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[Editors note: Machine Learning Breakthroughs Have Sparked the AI Revolution was previously published in February 2022. It has since been updated to include the most relevant information available.]

Its October 1950. Alan Turing, the genius who cracked the Enigma code and helped end World War II, has just introduced a novel concept.

Its called the Turing Test, and its aimed at answering the fundamental question:Can machines think?

The world laughs. Machines think for themselves? Not possible.

However, the Turing Test sets in motion decades of research into the emerging field of Artificial Intelligence (AI).

This research is conducted in the worlds most prestigious labs by some of the worlds smartest people. Collectively, theyre working to create a new class of computers and machines that can, indeed, think for themselves.

Fast forward 70 years.

AI is everywhere.

Its in yourphones. What do you think powers Siri? How does a phone recognize your face?

Its in yourapplications. How does Google Maps know directions and optimal routes? How does it make real-time changes based on traffic? And how does Spotify create hyper-personalized playlists or Netflix recommend movies?

AI is on yourcomputers. How does Google suggest personalized search items for you? How do websites use chatbots that seem like real humans?

As it turns out, the world shouldnt have laughed back in 1950.

The great Alan Turing ended up creating a robust foundation upon which seven decades of groundbreaking research has compounded. Ultimately, it resulted in self-thinking computers and machines not just being a thing but being everything.

Make no mistake. This decades-in-the-making AI Revolution is just getting started.

Thats because AI is mostly built on what industry insiders call machine learning (ML) and natural language processing (NLP) models. And these models are informed with data.

Accordingly, the more data they have, the better the models get and the more capable the AI becomes.

When I say identity, what do you think of?

If youre like me, you immediately start to think of what makes you, well, you your height, eye color; what job you have, what car you drive, what shows you like to binge-watch.

In other words, the amount of data associated with each individual identity is both endless and unique.

Those attributes make identity data extremely valuable.

Up until recently, though, enterprises had no idea how to extract value from this robust dataset. Thats all changing right now.

Breakthroughs in artificial intelligence and machine-learning technology are enabling companies to turn identity data into more personalized, secure and streamlined user experiences for their customers, employees and partners.

The volume and granularity of data is exploding right now. Thats mostly because every object in the world is becoming a data-producing device.

Dumb phones have become smartphones and have started producing a ton of usage data.

Dumb cars have become smart cars and have started producing lots of in-car driving data.

And dumb apps have become smart apps and have started producing heaps of consumer preference data.

Dumb watches have become smartwatches and have started producing bunches of fitness and activity data.

As weve sprinted into the Smart World, the amount of data that AI algorithms have access to has exploded. And its making them more capable than ever.

Why else has AI started popping up everywhere in recent years? Its because90% of the worlds data was generated in the last two years alone.

More data, better ML and NLP models, smarter AI.

Its that simple.

And guess what? The world isnt going to take any steps back in terms of this smart pivot. No. We love our smartphones, smart cars and smartwatches far too much.

Instead, society will accelerate in this transition. Globally, the world produces about 2.5 exabytes of data per day. By 2025, that number is expected to rise to 463 exabytes.

Lets go back to our process.

More data, better ML and NLP models, smarter AI.

Thus, as the volume of data produced daily soars more than 185X over the next five years, ML and NLP models will get 185X better (more or less). And AI machines will get 185X smarter (more or less).

Folks, the AI Revolution is just getting started.

Most things a human does, a machine will soon be able to do better, faster and cheaper.

Given the advancements AI has made over the past few years with the help of data and the exponential amount of it yet to come Im inclined to believe this.

Eventually, and inevitably, the world will be run by hyperefficient and hyperintelligent AI.

Im not alone in thinking this. Gartner predicts that 69% of routine office work will be fully automated by 2024. And the World Economic Forum has said that robots will handle 52% of current work tasks by 2025.

The AI Revolution is coming and its going to be the biggest youve seen in your lifetime.

You need to be invested in this emerging tech megatrend that promises to change the world forever.

Of course, the question remains: What AI stocks should you start buying right now?

You could play it safe and go with the blue-chip tech giants. All are making inroads with AI and are low-risk, low-reward plays on the AI Revolution. Im talking Microsoft (MSFT), Alphabet (GOOG), Amazon (AMZN), Adobe (ADBE) and Apple (AAPL).

However, thats not how we do things. We dont like safe we like best.

At present, enterprise AI software is being used very effectively by Big Tech. And its being used ineffectively or not at all by everyone else.

Todays AI companies are changing that. And the best way to play the AI Revolution is by buying the stocks that are changing the paradigm in which they exist.

We have identified several AI stocks to buy for enormous long-term returns.

Again, these AI stocks arent the safe way to play the AI Revolution. Theyre the best way to do it.

One company is pioneering a novel model-driven architecture. Indeed, it represents a promising paradigm shift in the AI application development process. Ultimately, it will democratize the power of AI so that its no longer a weapon used by Big Tech to crush its opponents.

Essentially, this company has pre-built multiple, highly scalable AI models in its ecosystem. And it allows customers to build their own AI models by simply editing and stacking them atop one another.

Think of building an AI application as a puzzle. You must have the right pieces and directions. In other words, to effectively utilize the power of enterprise AI, customers just piece it together in a way that works best for them.

Equally important, the building of these puzzles is not rocket science. The company does all the hard work of making the actual models. Customers simply have to pick which ones they want to use and decide how they want to use them.

In some instances, coding and data science are still required but not much. Todays top AI companies make it easy to develop, scale, and apply insights without writing any code.

Its a genius breakthrough to address the widening AI gap between Big Tech and everyone else.

Eventually, every company from every industry and of every size will leverage the power of AI to enhance their business, increase revenues and reduce costs.

Of course, this reality bodes well for AI stocks in the long term.

You just have to know which ones are worth buying and which are not

On the date of publication, Luke Lango did not have (either directly or indirectly) any positions in the securities mentioned in this article.

Original post:
Machine Learning Breakthroughs Have Sparked the AI Revolution - InvestorPlace

DENVER, July 28, 2022--(BUSINESS WIRE)--Palantir Technologies Inc. (NYSE: PLTR) today announced that it will expand its work with the U.S. Army Research Laboratory to implement data and artificial intelligence (AI)/machine learning (ML) capabilities for users across the combatant commands (COCOMs). The contract totals $99.9 million over two years.

Palantir first partnered with the Army Research Lab to provide those on the frontlines with state-of-the-art operational data and AI capabilities in 2018. Palantirs platform has supported the integration, management, and deployment of relevant data and AI model training to all of the Armed Services, COCOMs, and special operators. This extension grows Palantirs operational RDT&E work to more users globally.

"Maintaining a leading edge through technology is foundational to our mission and partnership with the Army Research Laboratory," said Akash Jain, President of Palantir USG. "Our nations armed forces require best-in-class software to fulfill their missions today while rapidly iterating on the capabilities they will need for tomorrows fight. We are honored to support this critical work by teaming up to deliver the most advanced operational AI capabilities available with dozens of commercial and public sector partners."

By working with the U.S. Army Research Lab, integrating with partner vendors, and iterating with users on the front lines, Palantirs software platforms will continue to quickly implement advanced AI capabilities against some of DODs most pressing problem sets. "Were looking forward to fielding our newest ML, Edge, and Space technologies alongside our U.S. military partners," said Shannon Clark, Senior Vice President of Innovation, Federal. "These technologies will enable operators in the field to leverage AI insights to make decisions across many fused domains. From outer space to the sea floor, and everything in between."

About Palantir Technologies Inc.

Foundational software of tomorrow. Delivered today. Additional information is available at https://www.palantir.com.

Forward-Looking Statements

This press release contains forward-looking statements within the meaning of Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934, as amended. These statements may relate to, but are not limited to, Palantirs expectations regarding the amount and the terms of the contract and the expected benefits of our software platforms. Forward-looking statements are inherently subject to risks and uncertainties, some of which cannot be predicted or quantified. Forward-looking statements are based on information available at the time those statements are made and were based on current expectations as well as the beliefs and assumptions of management as of that time with respect to future events. These statements are subject to risks and uncertainties, many of which involve factors or circumstances that are beyond our control. These risks and uncertainties include our ability to meet the unique needs of our customer; the failure of our platforms to satisfy our customer or perform as desired; the frequency or severity of any software and implementation errors; our platforms reliability; and our customers ability to modify or terminate the contract. Additional information regarding these and other risks and uncertainties is included in the filings we make with the Securities and Exchange Commission from time to time. Except as required by law, we do not undertake any obligation to publicly update or revise any forward-looking statement, whether as a result of new information, future developments, or otherwise.

View source version on businesswire.com: https://www.businesswire.com/news/home/20220728005319/en/

Contacts

Media Contact Lisa Gordonmedia@palantir.com

Go here to see the original:
U.S. Army Research Lab Expands Artificial Intelligence and Machine Learning Contract with Palantir for $99.9M - Yahoo Finance

Documents have spent decades stubbornly guarding their contents against software. In the late 1960s, the first OCR (optical character recognition) techniques turned scanned documents into raw text. By indexing and searching the text from these digitized documents, software sped up formerly laborious legal discovery and research projects.

Today, Google, Microsoft, and Amazon provide high-quality OCR as part of their cloud services offerings. But documents remain underused in software toolchains, and valuable data languish in trillions of PDFs. The challenge has shifted from identifying text in documents to turning them into structured data suitable for direct consumption by software-based workflows or direct storage into a system of record.

The prevailing assumption is that machine learning, often embellished as AI, is the best way to achieve this, superseding outdated and brittle template-based techniques. This assumption is misguided. The best way to turn the vast majority of documents into structured data is to use the next generation of powerful, flexible templates that find data in a document much as a person would.

The promise of machine learning is that you can train a model once on a large corpus of representative documents and then smoothly generalize to out-of-sample document layouts without retraining. For example, you want to train an ML model on company A, B, and Cs home insurance policies, and then extract the same data from similar documents issued by company Z. This is very difficult to achieve in practice for three reasons:

Your goal is often to extract dozens or hundreds of individual data elements from each document. A model at the document level of granularity will frequently miss some of these values, and those errors are quite difficult to detect. Once your model attempts to extract those dozens or hundreds of data elements from out-of-sample document types, you get an explosion of opportunities for generalization failure.

While some simple documents might have a flat key/value ontology, most will have a substructure:think of a list of deficiencies in a home inspection report or the set of transactions in a bank statement. In some cases youll even encounter complex nested substructures:think of a list of insurance policies, each with a claims history. You either need your machine learning model to infer these hierarchies, or you need to manually parameterize the model with these hierarchies and the overall desired ontology before training.

A document is anything that fits on one or more sheets of paper and contains data! Documents are really just bags of diverse and arbitrary data representations. Tables, labels, free text, sections, images, headers and footers: you name it and a document can use it to encode data. There's no guarantee that two documents, even with the same semantics, will use the same representational tools.

It's no surprise that ML-based document parsing projects can take months, require tons of data up front, lead to unimpressive results, and in general be "grueling" (to directly quote a participant in one such project with a leading vendor in the space).

These issues strongly suggest that the appropriate angle of attack for structuring documents is at the data element level rather than the whole-document level. In other words, we need to extract data from tables, labels, and free text; not from a holistic document. And at the data element level, we need powerful tools to express the relationship between the universe of representational modes found in documents and the data structures useful to software.

So let's get back to templates.

Historically, templates have had an impoverished means of expressing that mapping between representational mode and data structure. For example, they might instruct: go to page 3 and return any text within these box coordinates. This breaks down immediately for any number of reasons, including if:

None of these minor changes to the document layout would faze a human reader.

For software to successfully structure complex documents, you want a solution that sidesteps the battle of months-long ML projects versus brittle templates. Instead, lets build a document-specific query language that (when appropriate) embeds ML at the data element, rather than document, level.

First, you want primitives (i.e., instructions) in the language that describe representational modes (like a label/value pair or repeating subsections) and stay resilient to typical layout variations. For example, if you say:

Find a row starting with this word and grab the lowest dollar amount from it

You want row recognition thats resilient to whitespace variation, vertical jitter, cover pages, and document skew, and you want powerful type detection and filtering.

Second, for data representations with a visual or natural language component, such as tables, checkboxes, and paragraphs of free text, the primitives should embed ML. At this level of analysis, Google, Amazon, Microsoft, and OpenAI all have tools that work quite well off the shelf.

Sensible takes just that approach: blending powerful and flexible templates with machine learning. With SenseML, our JSON-based query language for documents, you can extract structured data from most document layouts in minutes with just a single reference sample. No need for thousands of training documents and months spent tweaking algorithms, and no need to write hundreds of rules to account for tiny layout differences.

SenseMLs wide range of primitives allows you to quickly map representational modes to useful data structures, including complex nested substructures. In cases where the primitives do not use ML, they behave deterministically to provide strong behavior and accuracy guarantees. And even for the non-deterministic output of our ML-powered primitives, such as tables, validation rules can identify errors in the ML output.

What this means is that document parsing with Sensible is incredibly fast, transparent, and flexible. If you want to add a field to a template or fix an error, it's straightforward to do so.

The tradeoff for Sensibles rapid time to value is that each meaningfully distinct document layout requires a separate template. But this tradeoff turns out to be not so bad in the real world. In most business use cases, there are a countable number of layouts (e.g., dozens of trucking carriers generating rate confirmations in the USA; a handful of software systems generating home inspection reports). Our customers dont create thousands of document templates most generate tremendous value with just a few.

Of course, for every widely used tax form, insurance policy, and verification of employment, collectively we only need to create a template once. Thats why weve introduced

Our open-source Sensible Configuration Library is a collection of over 100 of the most frequently parsed document layouts, from auto insurance policies to ACORD forms, loss runs, tax forms, and more. If you have a document that's of broad interest, we'll do the onboarding for you and then make it freely available to the public. It will also be free for you to use for up to 150 extractions per month on our free account tier.

We believe that this hybrid approach is the path to transparently and efficiently solving the problem of turning documents into structured data for a wide range of industries, including logistics, financial services, insurance, and healthcare. If you'd like to join us on this journey and connect your documents to software,schedule a demo or sign up for a free account!

L O A D I N G. . . comments & more!

Link:
Machine Learning is the Wrong Way to Extract Data From Most Documents - hackernoon.com

According to the latest research by SkyQuest Technology, the Global Machine Learning Market was valued at US$ 16.2 billion in 2021, and it is expected to reach a market size of US$ 164.05 billion by 2028, at a CAGR of 39.2 % over the forecast period 20222028. The research provides up-to-date Machine Learning Market analysis of the current market landscape, latest trends, drivers, and overall market environment.

Software systems may forecast events more correctly with the use of machine learning (ML), a type of artificial intelligence (AI), without needing to be explicitly told to do so. Machine learning algorithms use historical data as input to anticipate new output values. As organizations adopt more advanced security frameworks, the global machine learning market is anticipated to grow as machine learning becomes a prominent trend in security analytics. Due to the massive amount of data being generated and communicated over several networks, cyber professionals struggle considerably to identify and assess potential cyber threats and assaults.

Machine-learning algorithms can assist businesses and security teams in anticipating, detecting, and recognising cyber-attacks more quickly as these risks become more widespread and sophisticated. For example, supply chain attacks increased by 42% in the first quarter of 2021 in the US, affecting up to 7,000,000 people. For instance, AT&T and IBM claim that the promise of edge computing and 5G wireless networking for the digital revolution will be proven. They have created virtual worlds that, when paired with IBM hybrid cloud and AI technologies, allow business clients to truly experience the possibilities of an AT&T connection.

Computer vision is a cutting-edge technique that combines machine learning and deep learning for medical imaging diagnosis. This has been accepted by the Microsoft InnerEye programme, which focuses on image diagnostic tools for image analysis. For instance, using minute samples of linguistic data, an AI model created by a team of researchers from IBM and Pfizer can accurately forecast the eventual onset of Alzheimers disease in healthy persons by 71 percent (obtained via clinical verbal cognition tests).

Read Market Research Report, Global Machine Learning Market by Component, (Solutions, and Services), Enterprise Size (SMEs And Large Enterprises), Deployment (Cloud, On-Premise), End-User [Healthcare, Retail, IT and Telecommunications, Banking, Financial Services and Insurance (BFSI), Automotive & Transportation, Advertising & Media, Manufacturing, Others (Energy & Utilities, Etc.)], and Region Forecast and Analysis 20222028 By Skyquest

Get Sample PDF : https://skyquestt.com/sample-request/machine-learning-market

Large enterprises segment dominated the machine learning market in 2021. This is because data science and artificial intelligence technologies are being used more often to incorporate quantitative insights into business operations. For instance, under a contract between Pitney Bowes and IBM, IBM will offer managed infrastructure, IT automation, and machine learning services to help Pitney Bowes convert and adopt hybrid cloud computing to support its global business strategy and goals.

Small and midsized firms are expected to grow considerably throughout the anticipated timeframe. It is projected that AI and ML would be the main technologies allowing SMEs to reduce ICT investments and access digital resources. For instance, the IPwe Platform, IPwe Registry, and Global Patent Marketplace are just a few of the small- and medium-sized firms (SMEs) and other organizations that are reportedly already using IPwes technology.

The healthcare sector had the biggest share the global machine learning market in 2021 owing to the industrys leading market players doing rapid research and development, as well as the partnerships formed in an effort to increase their market share. For instance, per the terms of the two businesses signed definitive agreement, Francisco Partners would buy IBMs healthcare data and analytics assets that are presently a part of the Watson Health company. An established worldwide investment company with a focus on working with IT startups is called Francisco Partners. Francisco Partners acquired a wide range of assets, including Health Insights, MarketScan, Clinical Development, Social Program Management, Micromedex, and imaging software services.

The prominent market players are constantly adopting various innovation and growth strategies to capture more market share. The key market players are IBM Corporation, SAP SE, Oracle Corporation, Hewlett Packard Enterprise Company, Microsoft Corporation, Amazon Inc., Intel Corporation, Fair Isaac Corporation, SAS Institute Inc., BigML, Inc., among others.

The report published by SkyQuest Technology Consulting provides in-depth qualitative insights, historical data, and verifiable projections about Machine Learning Market Revenue. The projections featured in the report have been derived using proven research methodologies and assumptions.

Speak With Our Analyst : https://skyquestt.com/speak-with-analyst/machine-learning-market

Report Findings

What does this Report Deliver?

SkyQuest has Segmented the Global Machine Learning Market based on Component, Enterprise Size, Deployment, End-User, and Region:

Read Full Report : https://skyquestt.com/report/machine-learning-market

Key Players in the Global Machine Learning Market

About Us-SkyQuest Technology Group is a Global Market Intelligence, Innovation Management & Commercialization organization that connects innovation to new markets, networks & collaborators for achieving Sustainable Development Goals.

Find Insightful Blogs/Case Studies on Our Website:Market Research Case Studies

Read more:
Global Machine Learning Market is Expected to Grow at a CAGR of 39.2 % by 2028 - Digital Journal

image:The core of EnCORE: co-principal investigators include (from l to r) Yusu Wang, Barna Saha (the principal investigator), Kamalika Chaudhuri, (top row) Arya Mazumdar and Sanjoy Dasgupta. view more

Credit: University of California San Diego

A new National Science Foundation initiative has created a $10 million dollar institute led by computer and data scientists at University of California San DIego that aims to transform the core fundamentals of the rapidly emerging field of Data Science.

Called The Institute for Emerging CORE Methods in Data Science (EnCORE), the institute will be housed in the Department of Computer Science and Engineering (CSE), in collaboration with The Halcolu Data Science Institute (HDSI), and will tackle a set of important problems in theoretical foundations of Data Science.

UC San Diego team members will work with researchers from three partnering institutions University of Pennsylvania, University of Texas at Austin and University of California, Los Angeles to transform four core aspects of data science: complexity of data, optimization, responsible computing, and education and engagement.

EnCORE will join three other NSF-funded institutes in the country dedicated to the exploration of data science through the NSFs Transdisciplinary Research in Principles of Data Science Phase II (TRIPODS) program.

The NSF TRIPODS Institutes will bring advances in data science theory that improve health care, manufacturing, and many other applications and industries that use data for decision-making, said NSF Division Director for Electrical, Communications and Cyber Systems Shekhar Bhansali.

UC San Diego Chancellor Pradeep K. Khosla said UC San Diegos highly collaborative, multidisciplinary community is the perfect environment to launch and develop EnCORE. We have a long history of successful cross-disciplinary collaboration on and off campus, with renowned research institutions across the nation. UC San Diego is also home to the San Diego Supercomputer Center, the HDSI, and leading researchers in artificial intelligence and machine learning, Khosla said. We have the capacity to house and analyze a wide variety of massive and complex data sets by some of the most brilliant minds of our time, and then share that knowledge with the world.

Barna Saha, the EnCORE project lead and an associate professor in UC San Diegos Department of Computer Science and Engineering and HDSI, said: We envision EnCORE will become a hub of theoretical research in computing and Data Science in Southern California. This kind of national institute was lacking in this region, which has a lot of talent. This will fill a much-needed gap.

The other UC San Diego faculty members in the institute include professors Kamalika Chaudhury, and Sanjoy Dasgupta from CSE; Arya Mazumdar (EnCORE co-principal investigator), Gal Mishne, and Yusu Wang from HDSI; and Fan Chung Graham from Mathematics. Saura Naderi of HDSI will spearhead the outreach activities of the institute.

Professor Barna Saha has assembled a team of exceptional scholars across UC San Diego and across the nation to explore the underpinnings of data science. This kind of institute, focused on groundbreaking research, innovative education and effective outreach, will be a model of interdisciplinary initiatives for years to come, said Department of Computer Science and Engineering Chair Sorin Lerner.

CORE Pillars of Data Science

The EnCORE Institute seeks to investigate and transform three research aspects of Data Science:

EnCORE represents exactly the kind of talent convergence that is necessary to address the emerging societal need for responsible use of data. As a campus hub for data science, HDSI is proud of a compelling talent pool to work together in advancing the field, said HDSI founding director Rajesh K. Gupta.

Team members expressed excitement about the opportunity of interdisciplinary research that the institute will provide. They will work together to improve privacy-preserving machine learning and robust learning, and to integrate geometric and topological ideas with algorithms and machine learning methodologies to tame the complexity in modern data. They envision a new era in optimization with the presence of strong statistical and computational components adding new challenges.

One of the exciting research thrusts at EnCORE is data science for accelerating scientific discoveries in domain sciences, said Gal Mishne, a professor at HDSI. As part of EnCORE, the team will be developing fast, robust low-distortion visualization tools for real-world data in collaboration with domain experts. In addition, the team will be developing geometric data analysis tools for neuroscience, a field which is undergoing an explosion of data at multiple scales.

From K-12 and Beyond

A distinctive aspect to EnCORE will be the E, education and engagement, component.

The institute will engage students at all levels, from K-12 to postdoctoral students, and junior faculty and conduct extensive outreach activities at all of its four sites.

The geographic span of the institute in three regions of the United States will be a benefit as the institute executes its outreach plan, which includes regular workshops, events, hiring of students and postdoctoral students. Online and joint courses between the partner institutions will also be offered.

Activities to reach out to high school, middle school and elementary students in Southern California are also part of the institutes plan, with the first engagement planned for this summer with the Sweetwater Union High School District to teach students about the foundations of data science.

There will also be mentorship and training opportunities with researchers affiliated with EnCORE, helping to create a pipeline of data scientists and broadening the reach and impact of the field. Additionally, collaboration with industry is being planned.

Mazumdar, an associate professor in the HDSI and an affiliated faculty member in CSE, said the team has already put much thought and effort into developing data science curricula across all levels. We aim to create a generation of experts while being mindful of the needs of society and recognizing the demands of industry, he said.

We have made connections with numerous industry partners, including prominent data science techs and also with local Southern California industries including start-ups, who will be actively engaged with the institute and keep us informed about their needs, Mazumdar added.

An interdisciplinary, diverse field- and team

Data science has footprints in computer science, mathematics, statistics and engineering. In that spirit, the researchers from the four participating institutions who comprise the core team have diverse and varied backgrounds from four disciplines.

Data science is a new, and a very interdisciplinary area. To make significant progress in Data Science you need expertise from these diverse disciplines. And its very hard to find experts in all these areas under one department, said Saha. To make progress in Data Science, you need collaborations from across the disciplines and a range of expertise. I think this institute will provide this opportunity.

And the institute will further diversity in science, as EnCORE is being spearheaded by women who are leaders in their fields.

Continued here:
New $10M NSF-funded institute will get to the CORE of data science - EurekAlert