSML initiatives. They are then likely to adopt commercial software to tackle broader use cases and requirements for team collaboration and to move models into production.

Which vendors are leading — and why

Here are some company-specific insights included in this year’s Magic Quadrant:

  • SAS Visual Data Mining and Machine Learning (VDMML) is the market leader, having dominated the Leader quadrant for years in this specific Magic Quadrant. Gartner gives SAS credit for its cloud-native architecture, automated feature engineering and modeling, and domain expertise reflected in its advanced prototyping and production refinement use cases. SAS is often seen as a legacy vendor that’s expensive to implement and support. The customer loyalty SAS has accrued in global enterprises and the priority its development teams place on DSML helps the company maintain dominance in this market.
  • IBM’s Watson Studio ascended into the Leader quadrant this year, up from being considered a Challenger in 2020. Gartner believes the company’s completeness of vision (horizontal axis of the quadrant) has improved since last year, moving it into the Leader quadrant. This is mainly due to IBM Watson Studio’s multi-persona support, depth of responsible AI and governance, and component structure proving effective for decision modeling. Building on several years of reinventing itself, IBM can deliver an enterprise-class DSML that will successfully progress beyond the pilot or proof-of-concept phase. Gartner gives IBM credit for capitalizing on previous successes of SPSS, ILOG CPLEX Optimization Studio, earlier analytics products, and the continual stream of innovations from IBM Research.
  • Alteryx’s strong momentum in the market isn’t reflected in its shift from the Leader quadrant to Challenger. Alteryx powered through last year’s uncertainty, reporting a 19% year-over-year increase in revenue for 2020, reaching $495.3 million. Annual recurring revenue grew 32% year over year to reach $492.6 million. Gartner gives Alteryx credit for supporting multiple personas, a proven go-to-market strategy, and delivering excellent customer service and support. Alteryx has proven to be innovative, despite having that attribute mentioned as a caution in the Magic Quadrant.
  • Amazon SageMaker’s market momentum is formidable, further strengthened by its pace of innovation. In February, Amazon Web Services (AWS) announced it has designed and will produce its own machine learning training chip. AWS Trainium is designed to deliver the most teraflops of any machine learning training instance in the cloud. AWS also announced Trainium would support all major frameworks (including TensorFlow, PyTorch, and MXnet). Trainium will use the same Neuron SDK used by AWS Inferentia (an AWS-designed chip for machine learning inference acceleration), making it easy for customers to get started training quickly with AWS Trainium. AWS Trainium is coming to Amazon EC2 and Amazon SageMaker in the second half of 2021. Amazon SageMaker comprises 12 components: Studio, Autopilot, Ground Truth, JumpStart, Data Wrangler, Feature Store, Clarify, Debugger, Model Monitor, Distributed Training, Pipelines, and Edge Manager.
  • Google will launch its unified AI Platform in the first quarter of 2021. This is after the cutoff date for evaluation in this Magic Quadrant. It will release key features like AutoML tables, XAI, AI platform pipelines, and other MLOps services.

The challenges for DSML platform vendors today begin with balancing the needs for greater transparency and bias mitigation while developing and delivering innovative new features at a predictable cadence. The Magic Quadrant reflects current market reality after updating with four new cloud vendors, one with an extensive ecosystem and proven market momentum.

One thing to consider after looking at the Magic Quadrant is that there will be some mergers or acquisitions on the horizon. Look for BI vendors to either acquire or merge with DSML platform providers as the BI market’s direction moves toward augmented analytics and away from visualization. Further fueling potential M&A activity is the fact that DSML platforms could use enhanced data transformation and discovery support at the model level, which is a long-standing strength of BI platforms.

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Tech News

What to expect at Apple’s ‘By Innovation Only’ event: iPhones, Apple Watch, and one more thing

The big day has finally almost arrived. In just about 24 hours, Apple CEO Tim Cook and company will take the stage at the Steve Jobs Theater to unveil all of the new things we’re going to be putting on our holiday wish lists. And we’ll all be able to watch the Apple event live as it happens.

But what are those new things? As usual, the By Innovation Only invitation offers few clues as to what Apple has in store for us, but thankfully that’s not all we have to go on. A steady stream of rumors, a couple iOS 13 ciphers, and some good, old-fashioned intuition give us a pretty good idea of what we can expect from the big show tomorrow, in order of likelihood.

New iPhones

iphone 2019 rumor render Digit.in/@onleaks
The new iPhone is in the spotlight for this event.

Well, duh. It’s the second week in September, a slot that Apple has used to unveil the new iPhone ever since the iPhone 5 in 2012. Even if we didn’t have a lengthy list of rumors about it, we’d bet everything we have that iPhones are arriving tomorrow. But we do have a lengthy list of rumors, so we pretty much know everything about them already:

  • Name: Likely iPhone 11, iPhone 11 Pro, and iPhone 11 Pro Max.

  • Design: The design is expected to be the same as the current iPhone XR, XS, XS Max with one exception: a frosted glass back.

  • Sizes: Identical to the current lineup.

  • Camera: This is likely the marquee new feature. All three models are expected to pick up an extra lens, with the iPhone XR replacement getting a second lens and the upper models getting three, including an ultra wide lens.

  • Performance: As always, the new iPhones will get a new chip, the A13, which will bring speed and graphics boosts. There are rumors of a “matrix” co-processor that will handle AI and AR-related tasks as well, which will free up the main processor for phone-related tasks.

  • Charging: All three models will obviously have both wireless charging (which debuted two years ago), but they’re also expected to gain reverse wireless charging. That means you’ll be able to use the back of the new iPhones to charge an old iPhone or a pair of AirPods in a wireless charging case. Apple is also expected to finally include a better USB power adapter in the box, so you’ll be able to charge your phone quicker.

  • 5G: The new iPhones will most likely not have a 5G modem this year.

  • Colors: There are rumors of a new green and lavender colors making an appearance.

  • Apple Pencil: With the presumed launch of a pro model, the new iPhone 11 Pro models might gain Pencil support, as well as a smaller stylus made for the iPhone.

  • Price: Likely the same as the current lineup, starting at $740 for the iPhone 11 and $999 for the iPhone 11 Pro.

Apple Watch

For the past three years, Apple has used its September event to update the Apple Watch, so you can bet some stage time will be devoted to the next generation of Apple’s best-selling wearable. How much time is the question. After bringing LTE with Series 3 and a larger screen with Series 4, there aren’t too many obvious upgrades for this year’s Apple Watch.

apple watchos 6 noise Apple

We could see a new Apple Watch at the event on Tuesday.

The more persistent report is sleep tracking, an overdue feature that has been rumored to be in the works for a while. However, without a bigger battery, you’ll likely need to either charge your watch before bed or keep a second around just for sleep tracking. That’s not exactly a key selling point, so it’s possible sleep tracking launches in beta or is teased for watchOS 6.1.

There are also reports of new materials such as ceramic and titanium, but it’s unclear whether the new cases will warrant a fill Series 5 designation. It’s looking very likely that Apple keeps the Series 4 branding for another year.

Apple TV+

It’s been six months since Apple officially unveiled Apple TV+, its long-awaited streaming service, and with a November launch looming, there’s still a lot about it we don’t know. So Apple could use this event to tell is a few things about it, most notably how much it’s going to cost. We’re also hoping to hear about which shows will be available at launch, whether seasons will arrive all at once or weekly, and whether there will be any bundle discounts.

Apple Arcade

The other service that’s ready to launch is Apple Arcade, but like Apple TV+, but we still don’t have a full list of games or a price. We expect that to change on Tuesday.



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Tech News

Apple’s ‘Innovation’ event last-minute rumors: Pencil support is out but a new iPad is in

Apple’s annual fall iPhone event is mere hours away, but the rumors are still coming in. First, Bloomberg’s Mark Gurman is predicting that Apple will be dropping the number from the higher-end iPhones, leaving the naming as: iPhone 11, iPhone Pro and iPhone Pro Max. Previous rumors suggested “11” would be included in all three names.

However, the new devices might not be as “pro” as we think. As Macrumors reports, analyst Ming-Chi Kuo has some predictions of his own, and is debunking two rumored features we were counting on. Most notably, he says the new devices will not support the Apple Pencil, so you won’t be able to take notes or draw on the screen.

Also seemingly axed from the release is reverse wireless charging, which would allow you to use the back to charge another device, such as another iPhone or an AirPods case. Samsung already offers this feature on the Galaxy S10 and Note 10, but according to Kuo, “the charging efficiency may not meet Apple’s requirements.” Apple canceled AirPower earlier this year for similar reasons.

But while they won’t be able to share their battery, the new phones might be able to charge faster out of the box. According to Kuo, the iPhone Pro models will ship with an 18W USB-C-to-Lightning power adapter to support fast charging without needing to buy extra equipment. However, the cheaper 6.1-inch iPhone will continue to ship with the same 5W adapter, Kuo says.

Kuo also claims that all three iPhones will offer ultra-wideband (UWB) support for better indoor navigation. That will be helpful for mapping purposes and will presumably play a big role in Apple’s rumored Bluetooth trackers that will likely debut at the event. With UWB, Apple could use augmented reality to track lost items that are out of Bluetooth range.

Finally, Gurman says that Apple will announce its long-rumored 10.2-inch iPad today. He describes it as entry-level, so it will presumably replace the existing sixth-generation 9.7-inch model and retain a similar form factor with Touch ID. The current model starts at $329 and hasn’t been updated since March 2018.

Be sure to tune into the event at 10a.m. PT/1p.m. ET,  and check out Macworld after the event for full coverage of everything that’s announced.

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Tech News

LG V60 ThinQ 5G with Dual Screen: An awfully long name for a phone short on innovation

Update 2/26: LG has provided ballpark pricing for the V60.

You don’t actually need to see the LG V60 ThinQ 5G with Dual Screen to know what it is. Based on our experiences during a recent briefing and hands-on opportunity in New York, the V60 clearly looks like LG’s V50, V40, G8, and G7. It has a few gimmicks and some unnecessary carrier compromises. And it has a headphone jack.

In short, it’s an LG phone. It’s not entirely clear whether it will be the only flagship of 2020 from LG, but if there is a G9 in the works, my guess is that the only difference will be screen size. For better or worse, LG has a formula, and it’s sticking to it. I’m not really sure what, if anything, the ThinQ surname even means at this point, but you won’t find the G8’s 3D face unlock or touche commands, nor the V50’s tailored intelligence services in this iteration.

lg v60 dual screen Ben Patterson/IDG

The Dual Screen’s second display perfectly mirrors the one on the V60 itself, notch and all.

That said, the LG V60 ThinQ 5G is a perfectly fine 2020 phone. With a 6.8-inch screen and more bezel than the Galaxy S20 Ultra has, it’s a bit too big for my tastes, but LG is merely following the big-screen trend line. The Dual Screen case that we first saw on the G8X has some useful features, but it still feels like a very cheap imitation of the Galaxy Fold.

But if you’re looking for an Android phone with premium specs, the V60 checks off most of the boxes. We don’t know yet how much all of those components will cost—one of LG’s irksome carrier capitulations—but it’s certainly ready to go head to head with the S20 on paper:

  • Dimensions: 169.3 x 77.6 x 8.79mm
  • Display: 6.8-inch Full HD OLED
  • Processor: Snapdragon 865
  • RAM: 8GB
  • Storage: 256GB
  • Rear camera: Dual 64MP, f/1.8, OIS + 13MP Ultra Wide (117 deg), f/1.9
  • Front camera: 10MP, f/1.9
  • Battery: 5,000mAh
  • OS: Android 10
  • Colors: Classy Blue, Classy White

Those are all perfectly fine specs, though the 1080p 60Hz display will certainly show its inferiority alongside the S20 and upcoming OnePlus 8. LG has lagged somewhat with its smartphone displays, having only recently made the switch to OLED—so the V60 feels a bit behind the times, even with a screen size nearly as big as the 6.9-incher on the Galaxy S20 Ultra.

lg v60 full Ben Patterson/IDG

The V60 is the biggest V phone so far.

The same can be said of the design. While the corners are a bit more rounded, the V60 follows the same basic language LG introduced with the G7, and it very much looks like it’s stuck in 2018. The camera cutout at the top of the display is significantly smaller than the G8X’s, but the V60’s sizable top bezel doesn’t do it any favors. The chin is just as large, and the side bezels, while not as big as the top and bottom, are still distractingly thick.

Speaking of thick, the phone is pretty chunky at nearly 8.8mm, though its slight teardrop shape does well to hide it. At 218 grams it’s a bit lighter than the S20 Ultra (222g), though it’s less top-heavy due to the horizontal camera array. When you add the Dual Screen case, however, it tips the scales at nearly 350 grams.

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