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Logitech mouse and keyboard users are getting a free AI upgrade – ZDNet

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Logitech

Generative AI assistants have become so widely used that shortcuts to access these tools have been physically integrated into many new laptop keyboards. Logitech has taken a different approach, placing a shortcut to access ChatGPT in its keyboards and mice without adding a new physical key. 

On Wednesday, Logitech unveiled its new Logi AI Prompt Builder, a software window that helps users access the chatbot’s assistance without interrupting their workflow. 

Also: Limitless’ $99 AI wearable promises to remember your meetings and everything else

Within the Logi + Option app, users can access the Low AI Prompt Builder for free and create a shortcut key on their mouse or keyboard that automatically opens the Logi AI Prompt Builder software window when ChatGPT assistance is needed. 

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Once the window opens, it will offer users suggested queries based on the text they selected to work with according to pre-defined recipes of common queries, such as Rephrase or Summarize, says Logitech. Users can also customize queries to best meet their end goal, with options to change tone, length, style, and more, as seen below.  

Logitech

In addition to helping users get easier access to ChatGPT by circumventing the requirement to copy and paste text into a browser manually, the Low AI Prompt Builder also makes prompt writing easier with its pre-determined “recipes” and customization dropdowns. 

Also: Amazon Music echoes Spotify with an AI playlist generator of its own

“New Logi AI Prompt Builder is a shortcut to AI fluency for anyone with a Logitech mouse or keyboard compatible with Logi Options+ software who wants easily to access AI’s limitless potential,” said Delphine Donné, general manager of personal workspace solutions at Logitech.

Users can access Logi AI Prompt Builder today for free, as long as they are using a Logitech keyboard or mouse supported by the English version of the Logi + Option app, which includes some of the company’s most popular lines, including the Logitech MX, Ergo, Signature, and Studio Series, according to the press release. 

Also: 5 ways to prepare for the impact of generative AI on the IT profession

In short, the free software converts the users’ keyboard or mouse into a tool with a shortcut to access ChatGPT without upgrading the hardware. However, if you want to upgrade your mouse, Logitech has also launched a new wireless mouse, the Logitech Signature AI Edition Mouse. 

The mouse has an AI prompt button that offers a shortcut to the Logi AI Prompt Builder. Logitech says the mouse will be available “this month” on the Logitech website in the US for $49.99 and in the UK. 

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Active Fire Data Key to Keeping Staff Safe and Energy Assets Running | Earthdata – Earthdata

Mark Carinello, product manager for Indji Watch, monitors hazards on the Indji Watch system from his company’s office in Perth, Australia. Credit: Indji Systems.

As large wildfires that prompt evacuations, damage homes, and impact local economies become more common, it’s hard not to feel a sense of relief when a wildfire ignites in a remote landscape far from the nearest community.

Yet not everyone can breathe easy when a fire is reported in an unpopulated area. Utility companies with transmission lines running through out-of-the-way places and renewable energy providers with wind turbines and large solar arrays in remote areas are just two examples of businesses whose assets may be at risk from natural hazards when people and communities are not.

“Having [wildfire] hotspot data available to any company in North America or anywhere in the world is hugely important,”‘ said Mark Carinello, product manager for Indji Systems. “Without it, we and our customers would have much less awareness of where fires are right now and how they might impact their work.”

The first word in the company’s name, “Indji,” is an Australian Aboriginal term for “close to” and it”s a good choice for the company, which offers a real-time hazard monitoring and alert service known as Indji Watch to utility and renewable energy companies in Australia, the United States, Europe, and other countries around the world.

“If you have staff up on a wind turbine 100 meters above the ground in the open countryside of west Texas, you don’t want to be out there when there’s lightning,” Carinello said. “Or, if you’re working in a canyon for a utility in California and there’s a wildfire 10 miles away, it might take you a while to get out of that area.”

Indji Watch’s customers also rely on the company’s product to ensure their infrastructure are in good condition.

“If a wildfire is approaching a major transmission line, that line could short out and essentially stop operating, so our customers care about the hazards that threaten their operations,” said Carniello. “Lightning can actually punch a hole in a wind turbine and if the damage is not addressed quickly, it can cause additional problems. So, our clients need to know where lightning has occurred or where a fire has gone through, because they need to inspect their equipment for damage.”

As product manager, Carinello’s job is to ensure that Indji Systems products provide customers with the most current information. Because wildfire is among the threats it looks out for, Carniello and his colleagues incorporate active fire data from NASA’s Fire Information for Resource Management System (FIRMS) into their hazard alert and monitoring products.  

Members of Australia’s New South Wales Rural Fire Service monitor a fire in a remote region of the state that is also home to utility company transition lines. Credit: Indji Systems.

“Fire can a be a tough natural hazard to detect and getting the best information is a continuous challenge. There are many sources of wildfire information, such as the traditional state or federal fire agencies, but they rely on someone calling 911 and reporting a fire. They’re not always reliable, especially if the fire is in a remote area,” he said. “Now, with these satellites flying overhead and telling you that there’s something hot here, which is most likely a fire, we have a fantastic source of information for people with assets in remote areas.”

FIRMS provides access to satellite imagery, active fire and hotspot data, and related products that allow users to identify the location, extent, and intensity of wildfire activity. Its near real-time (NRT) data are available within three hours of a satellite overpass and its ultra real-time (URT) data (for the United States only) are available within 60 seconds using direct broadcast. Together, these products make FIRMS a valuable commodity for first responders and resource managers keen to pinpoint the location of a potential wildfire or track the development of established fires, especially in remote or rugged areas.

“Our clients generally care about where the fires are now, so we grab the hotspot data from FIRMS as soon as they become available,” Carniello said. “If there’s a fire a long way from a client’s infrastructure, the company may not care. Usually, they only want to be alerted for wildfires within a certain distance, like within five miles. When a fire is detected or reported by a fire agency within that distance, they’ll receive a notification.”

FIRMS’ active fire data come from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) instruments, both of which can detect radiated energy from their respective orbits approximately 440 and 515 miles (705 and 830 kilometers) above Earth’s surface.

MODIS flies aboard NASA’s Terra and Aqua satellites, and its Fire and Thermal Anomalies product is the foundation of FIRMS active fire products. When MODIS detects a thermal anomaly, which could indicate a wildfire or any significant source of heat, a computer algorithm identifies the center of the one square-kilometer area in which the anomaly is detected. This location is plotted and available to fire crews and wildland managers within three hours of the observation, providing an approximate location of a potential wildfire or hotspot.

VIIRS flies aboard the joint NASA-NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) and the Joint Polar Satellite Systems’ NOAA-20 and -21 satellites. The VIIRS I-band (375 meter) Active Fire product is based on the MODIS Fire and Thermal Anomalies product, which means that the two products complement each other in regard to hotspot detection (i.e., both products have shown good agreement in hotspot detection tests). However, VIIRS provides better response for smaller fires and provides improved mapping of large fire perimeters. Further, the VIIRS 375 m product shows a better response in nighttime observations, when fire activity normally subsides.

In addition to the NRT MODIS and VIIRS data, FIRMS also offers:

  • An NRT Landsat Fire and Thermal Anomaly active fire product generated with data from the Operational Land Imager (OLI) instrument aboard Landsat 8 and the Operational Land Imager-2 (OLI-2) aboard Landsat 9. These data are available in approximately 30 minutes from satellite overpass and offer much higher 30-meter resolution compared to the products from MODIS and VIIRS. (Note: This product covers only the continental United States and most of Canada and Mexico at this time.)
  • URT active fire data from MODIS and VIIRS for the continental United States within 60 seconds of observation via direct broadcast.
  • A short-wave infrared false color composite imagery layer from the Harmonized Landsat and Sentinel-2 (HLS) project. This layer is created with data from the Landsat OLI and OLI-2 instruments and the Multi-Spectral Instrument (MSI) aboard the ESA (European Space Agency) Sentinel-2A and -2B satellites. This product aids active fire detection by providing imagery of active fire fronts at the relatively high spatial resolution of 30 meters along with improved delineation of burned areas.
  • Geostationary active fire data from the Advanced Baseline Imager (ABI) aboard NOAA’s GOES-16 and -18 satellites, the Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) aboard the ESA Meteosat-9 and -11 satellites, and the Advanced Himawari Imager (AHI) aboard the JAXA (Japan Aerospace Exploration Agency) Himawari-8 satellite. These sensors provide data at 10- to 15-minute intervals, so they can detect more fire events and capture their growth and change. However, the spatial resolution of geostationary satellite data is coarser than MODIS data and therefore less sensitive to small fires.
This screen capture from Indji Watch shows a wildfire in northern California. The red flame icon identifies the reported location of the fire. The gray area identifies the fire perimeter and the red areas indicate areas of fire activity. This active fire information is derived from FIRMS hotspot data. Credit: Indji Systems.

Taken together, this suite of NRT and URT data make FIRMS a valuable tool for detecting ignitions, tracking the spread of ongoing fires, and assessing their impact.

In Australia, the need utility companies had for reliable fire location information came to the fore during Black Saturday in 2009, when bushfires in the Australian state of Victoria caused the deaths of 173 people and burned more than 450,000 hectares (more than 1.1 million acres).

“In the early 2000s, we had a catastrophic outbreak of wildfires here in Australia and during that event the fire agencies lost comms to a lot of their crews on the ground; they had no idea where the fires were,” said Carniello. “Then they thought, ‘Oh, let’s look at the satellite data and let’s see what it’s showing us.’ That was a key learning moment for us.”

But it wasn’t the only one. As Indji Watch developed its alert systems and began incorporating active fire data from FIRMS and other sources, its staff had to become familiar with every aspect of using wildfire data from satellites.

“In the early days, we had to educate ourselves on these satellites, on how to get the data from the different receiving stations after each overpass, how the data were processed, and how to use the files,” Carniello said. “Over time, NASA has become better and better at providing these data in easier to consume formats. So, now we just grab the raw data, but the nice thing about FIRMS is that the raw data [are] harmonized in a way that makes it easier for us to use and there are less variables we have to consider when building our own systems.”

This screen capture of the Indji Watch user interface shows the location of a transmission line (blue line on map) along with the location of satellite-detected fires nearby. Credit: Indji Systems.

For Diane Davies, operations manager for NASA’s Land, Atmosphere Near real-time Capability for EO (LANCE), Indji System’s reliance on FIRMS data is indicative of the value that FIRMS provides to users around the world.

“We see spikes in visitors viewing the FIRMS map interface when there are large fire events, but we are seeing increasing numbers of users routinely pull the active fire data via FIRMS web services,” Davies said. “Indji is a great example of this type of user—they routinely pull data from FIRMS into a customized GIS [geographic information system] and add value to it by combining it with other geospatial data.”

FIRMS is part of NASA’s LANCE, which provides more than 100 NRT products from instruments aboard Earth observation satellites. LANCE, in turn, is part of NASA’s Earth Observing System Data and Information System (EOSDIS), which is responsible for NASA’s Earth science data collection. FIRMS was developed by the University of Maryland in 2007 with funds from NASA and the United Nations Food and Agriculture Organization (UNFAO). NASA began offering FIRMS NRT data in 2007, and the UNFAO began offering the data in 2010 through its Global Fire Information Management System (GFIMS).

Resources

Active fire data for the last 24 hours, 48 hours, or week can be downloaded in shapefile, KML, WMS, or text file formats; data older than seven days can be obtained using the FIRMS Archive Download Tool.

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Let’s Confront Mnangagwa Head-on, Chamisa Aide Rallies Nation – ZimEye – Zimbabwe News

Let’s Confront Mnangagwa Head-on, Chamisa Aide Rallies Nation

30 April 2024

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By A Correspondent

In a stirring call for action, former MDC Alliance and CCC Youth Assembly spokesperson, Stephen Sarkozy Chuma, has urged Zimbabweans to confront head-on the authoritarian rule of President Emmerson Mnangagwa.

Speaking out against the entrenched dictatorship, Chuma’s words resonate as a rallying cry for change in a nation grappling with political oppression and economic turmoil.

Chuma released a brief statement on Monday.

Chuma’s impassioned plea for decisive action echoes the sentiments of many disillusioned Zimbabweans who refuse to remain passive in the face of oppression.

He challenges his fellow citizens to envision a different path, one where complacency is replaced by courage and determination.

His words, delivered with conviction, serve as a wake-up call to a nation on the brink of despair.

“Imagine dai during the liberation struggle if people were just folding hands waiting for Robert Mugabe & Joshua Nkomo to liberate them?” Chuma exclaimed.

“Surely Ian Smith’s ‘not in a 1000 years’ would have been fulfilled. We can’t relegate our struggle to Nelson Chamisa alone!”

With each word, Chuma underscores the urgency of the situation, drawing parallels between the struggles of the past and the challenges of the present.

His message is clear: the fight for freedom and justice requires the active participation of all Zimbabweans, not just a select few.

“Why are you quiet Zimbabwe when you don’t have electricity?” Chuma implores.

“Potholes, ZiG, corruption, inflation, vote rigging, abductions etc will never end if you remain silent fellow citizens.

Mnangagwa can’t have his cake and eat it too! We just can’t allow this rot to continue!”

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New algorithm cuts through ‘noisy’ data to better predict tipping points – UBNow: News and views for UB faculty and staff – University at Buffalo

Whether you’re trying to predict a climate catastrophe or mental health crisis, mathematics tells us to look for fluctuations. 

Changes in data, from wildlife population to anxiety levels, can be an early warning signal that a system is reaching a critical threshold, known as a tipping point, in which those changes may accelerate or even become irreversible. 

But which data points matter most? And which are simply just noise?

A new algorithm developed by UB researchers can identify the most predictive data points that a tipping point is near. Detailed in Nature Communications, this theoretical framework uses the power of stochastic differential equations to observe the fluctuation of data points, or nodes, and then determine which should be used to calculate an early warning signal. 

Simulations confirmed this method was more accurate at predicting theoretical tipping points than randomly selecting nodes.

“Every node is somewhat noisy — in other words, it changes over time — but some may change earlier and more drastically than others when a tipping point is near. Selecting the right set of nodes may improve the quality of the early warning signal, as well as help us avoid wasting resources observing uninformative nodes,” says the study’s lead author, Naoki Masuda, professor and director of graduate studies in the Department of Mathematics, College of Arts and Sciences.

The study was co-authored by Neil MacLaren, a postdoctoral research associate in the Department of Mathematics, and Kazuyuki Aihara, executive director of the International Research Center for Neurointelligence at the University of Tokyo. 

The work was supported by the National Science Foundation and the Japan Science and Technology Agency.

The algorithm is unique in that it fully incorporates network science into the process. While early warning signals have been applied to ecology and psychology for the past two decades, little research has focused on how those signals are connected within a network, Masuda says. 

Consider depression. Recent research has considered it and other mental disorders as a network of symptoms influencing each other by creating feedback loops. A loss of appetite could mean the onset of five other symptoms in the near future, depending on how close those symptoms are on the network.

“As a network scientist, I felt network science could offer a unique or perhaps even improved approach to early warning signals,” Masuda says. 

By thoroughly considering systems as networks, researchers found that simply selecting the nodes with highest fluctuations was not the best strategy. That’s because some selected nodes may be too closely related to other selected nodes.

“Even if we combine two nodes with nice early warning signals, we don’t necessarily get a more accurate signal. Sometimes combining a node with a good signal and another node with a mid-quality signal actually gives us a better signal,” Masuda says. 

While team members validated the algorithm with numerical simulations, they say it can readily be applied to actual data because it does not require information about the network structure itself; it only requires two different states of the networked system to determine an optimal set of nodes. 

“The next steps will be to collaborate with domain experts such as ecologists, climate scientists and medical doctors to further develop and test the algorithm with their empirical data and get insights into their problems,” Masuda says.

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