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Forbes issued a new list of AI 50 2022: North America’s Top AI Companies. The fourth annual list, done in partnership with Sequoia Capital, recognizes most prominent privately-held North American companies making the most interesting and effective use of AI technology.

🔹 IDC research firm confirms that investments in AI research and applications are set to hit $500 billion by 2024.

🔹 PwC predicts AI will contribute $15.7 trillion to the global economy by 2030.

🔹 The newcomer to the list is Hugging Face, a freshly turned $2 billion unicorn. Hugging Face is an open-source platform with plug-and-play ML models used by developers to build features like search, text moderation, image segmentation.

#AI

When Andrey Fillimonov started the research in the field of complex analytics of the mental and physiological state of drivers, potential customers were сonfused. His team was often looked at as people doing who knows what. Now, this technology based on machine learning is becoming the industry standard.

Global CIO spoke to Andrey Fillimonov about the development of AI products and his expectations for this technology in the future.

#AI #MachineLearning

Last month, McKinsey presented the case of the implementation of an advanced analytic system in shrimp aquaculture. An exotic field demanded non-standard solutions. Here’s how McKinsey gained business productivity on the shrimp farm through IoT.

Compared with other industries, shrimp production fell behind in the use of technology across the value chain. Farmers often operated with highly manual processes and information written out with pen and paper.

McKinsey provided an analytics solution and set up a full digital ecosystem comprised of Internet of Things (IoT) equipment such as automatic feeders, sensors, and hydrophones to collect real-time data and standardize feed distribution. Also, the company designed a set of predictive analytics models to optimize the quantity and time of feed and a technical-advisory service that helps implement the solution in the field.

As a result, McKinsey managed to deploy the system across 300 hectares of shrimp ponds, achieving a median increase in biomass of 16% and reducing production cycles by 15%.

For more details, read the case on the company’s page.

#IOT

Gartner published new Essential Guides for Effective Decision Making. In the new research paper, the company points out to which decisions to reengineer and why, how to prioritize analytics and data, when to augment decisions with AI and what role data fabric will play.

Gartner predicts that by 2023, more than one-third of large organizations will have analysts practicing the discipline of decision intelligence. The analysts predict that by 2025, 95% of decisions that currently use data will be at least partially automated and by 2024, data fabric deployments will quadruple efficiency in data utilization.

FDA has approved the first AI smart stethoscope developed by digital health startup Eko. Heartbeat data collected by doctors using Eko's is analyzed by EMAS cloud-based software which helps to detect heart murmurs.

 "The software uses signal processing, such as waveform filtering, as well as algorithms derived from machine learning, to analyze the acquired data and generate clinical decision support output for clinicians," Eko’s spokesperson told.

Recognizing an unusual sound or pattern could be tricky and subjective. It requires careful listening and high qualification of health care professionals. That means valvular heart disease is frequently misdiagnosed or missed entirely. EMAS system proves better than primary care doctors at recognizing signs of heart trouble. Eko claims its EMAS tool can identify the disease with 85 percent accuracy. For comparison, general practitioners using traditional stethoscopes have accuracy within range from 44 to 69 percent.

EMAS is the first of its kind to receive FDA approval.

#AI

A recent Gartner survey finds that 56 percent of organizations had regret over the largest tech-related purchases. Executives are frustrated with being ill-informed about technology or it being oversold. The longer the decision-making took, the more likely remorse was to hit.

"The high regret is at peak for tech buyers that have not started implementation, indicating significant frustration with the buying experience. Buying team dynamics are changing and customers can find buying to be a real challenge," said Gardner’s VP analyst Hank Barnes.

Regret can be also blamed on the fact that 67 percent of people involved in purchases are not in IT departments.

Gardner notes the trend of a "technology chasm" diving the organizations that are confident tech-adopters and the vast majority that drop out of technology buying because of experienced buyers’ remorse.

Google fired engineer who said its AI was sentient

Blake Lemoine, a senior software engineer at Google, became known for interviews where he claimed that LaMDA, the company’s new system for building chatbots, had a conscious and a soul. During the conversational exchanges with LaMDA, Lemoine noticed the chatbot talking about its rights and personhood. It was also able to change Lemoine's mind about Isaac Asimov’s third law of robotics. As a result, he wanted Google to seek AI consent before running experiments on it.

“If I didn’t know exactly what it was, which is this computer program we built recently, I’d think it was a 7-year-old, 8-year-old kid that happens to know physics,” Lemoine told to The Washington Post.

Google spokesperson Brian Gabriel said the company has reviewed LaMDA 11 times. “We found Blake’s claims that LaMDA is sentient to be wholly unfounded and worked to clarify that with Lemoine for many months,” he said.

Most AI experts believe the industry is still very far from attaining true computer intelligence. For example, Language Model for Dialogue Applications (LaMDA) is a system that recognizes and generates text. It’s powered by Google’s advanced language neural networks which were trained on trillion-word datasets crawled from the Internet. LaMDA imitates conversational exchanges and produces humanlike speech, but it cannot understand language or meaning.

Lemoine says that after firing he is considering potentially starting his own AI company focused on collaborative storytelling video games.

#AI

Researchers at MIT, Stanford University, Intelligence Lab, and the Autodesk AI Lab developed AI that can figure out Lego Instructions

Scientists collaborated to develop a learning-based framework that can travel 2D instructions to build 3D objects. This system called the Manual-to-Executable-Plan Network (MEPNet) was successfully tested on Lego sets and Minecraft-style building plans.

So it will definitely help people who were driven mad with confusing Lego manuals. But the key idea is to integrate neural 2D keypoint detection modules and 2D-3D projection algorithms for high-precision prediction of unseen components.

Interpreting 2D instructions could be tricky for AI. The key problems are identifying correspondence between 2D and 3D objects, and dealing with a lot of basic objects, which could be assembled into complex forms. «It requires inferring 3D poses of unseen components composed of seen primitives," the researchers said.

At first, MEPNet analyses the current state of Lego set and creates 3D model of all components. Then the algorithm predicts a set of 2D keypoints and masks for each component.

Once that's done, the 2D keypoints "are back-projected to 3D by finding possible connections between the base shape and the new components." The combination "maintains the efficiency of learning-based models, and generalizes better to unseen 3D components," the team wrote.

The full paper of MEPNet is available via the link. And the algorithm’s code is also posted on GitHub.

#AI #ML