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Generational shifts in the workforce are creating a loss of operational expertise. Veteran workers with years of institutional knowledge are retiring, replaced by younger employees fresh out of school, taught on technologies and concepts that donât match the reality of many organizationsâ workflows and systems. This dilemma is fueling the need for automated knowledge sharing and intelligence-rich applications that can close the skills gap.
Industrial organizations are accumulating massive volumes of data but deriving business value from only a small slice of it. Transient repositories like data lakes often become opaque and unstructured data swamps. Organizations are switching their focus from mass data accumulation to strategic industrial data management, homing in on data integration, mobility, and accessibilityâwith the goal of using AI-enabled technologies to unlock value hidden in these unoptimized and underutilized sets of industrial data. The rise of the digital executive (chief technology officer, chief data officer, and chief information officer) as a driver of industrial digital transformation has been a key influence on this trend.
According to recent Occupational Safety and Health Administration data, workers at Amazon fulfillment centers were seriously injured about twice as often as employees in other warehouses. To improve workplace safety, Amazon has been increasing its investment in robotic helpers to reduce injuries among its employees. With access granted for the first time ever, âSunday Morningâ correspondent David Pogue visited the companyâs secret technology facility near Seattle to observe some of the most advanced warehouse robots yet developed, and to experience how high-tech tools are being used to aid human workers.
âCBS Sunday Morningâ features stories on the arts, music, nature, entertainment, sports, history, science and Americana, and highlights unique human accomplishments and achievements. Check local listings for CBS Sunday Morning broadcast times.
An outstanding idea, because for one there has been a video/ TV show/ movie, etc⊠showing every conceivable action a human can do; and secondly the AI could watch all of these at super high speeds.
Predicting what someone is about to do next based on their body language comes naturally to humans but not so for computers. When we meet another person, they might greet us with a hello, handshake, or even a fist bump. We may not know which gesture will be used, but we can read the situation and respond appropriately.
In a new study, Columbia Engineering researchers unveil a computer vision technique for giving machines a more intuitive sense for what will happen next by leveraging higher-level associations between people, animals, and objects.
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Part of the problem mirrors the rise of automation in any other industry â performers told Input that theyâre nervous that game studios might try to replace them with sophisticated algorithms in order to save a few bucks. But the game modderâs decision also raises questions about the agency that performers have over their own voices, as well as the artistry involved in bringing characters to life.
âIf this is true, this is just heartbreaking,â video game voice actor Jay Britton tweeted about the mod. âYes, AI might be able to replace things but should it? We literally get to decide. Replacing actors with AI is not only a legal minefield but an utterly soulless choice.â
âWhy not remove all human creativity from games and use AIâŠâ he added.
Sanofi will apply Googleâs artificial intelligence (AI) and cloud computing capabilities toward developing new drugs, through a collaboration whose value was not disclosed.
The companies said they have agreed to create a virtual Innovation Lab to âradicallyâ transform how future medicines and health services are developed and delivered.
Sanofi has articulated three goals for the collaboration with Google: better understand patients and diseases, increase Sanofiâs operational efficiency, and improve the experience of Sanofi patients and customers.
The challenges of making AI work at the edgeâthat is, making it reliable enough to do its job and then justifying the additional complexity and expense of putting it in our devicesâare monumental. Existing AI can be inflexible, easily fooled, unreliable and biased. In the cloud, it can be trained on the fly to get betterâthink about how Alexa improves over time. When itâs in a device, it must come pre-trained, and be updated periodically. Yet the improvements in chip technology in recent years have made it possible for real breakthroughs in how we experience AI, and the commercial demand for this sort of functionality is high.
AI is moving from data centers to devices, making everything from phones to tractors faster and more private. These newfound smarts also come with pitfalls.
Satellite imagery is becoming ubiquitous. Research has demonstrated that artificial intelligence applied to satellite imagery holds promise for automated detection of war-related building destruction. While these results are promising, monitoring in real-world applications requires high precision, especially when destruction is sparse and detecting destroyed buildings is equivalent to looking for a needle in a haystack. We demonstrate that exploiting the persistent nature of building destruction can substantially improve the training of automated destruction monitoring. We also propose an additional machine-learning stage that leverages images of surrounding areas and multiple successive images of the same area, which further improves detection significantly. This will allow real-world applications, and we illustrate this in the context of the Syrian civil war.
Existing data on building destruction in conflict zones rely on eyewitness reports or manual detection, which makes it generally scarce, incomplete, and potentially biased. This lack of reliable data imposes severe limitations for media reporting, humanitarian relief efforts, human-rights monitoring, reconstruction initiatives, and academic studies of violent conflict. This article introduces an automated method of measuring destruction in high-resolution satellite images using deep-learning techniques combined with label augmentation and spatial and temporal smoothing, which exploit the underlying spatial and temporal structure of destruction. As a proof of concept, we apply this method to the Syrian civil war and reconstruct the evolution of damage in major cities across the country. Our approach allows generating destruction data with unprecedented scope, resolution, and frequencyâand makes use of the ever-higher frequency at which satellite imagery becomes available.
The âtechnology intelligence engineâ uses A.I. to sift through hundreds of millions of documents online, then uses all that information to spot trends.
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Tarraf was fed up with incorrect predictions. He wanted a more data-driven approach to forecasting that could help investors, governments, pundits, and anyone else to get a more accurate picture of the shape of tech-yet-to-come. Not only could this potentially help make money for his firm, but it could also, he suggested, illuminate some of the blind spots people have which may lead to bias.
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âIt would be difficult to introduce a single thing and it causes crime to go down,â one expert said.
âAre we seeing dramatic changes since we deployed the robot in January?â Lerner, the Westland spokesperson said. âNo. But I do believe it is a great tool to keep a community as large as this, to keep it safer, to keep it controlled.â
For its part, Knightscope maintains on its website that the robots âpredict and prevent crime,â without much evidence that they do so. Experts say this is a bold claim.
Continue reading “Security robots expand across U.S., with few tangible results” »
