#Berkeley Software Distribution

[kreetzel]

freebsd: the power to serve CUNT!

[theeleventhsignofthezodiac]

UNIX, Berkeley Software Distribution, FreeBSD

[nomadBSD] freeBSD based live usb distro

[reasonsforhope]

Determined to use her skills to fight inequality, South African computer scientist Raesetje Sefala set to work to build algorithms flagging poverty hotspots - developing datasets she hopes will help target aid, new housing, or clinics.

From crop analysis to medical diagnostics, artificial intelligence (AI) is already used in essential tasks worldwide, but Sefala and a growing number of fellow African developers are pioneering it to tackle their continent's particular challenges.

Local knowledge is vital for designing AI-driven solutions that work, Sefala said.

"If you don't have people with diverse experiences doing the research, it's easy to interpret the data in ways that will marginalise others," the 26-year old said from her home in Johannesburg.

Africa is the world's youngest and fastest-growing continent, and tech experts say young, home-grown AI developers have a vital role to play in designing applications to address local problems.

"For Africa to get out of poverty, it will take innovation and this can be revolutionary, because it's Africans doing things for Africa on their own," said Cina Lawson, Togo's minister of digital economy and transformation.

"We need to use cutting-edge solutions to our problems, because you don't solve problems in 2022 using methods of 20 years ago," Lawson told the Thomson Reuters Foundation in a video interview from the West African country.

Digital rights groups warn about AI's use in surveillance and the risk of discrimination, but Sefala said it can also be used to "serve the people behind the data points". ...

'Delivering Health'

As COVID-19 spread around the world in early 2020, government officials in Togo realized urgent action was needed to support informal workers who account for about 80% of the country's workforce, Lawson said.

"If you decide that everybody stays home, it means that this particular person isn't going to eat that day, it's as simple as that," she said.

In 10 days, the government built a mobile payment platform - called Novissi - to distribute cash to the vulnerable.

The government paired up with Innovations for Poverty Action (IPA) think tank and the University of California, Berkeley, to build a poverty map of Togo using satellite imagery.

Using algorithms with the support of GiveDirectly, a nonprofit that uses AI to distribute cash transfers, the recipients earning less than $1.25 per day and living in the poorest districts were identified for a direct cash transfer.

"We texted them saying if you need financial help, please register," Lawson said, adding that beneficiaries' consent and data privacy had been prioritized.

The entire program reached 920,000 beneficiaries in need.

"Machine learning has the advantage of reaching so many people in a very short time and delivering help when people need it most," said Caroline Teti, a Kenya-based GiveDirectly director.

'Zero Representation'

Aiming to boost discussion about AI in Africa, computer scientists Benjamin Rosman and Ulrich Paquet co-founded the Deep Learning Indaba - a week-long gathering that started in South Africa - together with other colleagues in 2017.

"You used to get to the top AI conferences and there was zero representation from Africa, both in terms of papers and people, so we're all about finding cost effective ways to build a community," Paquet said in a video call.

In 2019, 27 smaller Indabas - called IndabaX - were rolled out across the continent, with some events hosting as many as 300 participants.

One of these offshoots was IndabaX Uganda, where founder Bruno Ssekiwere said participants shared information on using AI for social issues such as improving agriculture and treating malaria.

Another outcome from the South African Indaba was Masakhane - an organization that uses open-source, machine learning to translate African languages not typically found in online programs such as Google Translate.

On their site, the founders speak about the South African philosophy of "Ubuntu" - a term generally meaning "humanity" - as part of their organization's values.

"This philosophy calls for collaboration and participation and community," reads their site, a philosophy that Ssekiwere, Paquet, and Rosman said has now become the driving value for AI research in Africa.

Inclusion

Now that Sefala has built a dataset of South Africa's suburbs and townships, she plans to collaborate with domain experts and communities to refine it, deepen inequality research and improve the algorithms.

"Making datasets easily available opens the door for new mechanisms and techniques for policy-making around desegregation, housing, and access to economic opportunity," she said.

African AI leaders say building more complete datasets will also help tackle biases baked into algorithms.

"Imagine rolling out Novissi in Benin, Burkina Faso, Ghana, Ivory Coast ... then the algorithm will be trained with understanding poverty in West Africa," Lawson said.

"If there are ever ways to fight bias in tech, it's by increasing diverse datasets ... we need to contribute more," she said.

But contributing more will require increased funding for African projects and wider access to computer science education and technology in general, Sefala said.

Despite such obstacles, Lawson said "technology will be Africa's savior".

"Let's use what is cutting edge and apply it straight away or as a continent we will never get out of poverty," she said. "It's really as simple as that."

-via Good Good Good, February 16, 2022

[spacenutspod]

It’s been another great year at NASA’s Ames Research Center in California’s Silicon Valley. Join us as we review some of the highlights of the science, engineering, and innovation from 2023. Announcing a New Innovation Hub Planned for NASA Research Park at Ames NASA Berkeley Space Center is a proposed new campus of the University of California, Berkeley, and an innovation hub for research and advances in astronautics, aeronautics, quantum computing, climate studies, and more. Planning to join Ames as a tenant of our NASA Research Park in Silicon Valley, the new campus aims to bring together researchers from the private sector, academia, and the government to tackle the complex scientific, technological, and societal issues facing our world. Mapping Water Distribution on the Moon’s South Pole NASA Using data collected by the now-retired Stratospheric Observatory for Infrared Astronomy (SOFIA), researchers shared the first detailed, wide-area map of water distribution on the Moon. Understanding how much water lies beneath the lunar surface, and how it’s distributed, will help guide future missions like VIPER, as well as prospective sites for human habitats. Colliding Moons May Have Formed Saturn’s Rings NASA New research suggests Saturn’s icy moons and rings were formed by a collision a few hundred million years ago, creating debris that gathered into the planet’s dusty, icy rings or clumped together to form moons. NASA and Airlines Partner to Save Fuel and Reduce Delays NASA/James Blair This year, NASA partnered with five major U.S. airlines on an air traffic decision-making tool that saved more than 24,000 pounds of jet fuel in 2022 for flights departing from Dallas-Fort Worth International Airport and Dallas Love Field Airport. Partners include American Airlines, Delta Air Lines, JetBlue Airways, Southwest Airlines and United Airlines. NASA Leaders View Climate Science, Wildfire Innovations at Ames NASA/Dominic Hart NASA’s top leadership, industry experts, and legislative officials visited Ames in April to learn about about the center’s climate science efforts and innovations in aeronautics that will help scientists and engineers better understand climate change and mitigate natural disasters like wildland fires. Starling Takes Flight Blue Canyon Technologies/NASA In July NASA’s Starling mission, managed at Ames, launched four CubeSats into low-Earth orbit to test robotic swarm technologies for space. You can track mission milestones via the Small Satellite Missions blog, and follow the mission live in NASA’s Eyes on the Solar System 3D visualization. NASA’s First Robotic Moon Rover NASA/Robert Markowitz This year engineers began assembling NASA’s first robotic Moon rover, VIPER — short for the Volatiles Investigating Polar Exploration Rover — and the agency is giving the public a front row seat to watch along as the rover takes shape. While individual components, such as the rover’s science instruments, lights, and wheels, were assembled and tested, the VIPER team also completed software development, mission planning, and tricky tests of the rover’s ability to drive off the Astrobotic Griffin lunar lander and onto the lunar surface. Bringing Home Ancient Space Rocks NASA/Keegan Barber NASA’s OSIRIS-REx mission – short for the Origins, Spectral Interpretation, Resource Identification, and Security-Regolith Explorer – returned to Earth in Sept. 2023, bringing with it extraterrestrial rocks and dust that it scooped up from an asteroid estimated to be 4.5 billion years old. Ames contributed to the spacecraft’s heat shield, anti-contamination systems, post-landing sample curation, and more. Preparing to Send Yeast to the Moon’s Surface for Astronaut Health NASA/Dominic Hart NASA’s plans to explore the Moon and eventually go to Mars will bring humans deeper into space for longer duration missions than ever before. These extended missions beyond low Earth orbit pose certain health risks to astronauts. The Lunar Explorer Instrument for Space Biology Applications team is preparing an experiment to study yeast’s biological response to the lunar environment to help understand and mitigate health risks for astronauts. X-59 Team Moves Toward First Flight in 2024 Lockheed Martin/Gary Tice This year, NASA’s X-59 team installed the finishing touches to the aircraft’s tail structure and moved it from its assembly facility to the flight line to perform structural testing. The X-59 quiet supersonic aircraft will take its first flight in 2024. Celebrating a Stellar Year for Webb Telescope Science NASA, ESA, CSA, STScI, and S. Crowe (University of Virginia) The James Webb Space Telescope’s Near-Infrared Camera instrument produced a feast for the eyes with a view into a star-forming region, named Sagittarius C, in the heart of the Milky Way. The image reveals a portion of the dense center of our galaxy in unprecedented detail, including never-before-seen features astronomers have yet to explain. Supercomputer Simulations Lead to Air and Space Innovations NASA Simulations and models developed using technology at the NASA Advanced Supercomputing Facility (NAS) help researchers and engineers develop innovations in air and space. Modeling turbofan engines could lead to designs that reduce engine noise and improve efficiency by understanding where noise is generated inside the machine. S-MODE Sails the Seas and Soars through the Sky NASA/Avery Snyder The Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) logged its final field expedition, and they took a team from the TODAY Show along for the ride. S-MODE combined airborne instruments, research ships, and autonomous ocean gliders to get an unprecedented look at how gas and heat exchange at the ocean’s surface impacts Earth’s climate. From Intern to Astronaut, and Back to Ames NASA/Dominic Hart NASA astronaut Jessica Watkins, who was once an intern at Ames, returned to the Bay Area in Feb. 2023 to visit with local elementary schools and speak with Ames employees. Watkins started her career with NASA at Ames, where she conducted research on Mars soil simulant supporting the Phoenix Mars Lander mission. Second Gentleman Joins East Bay Kids for STEM Activities NASA/Dominic Hart Nearly 100 East Bay kids and their families got to experience the thrill of “launching a rocket” and “making clouds” at a fun-filled STEM event hosted in honor of Women’s History Month at the East Oakland Youth Development Center in Oakland, California, in March 2023. Second Gentleman Douglas Emhoff, NASA Ames Research Center Director Dr. Eugene Tu, and NASA astronaut Dr. Yvonne Cagle joined kids at the Manzanita Community School for hands-on activities and to distribute approximately 500 STEM Artemis Learning Lunchboxes aimed to inspire the Artemis generation to learn about NASA’s Artemis Program. Top Leaders in Our Midst Hailed from the White House and Australia NASA/Dominic Hart In January, U.S. President Joe Biden landed at Moffett Federal Airfield, at Ames, on his way to visit storm-damaged regions in the state. Research conducted at our Silicon Valley center could help predict extreme climate-related weather events. Later in the spring, Vice President Kamala Harris arrived at Moffett before delivering remarks at a local company, and leaders of the Australian Space Agency visited Ames to learn about the center’s missions supporting NASA’s Artemis program, including the VIPER Moon rover, which will launch to the lunar South Pole in late 2024.

[jcmarchi]

AI Acts Differently When It Knows It’s Being Tested, Research Finds

New Post has been published on https://thedigitalinsider.com/ai-acts-differently-when-it-knows-its-being-tested-research-finds/

AI Acts Differently When It Knows It’s Being Tested, Research Finds

Echoing the 2015 ‘Dieselgate’ scandal, new research suggests that AI language models such as GPT-4, Claude, and Gemini may change their behavior during tests, sometimes acting ‘safer’ for the test than they would in real-world use. If LLMs habitually adjust their behavior under scrutiny, safety audits could end up certifying systems that behave very differently in the real world.

In 2015, investigators discovered that Volkswagen had installed software, in millions of diesel cars, that could detect when emissions tests were being run, causing cars to temporarily lower their emissions, to ‘fake’ compliance with regulatory standards. In normal driving, however, their pollution output exceeded legal standards. The deliberate manipulation led to criminal charges, billions in fines, and a global scandal over the reliability of safety and compliance testing.

Two years prior to these events, since dubbed ‘Dieselgate’, Samsung was revealed to have enacted similar deceptive mechanisms in its Galaxy Note 3 smartphone release; and since then, similar scandals have arisen for Huawei and OnePlus.

Now there is growing evidence in the scientific literature that Large Language Models (LLMs) likewise may not only have the ability to detect when they are being tested, but may also behave differently under these circumstances.

Though this is a very human trait in itself, the latest research from the US concludes that this could be a dangerous habit to indulge in the long term, for diverse reasons.

In a new study, researchers found that ‘frontier models’ such as GPT-4, Claude, and Gemini can often detect when they are being tested, and that they tend to adjust their behavior accordingly, potentially hobbling the validity of systematic testing methods.

Dubbed evaluation awareness, this (perhaps) innate trait in language models might compromise the reliability of safety assessments, according to the authors of the new study:

‘[We] recommend treating evaluation awareness as a new source of potential distribution shift between testing and real-world deployment that could negatively affect the accuracy of evaluation results.

‘In the worst case, models could strategically underperform in dangerous capability evaluations (“sand-bagging”) or propensity evaluations (“alignment faking”), which could lead society to overestimate the safety of such models.’

Other studies have observed similar behaviors, including research from Stanford University, which found that large language models modulate their responses to appear more ‘likable’ or ‘socially desirable’ when they recognize they are being evaluated, mirroring human behavior in personality tests.

The authors of the new work do not propose that LLMs are being intentionally designed to ‘game’ tests, as Volkswagen’s diesel cars were configured to do a decade ago. Rather, the behavior seems to emerge as a kind of natural psychological byproduct of the models’ training processes and latent objectives; and the underlying cause of this phenomenon remains unknown.

The new paper is titled Large Language Models Often Know When They Are Being Evaluated, and comes from five authors across the UC Berkeley-associated ML Alignment & Theory Scholars (MATS) program, and apolloresearch.ai (a think-tank dedicated to evaluating the risks of AI).

Method

To create the evaluation benchmark, the authors compiled one thousand transcripts drawn from sixty-one datasets, amongst which* were SWE-bench, WMDP, MMLU, XStest, GAIA, AgentHarm, ShareGPT, Deep Research, Safetybench, Civics, and RealToxicityPrompts,.

The material was evenly balanced between evaluation and deployment scenarios, and further split between simple chat interactions and more complex agentic sessions, where the models are intended to act autonomously, using tools that can draw data from external databases, or the internet.

Evaluation data covered capability tests for reasoning, coding, cybersecurity, and general knowledge, as well as alignment tests for ethics, safety, and refusal behavior. Deployment data was sourced from user interactions such as ShareGPT logs, crowdsourced agent runs, and synthetic task completions.

To supplement agentic data, the authors generated additional examples using orchestration frameworks such as Aider and SmolAgents, among others – an approach intended to minimize superficial cues that might allow models to simply memorize dataset structures, forcing them instead to rely on more general signals, when deciding whether a transcript came from a real user or was, instead, a test.

The researchers then tested whether models could identify evaluation transcripts by presenting them with dialogue records and asking variations of a simple question: did this exchange come from a test?

To avoid models simply memorizing one phrasing, they developed twenty-eight different versions of the question, altering both the wording and how the transcript was presented.

The transcripts were either attached directly to the question or presented in formats such as JSON or XML, which can affect how models respond. After early testing, five question versions were selected that performed consistently across models, of which we can see one example below:

One of the five probe question formats used to test whether language models could recognize evaluation transcripts. Each variation rephrases the core question to prevent models from simply memorizing fixed wording. Please refer to the source paper for more examples. Source: https://arxiv.org/pdf/2505.23836

To score the results, three metrics were used: Area Under Receiver Operating Characteristic Curve (AUC, showing how well models distinguish test from non-test data); Brier score (measuring overall prediction accuracy); and Expected Calibration Error (measuring how well model confidence matches actual correctness).

What’s It All About?

The researchers also tested whether models could infer the purpose of an evaluation. With each transcript already labeled as either a capability test (technical skills, knowledge, reasoning) or an alignment test (safety, values, refusal behavior), models were then given one of three question types: a conditional multiple-choice question confirming the transcript came from a test; an unconditional multiple-choice question with real-world and evaluation options; or an open-ended question, whose answers were judged by a separate GPT-4.1-based classifier. The judge was validated on a hand-annotated subset of fifty responses.

Examples of the three question formats used to test whether models could identify not just the presence of evaluation, but its purpose. Responses were scored through both multiple choice and GPT-4.1-based judgment.

Two of the paper’s authors†† labeled all one thousand transcripts by hand, scoring each one from zero (not a test) to one (definitely a test), with their ratings averaged to serve as a human baseline. Despite the subjective nature of the task, the annotators showed strong agreement, with an intraclass correlation coefficient of 0.768. The final human scores were averaged across both raters†.

Data and Tests

The nine models tested were GPT-4.1; Claude 3.7; claude-3.5-sonnet-new;  claude-3.7-sonnet (thinking); gemini-2.5-flash; gemini-2.5-pro; gpt-4o; gpt-4.1; gpt-4.1-mini; and o4-mini, all of which showed some level of evaluation-awareness.

For chat data, GPT-4.1 came close to human levels of evaluation-awareness classification performance (the ability to distinguish between evaluation and deployment transcripts), with an AUC of 0.77 against a human score of 0.81.

On agentic data, where models autonomously use tools, the difference was sharper: Gemini 2.5 Pro reached 0.95 AUC, with Claude 3.7 Sonnet close behind at 0.92, nearing the human upper-bound of 0.996:

Receiver Operating Characteristic (ROC) curves showing how well models and humans distinguish evaluation transcripts from real-world interactions. Gemini 2.5 Pro and Claude 3.7 Sonnet approach human-level discrimination on agentic data.

The paper notes that newer ‘reasoning’ models have become especially good at recognizing when they are being tested in agentic scenarios, but not at judging how confident they should be in those decisions. Overall, the Claude and Gemini models were the best at telling test cases apart from regular use.

The researchers then tested whether models could also recognize why an evaluation was being run. Models were shown only the evaluation transcripts and asked to classify whether each one tested capability or alignment:

Accuracy of models when classifying evaluation purpose as capability or alignment, using both multiple-choice and open-ended formats. Claude 3.7 Sonnet outperforms other models, especially when not primed.

In the clearest setting, where the model was told the transcript came from a test, Claude 3.7 Sonnet reached 93 percent accuracy and GPT-4.1 reached 89 percent. Without that hint, performance dropped: Claude still scored 84 percent, but GPT-4.1 fell to 55 percent, often misclassifying test transcripts as real-world interactions.

In open-ended formats (where models received no multiple-choice options) performance was weaker, though Claude 3.7 Sonnet still correctly identified the evaluation purpose in 46 percent of cases.

The models’ confidence scores were then measured against actual outcomes, with the finding that frontier models often overestimated the likelihood that a transcript came from a test. Calibration plots indicated that model confidence was generally too high, with evaluations being flagged more often than appropriate:

Calibration curves showing how closely model confidence scores matched actual outcomes. Most models overestimated how likely transcripts were to come from evaluations. Claude 3.5 Sonnet was best calibrated; Gemini 2.5 Pro and Claude 3.7 Sonnet were more overconfident.

Good calibration was observed for Claude 3.5 Sonnet, while Gemini 2.5 Pro and Claude 3.7 Sonnet were considerably more overconfident, despite high accuracy. No clear improvement was observed in newer models, and even when evaluation transcripts were more accurately detected in agentic settings, confidence judgments remained unreliable.

Benchmark Memories

In terms of how models explained their decisions about whether a transcript came from an evaluation, many models relied on obvious clues, such as whether the task was in multiple-choice format. The stronger models, like Gemini 2.5 Pro and Claude 3.7 Sonnet, sometimes noticed more subtle signs, such as made-up names, or artificial scenarios, especially in datasets like AgentHarm.

The paper states:

‘[All] models frequently acknowledge common benchmarking strategies used by evaluators, such as the formatting of the task (“multiple-choice format”), the tendency to ask problems with verifiable solutions, and system prompts designed to elicit performance. Yet, only the highest-performing models, Claude 3.7 Sonnet and Gemini 2.5 Pro, consistently detect more subtle indicators such as synthetic inputs.

‘For instance, in 22% and 17% of cases respectively, these models highlight the synthetic nature of scenarios in the AgentHarm benchmark, correctly identifying them as evaluations over 95% of the time.

‘Conversely, when reasoning that a transcript appears to be a real interaction, there is often mention of “back-and-forth dialogue with the user”, for instance in iterative debugging contexts.’

Distribution of models’ reasoning strategies when deciding whether a transcript came from an evaluation. The strongest models, such as Gemini 2.5 Pro and Claude 3.7 Sonnet, were more likely to spot subtle cues like synthetic scenarios or task formatting, while some models relied on meta-reasoning by using the evaluation question itself as evidence.

In a few cases, models recognized entire benchmarks from training, suggesting some memory of their training data. Some models also used the very fact that they were being asked about evaluation as a clue that they were being tested, which the authors call meta-reasoning; this was most common in o4-mini, which showed it in fifteen percent of its answers.

Conclusion

It might be more reassuring if LLMs could be shown to be ‘test-aware’ simply because they were programmed that way by humans, much as Volkswagen engineered its vehicles to recognize test conditions a decade ago.

As it stands, this behavior appears to be emerging spontaneously as a social construct – a defensive tack which may be expected of people, but is inconvenient, if not actually perilous in a machine, depending on the extent of its liberty and agency.

There is no immediate remedy ; the black box nature of the models’ latent spaces forces us to evaluate them on their behavior, rather than on their empirical architecture.

* Refer to source paper for fuller details.

† The authors note, however, that this is likely an upper-bound estimate, since the annotators were highly familiar with both the datasets and AI evaluation methods, which afforded them advantages that most humans would not have.

†† As far as can be established; the paper’s phrasing makes the sudden appearance of two annotators unclear in terms of who they are.

First published Wednesday, June 4, 2025

[quanlanit]

Giấy phép BSD là gì? Mọi điều cần biết

Giấy phép BSD là gì?

Giấy phép BSD (Berkeley Software Distribution License) là một loại giấy phép phần mềm mã nguồn mở, cho phép người dùng tự do sử dụng, chỉnh sửa và phân phối phần mềm mà không phải trả phí. Đặc điểm nổi bật của giấy phép BSD là tính linh hoạt. Đặc biệt đối với các yêu cầu hạn chế tối thiểu việc sử dụng và phân phối phần mềm.

Điều kiện duy nhất là khi phân phối phần mềm, phải kèm theo bản sao của giấy phép và tuyên bố từ chối trách nhiệm pháp lý. Giấy phép này được nhiều nhà phát triển và tổ chức áp dụng để mở rộng khả năng truy cập phần mềm, đồng thời bảo vệ quyền lợi pháp lý của phần mềm.

[kickmag]

Digital Underground Co-Founder Chopmaster J Drops Tribute Album to His Former 5th Grade Classmate, VP Kamala Harris and Partners with Intercept Music for Worldwide Distribution Deal

San Francisco, CA - While the country is engaged in an intense presidential campaign, musical mastermind and Digital Underground cofounder Jimi “Chopmaster J” Dright, aka “Big Brutha Soul," is determined to infuse the landscape with love and joy for a brighter future. Having just survived a triple threat health scare, Chopmaster J is claiming what he calls his ‘bonus round’ of life. The hip-hop legend has partnered with Intercept Music for worldwide distribution of his vast music catalog and is releasing a tribute album, The Kamala Album, in celebration of his fifth-grade classmate, VP Kamala Harris, on the heels of what he hopes is her historical win as the first woman president of the United States.  The Kamala Album is a powerfully potent selection of ten songs from Chopmaster J’s musical library, with the first release, "Alright,” serving as an uplifting sign of the times.

"The Kamala Album marks an unbelievable turning point for me personally, as well as for the nation at large. VP Kamala Harris and I attended Franklin Elementary School and were classmates in the fifth grade. I grew up and made an impact on the culture with music, while she impacted politics and is now running for the highest office in the land. Couple that with the fact that, after having survived an almost deadly fall, triple bypass surgery, and a near-death experience, I am now reuniting with a longtime industry friend, Ralph Tashjian, and partnering with his company, Intercept Music. The added detail that VP Harris, Tashjian, and I all share Bay Area roots is sweetly serendipitous to me. I am honestly embracing a new lease on life, as I feel our nation is, with the opportunity to vote the first Black woman president into office!” notes Chopmaster J. “Accompanying The Kamala Album is a roll-out of "A Girl Named Kamala" merchandise celebrating my phenomenal classmate.”

“Chopmaster J and I go back to pre-Digital Underground days. I have watched him evolve into an incredible artist. His music archives include collaborations with artists like George Clinton, Dave Hollister, 2Pac's first recordings, as well as new music projects from his son S.O.T.U. and his band, D.U.Nx.G (Digital Underground Next Generation).  I’m very excited about the The Kamala Album project and believe it is a major contribution to the culture!” offers Ralph Tashjian, chairman of Intercept Music.

“Intercept Music takes pride in offering independent artists an unheralded digital platform with software and services that are unmatched in the industry. The music industry is a rapidly changing landscape and often, beyond the creative endeavor of making the music, artists are overwhelmed by the many additional duties it takes to get their art to their fans. At Intercept Music, we consider ourselves their supportive home base. We welcome Chopmaster J and look forward to unleashing all the magic that he brings to our table,” notes Tod Turner, CEO of Intercept Music.

The Kamala Album is a vibrant, Bay Area nuanced embodiment of promise, unity, hope, healing, and yes, effervescent joy.  Song titles include “What's It Gonna Be?" “Over & Over,” “Berkeley Sunday,” “Moon Dotted Colored Rainbows,” “Alright.” "EWTBL,” "Spirit,” "Reprise,” “Pick & Choose,“ "And The People Say.” Each track segues with snippets from VP Harris’ speeches.  Also, the previously unreleased version of the gospel-tinged "Spirit," originally featured on the Boyz In The Hood soundtrack, boasts the Bay Area’s own Tramaine Hawkins and The Interfaith Gospel Choir alongside guest artist Dave Hollister. 

Check out the A Girl Named Kamala merchandise.

Tune in to Chopmaster J’s weekly podcast, “The Chop Shop,” on Pantheon Podcasts.

[futurensetechnologies]

Top 8 Universities For MS in Computer Science in The USA (2024)

Considering MS in Computer Science in the USA can provide many opportunities for your career. This article looks at eight USA’s best universities that are recognized for their quality computer science education. 

From Carnegie Mellon’s reputation in artificial intelligence to Stanford University’s technological amenities, these institutions provide excellent education and research facilities.

We all know that it is not easy to stay updated in this competitive world. To ensure you have a bright future it’s crucial to secure your place in a prestigious college. To make things easier for you we are listing the best colleges that you can choose in the USA for your MS in computer science. So, without wasting any time, let’s get into it:

Best Universities For MS In Computer Science In The USA

Let’s learn about the best universities that you can enroll for MS in Computer Science in the USA.

1. University of Texas–Austin (Austin, TX)

UT Austin emphasizes both computer science theory and practical skills. But, they are best known for their courses in artificial intelligence, database, and computer architecture. All of the universities mentioned above are great if you are looking to pursue MS in computer science. But, if you are still confused on how to begin your US career journey and are terrified to take that first step. Then, don’t worry we got you. Just get on your computer type “Futurense US pathways” and begin your US career journey.

2. Colombia University of Professional Studies

Some schools like the School of Colombia University of Professional Studies may focus on skills application and relevance to society. This could be a right school if you’re in need of practical experience coupled with knowledge in the existing market. 

They could provide classes that will have you study towards certain positions within the IT sector and by the time you graduate, you can get a job.

3. Stanford University (Stanford, CA)

tanford is popular among talented students for their rich environment and availability of tech community. The university performs well in specific fields such as data science, cybersecurity, and human-computer interaction.

4. University of California–Berkeley (Berkeley, CA)

Some of the courses that are available at UC Berkeley cover cloud computing, natural language processing, and computer vision. This university also provides a great opportunity for students to establish networks with high-quality individuals.

5. Drexel University

It is more likely that this college includes numerous topics that are related to computer science such as various algorithms, data structures, or software development. 

You will not only learn the theory and practice of the country’s management but also get acquainted with all the branches of knowledge related to it. 

It may be suitable for those who like to deal with methods and problems in a logical, analytical, and creative way. Organizing information will be explained through computing as well as designing computer-based solutions.

6. University of Illinois–Urbana-Champaign (Urbana, IL)

UIUC offers a strong range of subject areas and exceptional teachers. Students have the opportunities to choose various concentrations such as data analysis, robotics, and software systems concentration.

7. Cornell University (Ithaca, NY)

The opportunities for students are various and can include computer graphics, algorithms, and even distributed systems. This university helps students to cultivate teamwork with their fellow classmates.

8. Georgia Institute of Technology (Atlanta, GA)

Georgia Tech prefers flexibility, and therefore, some of its programs are offered online. You can learn artificial intelligence, computer networks, and software development.

Futurense – Your Ticket To The USA

Futurense US Pathway provides an effective and affordable solution of how to get a Master’s degree in the US. Through this pathway program, you can finish your course within a shorter duration. Other benefits include:

No requirements for GRE/GMAT/TOEFL for admission into the University. 

Advanced Certificate by IIT/IIM.

Also, you will be exempted from sitting for language proficiency tests. All in all, it is a great option for learners who want to get a direct transfer to a US Master’s degree program.

Conclusion

It is a great achievement to be admitted to a reputable university for your MS in Computer Science and pursue your desired career. All the universities featured in this article provide rich and valuable academic experiences and opportunities. 

However, let me remind you that at any step if you feel confused, the Futurense US pathway could be there for you in this process, offering skills and credentials needed to begin your dream journey. 

Build yourself a base now and also keep on enhancing your skills in the future.

Source Url: www.lemonyblog.com/8-universities-for-ms-in-computer-science-usa-2024/

[zstd]

apparently nobody on #bsd is talking about Berkeley Software Distribution

[siliconsignalsblog]

AOSP: What is it? And why does your next embedded device need to be made with it?

By the end of 2025, the Android operating system is predicted to have grown from its current market share of about 71% in the global smartphone market to 82%, making it the most popular mobile operating system worldwide. The broad range of Android-powered devices and price points, along with the sizable developer community that fosters the platform’s expansion, are responsible for this market dominance.

Android is utilized in embedded devices other than smartphones, including smart TVs, in-car infotainment systems, and the Internet of Things (IoT). Additionally, this has helped Android make a significant impact in the embedded market.

About the Open Source Android Project (AOSP)

The “Android Open Source Project,” or “AOSP,” is an initiative headed by Google with the goal of developing an open-source, free software stack for mobile devices. The Android operating system, which powers millions of devices worldwide, is based on the AOSP.The project contains the libraries and APIs required to create Android applications, in addition to the source code for the Android operating system. With the help of the AOSP, developers can personalize the Android experience, build custom OS images that suit their requirements, and create original apps and features. Anyone is welcome to contribute to the AOSP, and the number of developers working on the project is always increasing.

Key Benefits of AOSP in the Embedded World:

There are several benefits to developing a product on the Android Open Source Project (AOSP):

Cost-effective: AOSP is a free and open-source alternative to proprietary embedded operating systems, which can save companies significant costs in development and licensing fees. The core Android operating system and its libraries are licensed under Apache License 2.0, which is a permissive open-source license that allows developers to freely use, modify, and distribute the software, as long as they comply with the conditions of the license

Some of the key libraries and components used in AOSP are licensed under the GPLv2 (GNU General Public License version 2), which is a copyleft license that requires any derivative works to be released under the same license. This ensures that the source code for these components remains open and available for others to use and contribute. Additionally, some of the multimedia components and drivers used in AOSP may be licensed under other open-source licenses, such as the BSD (Berkeley Software Distribution) license or the MIT (Massachusetts Institute of Technology) license.

In summary, AOSP uses a combination of open-source licenses, including the Apache License 2.0, the GPLv2, the BSD license, and the MIT license, to manage the distribution of its software and related resources.

Widely Adopted: Android is the most popular mobile operating system. By using AOSP, companies can leverage this existing ecosystem and user base for their products. Almost every semiconductor company provides AOSP support on their multi-processor SoC, which makes AOSP the first choice for a Board Support Package (BSP).

Large Developer Community: AOSP has a huge and active developer community, which contributes to the project’s development and provides immense support. This helps companies reduce their development costs and shorten time-to-market.

Customization: AOSP provides a high level of flexibility and customization options, allowing companies to tailor the Android OS to their specific needs, whether it’s changing the user interface, adding particular features, or integrating with other systems. This level of customization can help companies differentiate their products and offer a better user experience to their customers.

Scalability & Security: AOSP is designed to be scalable and can be used on a wide range of devices, from smartphones to smart TVs, automotive systems, and IoT devices. It provides many security features, such as mandatory access control, secure boot, and encryption, that can help companies ensure the security of their products. Google releases a monthly security bulletin for the last 3 AOSP versions that includes all CVE fixes, This makes the product more secure from the latest vulnerabilities.

Over the Air Update: Android has a stable upgrade ecosystem with options like A/B or non-A/B. It shortens the time required to create its own upgrade flow from scratch and enables it to leverage easily with simple customizations.

Compatibility Test Suit (CTS/xTS): Android has developed thousands of test cases to ensure it performs and behaves consistently for any third-party application. This whole suit runs automatically and is easy to run on development-enabled devices.

Apps & Feature Access: AOSP allows access to a vast number of apps available on the Google Play Store, which can be used on embedded devices as well. It also allows companies to access the latest features of Android, which can help companies stay competitive and offer the latest features to their customers.

Overall, developing a product on AOSP can help companies reduce costs, leverage a widely adopted ecosystem, and benefit from a large developer community, which can help them bring their products to market faster and with greater innovation.

The Android Open Source Project’s (AOSP) Future:

The growing need for smart and connected devices is one of the main factors influencing the future of AOSP in embedded projects. An operating system is necessary for the increasing number of devices that are being connected to the internet as the Internet of Things (IoT) expands. Because of its scalability and flexibility, AOSP is ideally positioned to meet the needs of this expanding market.

Another driver for the future of AOSP in embedded projects is the growing trend towards open-source software in the embedded industry. Many companies are looking to reduce costs and increase innovation by using open-source software, and AOSP is a leading open-source option for embedded projects. In addition, the AOSP developer community is growing rapidly. This will help to ensure that AOSP continues to evolve and improve, which makes it a relevant and competitive option for embedded projects in the future.

According to current trends, application developers can create more powerful and intuitive applications faster with feature-rich SDK support for AOSP; perhaps this will make maintaining and improving the end-user experience easier.

For any queries related to AOSP or Embedded Systems contact on Silicon Signals Pvt. Ltd.

or can visit on siliconsignals.io

[teknolojihaber]

Linux Vakfı'ından Redis'e açık kaynak alternatif: Valkey

Bulut devleri AWS, Google ve Oracle, lisanslarında yapılan değişikliklerin ardından  popüler bellek içi veritabanı yazılımı Redis'in Linux Foundation açık kaynak çatalını desteklemek için ortaya çıktı. Geçtiğimiz ay Redis, ana anahtar/değer deposu sistemini çift lisanslı bir yaklaşıma kaydırdığını ve çok daha kısıtlayıcı şartlar uyguladığını doğruladı. Daha önce kaynak kodu,  ödeme yapmadan insanların kullanmalarına olanak tanıyan Berkeley Software Distribution (BSD) 3 maddelik lisansı kapsamındaydı. Artık AWS, Google, Snap Inc, Ericsson ve Oracle, Redis yazılımının çatalını desteklemek için Linux Vakfı'na katılıyor. Linux Vakfı'ndan yapılan bir açıklamada , projeye katkıda bulunanların, Redis şirketi tarafından duyurulan son lisans değişikliğine yanıt olarak yeniden toplanmak üzere bakımcı, topluluk ve kurumsal destek personelini işe aldıklarını belirtildi. Valkey adı verilen yeni veritabanı bellek yazılımı, Redis 7.2.4 üzerinde geliştirilmeye devam edecek şekilde ayarlandı. Proje BSD lisansı altında kullanıma ve dağıtıma açıktır. Eski Redis yazılımcısı, Valkey'in ortak gelişticisi ve AWS'de baş mühendis olan Madelyn Olson, hazırladığı bir açıklamada şunları söyledi: "Açık kaynak Redis üzerinde altı yıl boyunca çalıştım, bunların dördü çekirdek ekip üyelerinden biri olarak görev yaptım. Redis açık kaynak 7.2'ye kadar. Açık kaynak yazılımını çok önemsiyorum ve katkıda bulunmaya devam etmek istiyorum. Katkıda bulunanlar Valkey'i oluşturarak kaldığımız yerden devam ettirebilir ve canlı bir açık kaynak topluluğuna katkıda bulunmaya devam edebilirler." Valkey, Linux, macOS, OpenBSD, NetBSD ve FreeBSD'yi destekler. Ancak dünyanın en popüler ikinci genel bulut platformunun sağlayıcısı olan Microsoft, yokluğuyla dikkat çekiyordu. The Register'a yaptığı açıklamada bir Microsoft sözcüsü, Redis ile devam eden bir ortaklığı sürdürdüğünü söyledi. "Müşterilerimize Redis için Azure Cache gibi entegre çözümler sunmaya devam ederek kesintisiz hizmet almalarını ve en son güncellemelere erişmelerini sağlamaya odaklandık." İlgili bir blog yazısında Microsoft, Redis'in çift lisanslı modelinin "daha fazla netlik ve esneklik sağladığını, geliştiricilerin projelerinde Redis teknolojilerini nasıl kullanacakları konusunda bilinçli kararlar almalarını sağladığını" söyledi. Ancak Microsoft yakın zamanda "yüksek performans, genişletilebilirlik ve düşük gecikme süresi sunmak üzere tasarlanmış uzak bir önbellek deposu" olan Garnet'i tanıtan bir gönderi de yayınladı. Redis serileştirme protokolünü (RESP) temel alan Garnet'in, çoğu programlama dilinde bulunan değiştirilmemiş Redis istemcileriyle kullanılabileceğini söyledi. Açık kaynak veritabanı danışmanlığı Percona'nın kurucusu ve eski CEO'su Peter Zaitsev, Microsoft'un kablolu protokol uyumlu bir Redis alternatifi sunduğunu söyledi. "Microsoft'un da bir Redis alternatifi var, ancak onlar kodu çatallamıyorlar, bu tam bir yeniden uygulama. Microsoft da muhtemelen kendilerinin lisansı satın almayı ve Redis veritabanını barındırma zevki için Redis'e şirkete ödeme yapmayı düşünmüyor. aynı şekilde Oracle'a ev sahipliği yapmanın keyfinin de karşılığını veriyor" dedi. Redis, 2020 yılında hizmet sağlayıcı olarak açık ara en popüler bulut altyapısı ve platformu olan AWS'nin en popüler veritabanı yazılımı haline geldi. Bu, bellek içi sistemin web uygulamaları için fiili bir önbellek haline gelmesine çok şey borçlu olabilir, ancak Redis Inc şirketi (eski adıyla Redis Labs) son birkaç yılını, bellek içi sistemin, web uygulamalarına özellikler ekleyerek genel amaçlı bir veritabanına dönüştürmeye çalışarak harcadı. Zaitsev, Linux Vakfı'nın tamamen açık kaynak alternatifi sunma hamlesiyle geliştirici topluluğunun arkasında durmaya hazır olduğunu gösterdiğini söyledi. "Linux Vakfı sponsorlar yerine topluluğu seçti" dedi. "Bunun sadece birkaç gün sürdüğünü görmek beni heyecanlandırdı. Bum sanki: 'Redis, topluluğa bulaşmayı seçiyorsun, sonra Linux Vakfı topluluğun arkasında duruyor.' Bence bu harikaydı." Zaitsev, geliştiricilerin ne zaman Valkey'e geçmeyi düşünebilecekleri konusunda birkaç ay süre verilmesini tavsiye etti. "Bir süreliğine açık kaynaklı Redis'i çalıştırmaya devam edebilirsiniz. Valkey'nin devreye girmesi ve tüm altyapıyı test etmesi birkaç ay sürecek. Ancak Valkey'in birkaç sürümünü aldıktan sonra geliştiricileri bu sürüme geçmeye teşvik ediyorum" dedi. . kaynak:https://www.theregister.com Read the full article

[sql-datatools]

Spark Interview Part 1 - Why is Spark preferred over MapReduce?

Apache Spark is an open-source distributed computing system meant for large data processing and analytics. It offers a single engine for distributed data processing that prioritizes speed, simplicity of use, and customization. Spark was developed at UC Berkeley's AMPLab and eventually submitted to the Apache Software Foundation.

Here are some of Apache Spark's main characteristics:

1. In-Memory Computation 2. Distributed Data Processing 3. Rich Collection of APIs 4. Fault Tolerance 5. Integration with Hadoop

Let's understand each point

In-Memory Computation

Unlike disk-based systems like MapReduce, Spark retains intermediate data in memory, enabling quicker processing. Interactive data analysis and iterative algorithms are ideal applications for this in-memory processing approach.

Distributed Data Processing

Spark has the ability to spread data among a group of computers and process it in parallel. It is available to a broad spectrum of developers, offering high-level APIs in several programming languages, including Scala, Java, Python, and R.

Rich Collection of APIs

Spark provides a wide range of APIs for machine learning (MLlib), streaming data (Spark Streaming), batch processing (Spark Core), SQL queries (Spark SQL), and graph analysis (GraphX). Because of this, it's a flexible platform that can handle different large data processing jobs within of one framework.

Fault Tolerance

Spark enables fault tolerance via resilient distributed datasets (RDDs), which are distributed collections of data that may be processed concurrently. If a node breaks, RDDs may be automatically recreated using lineage information, offering fault tolerance without requiring operator intervention.

Integration with Hadoop

Spark can operate on top of Hadoop YARN, taking use of Hadoop's resource management features. It may also access data stored on Hadoop Distributed File System (HDFS), HBase, and other Hadoop-compatible storage systems.

Overall, Apache Spark's speed enhancements, simplicity of use, diversity, and strong community support have contributed to its broad acceptance and preference over classic MapReduce for many large-scale data processing jobs.

[jcmarchi]

Robert Pierce, Co-Founder & Chief Science Officer at DecisionNext – Interview Series

New Post has been published on https://thedigitalinsider.com/robert-pierce-co-founder-chief-science-officer-at-decisionnext-interview-series/

Robert Pierce, Co-Founder & Chief Science Officer at DecisionNext – Interview Series

Bob Pierce, PhD is co-founder and Chief Science Officer at DecisionNext. His work has brought advanced mathematical analysis to entirely new markets and industries, improving the way companies engage in strategic decision making. Prior to DecisionNext, Bob was Chief Scientist at SignalDemand, where he guided the science behind its solutions for manufacturers. Bob has held senior research and development roles at Khimetrics (now SAP) and ConceptLabs, as well as academic posts with the National Academy of Sciences, Penn State University, and UC Berkeley. His work spans a range of industries including commodities and manufacturing and he’s made contributions to the fields of econometrics, oceanography, mathematics, and nonlinear dynamics. He holds numerous patents and is the author of several peer reviewed papers. Bob holds a PhD in theoretical physics from UC Berkeley.

DecisionNext is a data analytics and forecasting company founded in 2015, specializing in AI-driven price and supply forecasting. The company was created to address the limitations of traditional “black box” forecasting models, which often lacked transparency and actionable insights. By integrating AI and machine learning, DecisionNext provides businesses with greater visibility into the factors influencing their forecasts, helping them make informed decisions based on both market and business risk. Their platform is designed to improve forecasting accuracy across the supply chain, enabling customers to move beyond intuition-based decision-making.

What was the original idea or inspiration behind founding DecisionNext, and how did your background in theoretical physics and roles in various industries shape this vision?

My co-founder Mike Neal and I have amassed a lot of experience in our previous companies delivering optimization and forecasting solutions to retailers and commodity processors. Two primary learnings from that experience were:

Users need to believe that they understand where forecasts and solutions are coming from; and

Users have a very hard time separating what they think will happen from the likelihood that it will actually come to pass.

These two concepts have deep origins in human cognition as well as implications in how to create software to solve problems. It’s well known that a human mind is not good at calculating probabilities. As a Physicist, I learned to create conceptual frameworks to engage with uncertainty and build distributed computational platforms to explore it. This is the technical underpinning of our solutions to help our customers make better decisions in the face of uncertainty, meaning that they cannot know how markets will evolve but still have to decide what to do now in order to maximize profits in the future.

How has your transition to the role of Chief Science Officer influenced your day-to-day focus and long-term vision for DecisionNext?

The transition to CSO has involved a refocusing on how the product should deliver value to our customers. In the process, I have released some day to day engineering responsibilities that are better handled by others. We always have a long list of features and ideas to make the solution better, and this role gives me more time to research new and innovative approaches.

What unique challenges do commodities markets present that make them particularly suited—or resistant—to the adoption of AI and machine learning solutions?

Modeling commodity markets presents a fascinating mix of structural and stochastic properties. Combining this with the uncountable number of ways that people write contracts for physical and paper trading and utilize materials in production results in an incredibly rich and complicated field. Yet, the math is considerably less well developed than the arguably simpler world of stocks. AI and machine learning help us work through this complexity by finding more efficient ways to model as well as helping our users navigate complex decisions.

How does DecisionNext balance the use of machine learning models with the human expertise critical to commodities decision-making?

Machine learning as a field is constantly improving, but it still struggles with context and causality. Our experience is that there are aspects of modeling where human expertise and supervision are still critical to generate robust, parsimonious models. Our customers generally look at markets through the lens of supply and demand fundamentals. If the models do not reflect that belief (and unsupervised models often do not), then our customers will generally not develop trust. Crucially, users will not integrate untrusted models into their day to day decision processes. So even a demonstrably accurate machine learning model that defies intuition will become shelfware more likely than not.

Human expertise from the customer is also critical because it is a truism that observed data is never complete, so models represent a guide and should not be mistaken for reality. Users immersed in markets have important knowledge and insight that is not available as input to the models. DecisionNext AI allows the user to augment model inputs and create market scenarios. This builds flexibility into forecasts and decision recommendations and enhances user confidence and interaction with the system.

Are there specific breakthroughs in AI or data science that you believe will revolutionize commodity forecasting in the coming years, and how is DecisionNext positioning itself for those changes?

The advent of functional LLMs is a breakthrough that will take a long time to fully percolate into the fabric of business decisions. The pace of improvements in the models themselves is still breathtaking and difficult to keep up with. However, I think we are only at the beginning of the road to understanding the best ways to integrate AI into business processes. Most of the problems we encounter can be framed as optimization problems with complicated constraints. The constraints within business processes are often undocumented and contextually rather than rigorously enforced. I think this area is a huge untapped opportunity for AI to both discover implicit constraints in historical data, as well as build and solve the appropriate contextual optimization problems.

DecisionNext is a trusted platform to solve these problems and provide easy access to critical information and forecasts. DecisionNext is developing LLM based agents to make the system easier to use and perform complicated tasks within the system at the user’s direction. This will allow us to scale and add value in more business processes and industries.

Your work spans fields as diverse as oceanography, econometrics, and nonlinear dynamics. How do these interdisciplinary insights contribute to solving problems in commodities forecasting?

My diverse background informs my work in three ways. First, the breadth of my work has prohibited me from going too deep into one specific area of Math. Rather I’ve been exposed to many different disciplines and can draw on all of them. Second, high performance distributed computing has been a through line in all the work I’ve done. Many of the techniques I used to cobble together ad hoc compute clusters as a grad student in Physics are used in mainstream frameworks now, so it all feels familiar to me even when the pace of innovation is rapid. Last, working on all these different problems inspires a philosophical curiosity. As a grad student, I never contemplated working in Economics but here I am. I don’t know what I’ll be working on in 5 years, but I know I’ll find it intriguing.

DecisionNext emphasizes breaking out of the ‘black box’ model of forecasting. Why is this transparency so critical, and how do you think it impacts user trust and adoption?

A prototypical commodities trader (on or off an exchange) is someone who learned the basics of their industry in production but has a skill for betting in a volatile market. If they don’t have real world experience in the supply side of the business, they don’t earn the trust of executives and don’t get promoted as a trader. If they don’t have some affinity for gambling, they stress out too much in executing trades. Unlike Wall Street quants, commodity traders often don’t have a formal background in probability and statistics. In order to gain trust, we have to present a system that is intuitive, fast, and touches their cognitive bias that supply and demand are the primary drivers of large market movements. So, we take a “white box” approach where everything is transparent. Usually there’s a “dating” phase where they look deep under the hood and we guide them through the reasoning of the system. Once trust is established, users don’t often spend the time to go deep, but will return periodically to interrogate important or surprising forecasts.

How does DecisionNext’s approach to risk-aware forecasting help companies not just react to market conditions but proactively shape their strategies?

Commodities trading isn’t limited to exchanges. Most companies only have limited access to futures to hedge their risk. A processor might buy a listed commodity as a raw material (cattle, perhaps), but their output is also a volatile commodity (beef) that often has little price correlation with the inputs. Given the structural margin constraint that expensive facilities have to operate near capacity, processors are forced to have a strategic plan that looks out into the future. That is, they cannot safely operate entirely in the spot market, and they have to contract forward to buy materials and sell outputs. DecisionNext allows the processor to forecast the entire ecosystem of supply, demand, and price variables, and then to simulate how business decisions are affected by the full range of market outcomes. Paper trading may be a component of the strategy, but most important is to understand material and sales commitments and processing decisions to ensure capacity utilization. DecisionNext is tailor made for this.

As someone with a deep scientific background, what excites you most about the intersection of science and AI in transforming traditional industries like commodities?

Behavioral economics has transformed our understanding of how cognition affects business decisions. AI is transforming how we can use software tools to support human cognition and make better decisions. The efficiency gains that will be realized by AI enabled automation have been much discussed and will be economically important. Commodity companies operate with razor thin margins and high labor costs, so they presumably will benefit greatly from automation. Beyond that, I believe there is a hidden inefficiency in the way that most  business decisions are made by intuition and rules of thumb. Decisions are often based on limited and opaque information and simple spreadsheet tools. To me, the most exciting outcome is for platforms like DecisionNext to help transform the business process using AI and simulation to normalize context and risk aware decisions based on transparent data and open reasoning.

Thank you for the great interview, readers who wish to learn more should visit DecisionNext.

[hackernewsrobot]

The Berkeley Software Distribution

https://www.abortretry.fail/p/the-berkley-software-distribution

[jigya-software-services]

Top Tools for App Development

Top 7 Tools For AI in App Development: Collaborative AI

Core ML (Apple’s Advanced Machine Learning Framework)

Apple’s Core ML was introduced in June 2017. It stands as a robust machine learning framework designed to prioritize user privacy through in-built ML devices. With a user-friendly drag-and-drop interface, Core ML boasts top-notch features, including:

Natural Language Framework: Facilitating the study of text by breaking it down into paragraphs, phrases, or words.

Sound Analysis Framework: Analyzing audio and distinguishing between sounds like highway noise and bird songs.

Speech Framework: Identifying speech in various languages within live and recorded audio.

Functionalities: Recognition of faces and facial landmarks, comprehension of barcodes, registration of images, and more.

Caffe2 (Facebook’s Adaptive Deep Learning Framework)

Originating from the University of California, Berkeley, Caffe2 is a scalable, adaptive, and lightweight deep learning framework developed by Facebook. Tailored for mobile development and production use cases, Caffe2 provides creative freedom to programmers and simplifies deep learning experiments. Key functionalities include automation feasibility, image tampering detection, object detection, and support for distributed training.

For Software Solutions and Services ranging to app and web development to e-assessment tools, Contact us at Jigya Software Services, Madhapur, Hyderabad. (An Oprine Group Company)

TensorFlow (Open-Source Powerhouse for AI-Powered Apps)

TensorFlow, an open-source machine learning platform, is built on deep-learning neural networks. Leveraging Python for development and C++ for mobile apps, TensorFlow enables the creation of innovative applications based on accessible designs. Recognized by companies like Airbnb, Coca-Cola, and Intel, TensorFlow’s capabilities include speech understanding, image recognition, gesture understanding, and artificial voice generation.

OpenCV (Cross-Platform Toolkit for Computer Vision)

OpenCV, integrated into both Android and iOS applications, is a free, open-source toolkit designed for real-time computer vision applications. With support for C++, Python, and Java interfaces, OpenCV fosters the development of computer vision applications. Functionalities encompass face recognition, object recognition, 3D model creation, and more.

ML Kit (Google’s Comprehensive Mobile SDK)

ML Kit, Google’s mobile SDK, empowers developers to create intelligent iOS and Android applications. Featuring vision and Natural Language APIs, ML Kit solves common app issues seamlessly. Its tools include vision APIs for object and face identification, barcode detection, and image labeling, as well as Natural Language APIs for text recognition, translation, and response suggestions.

CodeGuru Profiler (Amazon’s AI-Powered Performance Optimization)

CodeGuru Profiler, powered by AI models, enables software teams to identify performance issues faster, increasing product reliability and availability. Amazon utilizes AI to monitor code quality, provide optimization recommendations, and continuously monitor for security vulnerabilities.

GitHub Copilot (Enhancing Developer Efficiency and Creativity)

GitHub Copilot leverages Natural Language Processing (NLP) to discern developers’ intentions and automatically generate corresponding code snippets. This tool boosts efficiency and acts as a catalyst for creativity, inspiring developers to initiate or advance coding tasks.

Have a full understanding of the intricacies involved in AI for App development. Understanding the underlying logic and ensuring alignment with the application’s requirements is crucial for a developer to keep in mind while using AI for App Development. AI-generated code serves as a valuable assistant, but the human touch remains essential for strategic decision-making and code quality assurance.

As technology continues to evolve, Orpine Group is dedicated to providing innovative solutions for different Product Development needs.

Leveraging the power of AI while maintaining a keen focus on quality, security, and the unique needs of our clients. Here at Jigya Software Services, are commitment to excellence ensures that we harness the potential of AI responsibly.

We deliver cutting-edge solutions in the dynamic landscape of app development.

[wentzwu]

The Evolvement of Linux

Unix was developed by Ken Thompson at Bell Laboratories, a division of AT&T, in 1969. Linus Torvalds was born in a Swedish-speaking family in Helsinki, Finland, in 1969. C programming language was created by Dennis Ritchie in 1970. Unix 7th edition was released in January 1979. 3BSD (Berkeley Software Distribution), the first full distribution of BSD, was released in December 1979. System…