If you’re like most business owners, you probably think of your website as an online brochure. It’s a...

The post How to Make Your Website a Lead-Generating Machine appeared first on Ignition Media.

Fans congratulate the Transformers star, who's spoken about a previous pregnancy ending in miscarriage.

Coding for #AI and #machinelearning – online course/workshop We are exploring a new way to learning how to code for AI and Machine Learning by applying ideas of  deliberate practise Starting Oct 2018 – the workshop has limited places Please contact info@futuretext.com if you want to sign up for our online workshop (300 USD)   [...]

The Internet Archive, an online repository of web pages, was offline Thursday after its founder confirmed a major cyberattack that exposed the data of millions of users and left the site defaced.

Actress Megan Fox and musician Machine Gun Kelly are expecting a child together.

The first computer is thought to be the invention of a 19th century mathematician

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Solutions Catch Problems Early and Drive Cost Out of Designs

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Chief of Defence Staff Gen Anil Chauhan asserted that "we are at the cusp of a major change in warfare".

"Nothing is ever really lost," Megan Fox captioned the post

The new SAS Certified Specialist: Statistics for Machine Learning credential is designed to help you showcase your expertise and commitment to staying ahead in the industry.

Earn this SAS certification to validate your skills and training in machine learning was published on SAS Users.

Microsoft announced it has acquired Semantic Machines, a conversational AI startup providing chatbots and AI chat apps founded in 2014 having $20.9 million in funding from investors. The acquisition will help Microsoft catch up with Amazon Alexa, though the latter is more focused on enabling consumer applications of conversational AI.

Microsoft will use Semantic Machine’s acquisition to establish a conversational AI center of excellence in Berkeley to help it innovate in natural language interfaces.

Microsoft has been stepping up its products in conversational AI. It launched the digital assistant Cortana in 2015, as well as social chatbots like XiaoIce. The latest acquisition can help Microsoft beef up its ‘enterprise AI’ offerings.

As the use of NLP (natural language processing) increases in IoT products and services, more startups are getting traction from investors and established players. In June last year, Josh.ai, avoice-controlled home automation software has raised $8M.

Followed by it was SparkCognition that raised $32.5M Series B for its NLP-based threat intelligence platform.

It appears Microsoft’s acquisition of Semantic Machines was motivated by the latter’s strong AI team. The team includes technology entrepreneur Daniel Roth who sold his previous startups Voice Signal Technologies and Shaser BioScience for $300M and $100M respectively. Other team members include Stanford AI Professor Percy Liang, developer of Google Assistant Core AI technology and former Apple chief speech scientist Larry Gillick.

Artificial intelligence (AI) is everywhere. Machine learning (ML) and its associated inference abilities promise to revolutionize everything from driving your car to making your breakfast. Verification is never truly complete; it is over when you run...(read more)

Megan Fox's current beau Machine Gun Kelly is in “no relationship” with the actress’ ex-husband Brian Austin Green, as the couple expect their first baby together.According to TMZ, MGK and Green have had little to no contact over the past two years, with sources saying that the...

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Devices that let people with paralysis walk and talk are rapidly improving. Some see a future in which we alter memories and download skills – but major challenges remain

The pursuit of harnessing data for knowledge creation has been an enduring quest, with the advent of machine learning (ML) and artificial intelligence (AI) marking significant milestones in this journey. ML, a subset of AI, emerged as the practice of employing mathematical models to enable computers to learn and improve autonomously based on their experiences. In the pharmaceutical and biopharmaceutical sectors, a significant portion of manufacturing data remains untapped or insufficient for practical use. Recognizing the potential advantages of leveraging the available data for process design and optimization, manufacturers face the daunting challenge of data utilization. Diverse proprietary data formats and parallel data generation systems compound the complexity. The transition to Pharma 4.0 necessitates a paradigm shift in data capture, storage, and accessibility for manufacturing and process operations. This paper highlights the pivotal role of AI in converting process data into actionable knowledge to support critical functions throughout the whole product life cycle. Furthermore, it underscores the importance of maintaining compliance with data integrity guidelines, as mandated by regulatory bodies globally. Embracing AI-driven transformations is a crucial step toward shaping the future of the pharmaceutical industry, ensuring its competitiveness and resilience in an evolving landscape.

Massively parallel reporter assays (MPRAs) are powerful tools for quantifying the impacts of sequence variation on gene expression. Reading out molecular phenotypes with sequencing enables interrogating the impact of sequence variation beyond genome scale. Machine learning models integrate and codify information learned from MPRAs and enable generalization by predicting sequences outside the training data set. Models can provide a quantitative understanding of cis-regulatory codes controlling gene expression, enable variant stratification, and guide the design of synthetic regulatory elements for applications from synthetic biology to mRNA and gene therapy. This review focuses on cis-regulatory MPRAs, particularly those that interrogate cotranscriptional and post-transcriptional processes: alternative splicing, cleavage and polyadenylation, translation, and mRNA decay.

Kelley watches the American Sailor Moon pilot acid trip fever dream. Tam is going off to get 800 glamour shots. Chris gets carded while trying to buy a Mara.

The post RPG Cast – Episode 644: “Is That a Demon Machine Making a Pizza?” appeared first on RPGamer.

Josh learns that despite Bethesda's best efforts, Starfield is not Fallout 76. Robert learns the taste of Amiibo blood. Chris is just here to ask, "kink-why?" And Kelley is obsessed with Red XIII on a chocobo.

The post RPG Cast – Episode 692: “Vending Machine Isekai” appeared first on RPGamer.

"Imagine you buy a pinball machine, and years later, you enter your den to go play it, only to discover that all the paddles are missing, the pinball and bumpers are gone, and the monitor that proudly displayed your unassailable high score is removed". As reported by Polygon, that's an argument put forth by a new lawsuit against Ubisoft, filed by two Californian players of The Crew. They're suing the company in a proposed class action lawsuit over shutting down the racing game's servers, rendering it unplayable.

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Devices that let people with paralysis walk and talk are rapidly improving. Some see a future in which we alter memories and download skills – but major challenges remain

Gears just a few micrometres wide can be carved from silicon using a beam of electrons, enabling tiny robots or machines that could interact with human cells

Two pioneers of artificial intelligence have won the Nobel Prize in physics for discoveries and inventions that formed the building blocks of machine learning.

Finished chips coming in from the foundry are subject to a battery of tests. For those destined for critical systems in cars, those tests are particularly extensive and can add 5 to 10 percent to the cost of a chip. But do you really need to do every single test?

Engineers at NXP have developed a machine-learning algorithm that learns the patterns of test results and figures out the subset of tests that are really needed and those that they could safely do without. The NXP engineers described the process at the IEEE International Test Conference in San Diego last week.

NXP makes a wide variety of chips with complex circuitry and advanced chip-making technology, including inverters for EV motors, audio chips for consumer electronics, and key-fob transponders to secure your car. These chips are tested with different signals at different voltages and at different temperatures in a test process called continue-on-fail. In that process, chips are tested in groups and are all subjected to the complete battery, even if some parts fail some of the tests along the way.

Chips were subject to between 41 and 164 tests, and the algorithm was able to recommend removing 42 to 74 percent of those tests.

“We have to ensure stringent quality requirements in the field, so we have to do a lot of testing,” says Mehul Shroff, an NXP Fellow who led the research. But with much of the actual production and packaging of chips outsourced to other companies, testing is one of the few knobs most chip companies can turn to control costs. “What we were trying to do here is come up with a way to reduce test cost in a way that was statistically rigorous and gave us good results without compromising field quality.”

A Test Recommender System

Shroff says the problem has certain similarities to the machine learning-based recommender systems used in e-commerce. “We took the concept from the retail world, where a data analyst can look at receipts and see what items people are buying together,” he says. “Instead of a transaction receipt, we have a unique part identifier and instead of the items that a consumer would purchase, we have a list of failing tests.”

The NXP algorithm then discovered which tests fail together. Of course, what’s at stake for whether a purchaser of bread will want to buy butter is quite different from whether a test of an automotive part at a particular temperature means other tests don’t need to be done. “We need to have 100 percent or near 100 percent certainty,” Shroff says. “We operate in a different space with respect to statistical rigor compared to the retail world, but it’s borrowing the same concept.”

As rigorous as the results are, Shroff says that they shouldn’t be relied upon on their own. You have to “make sure it makes sense from engineering perspective and that you can understand it in technical terms,” he says. “Only then, remove the test.”

Shroff and his colleagues analyzed data obtained from testing seven microcontrollers and applications processors built using advanced chipmaking processes. Depending on which chip was involved, they were subject to between 41 and 164 tests, and the algorithm was able to recommend removing 42 to 74 percent of those tests. Extending the analysis to data from other types of chips led to an even wider range of opportunities to trim testing.

The algorithm is a pilot project for now, and the NXP team is looking to expand it to a broader set of parts, reduce the computational overhead, and make it easier to use.

Megan Fox has announced pregnancy with Machine Gun Kelly after experiencing a pregnancy loss in 2023

Megan Fox et son petit ami Machine Gun Kelly seront bientôt parents.

Megan Fox is pregnant. The 38-year-old actress — who has Noah, 12, Bodhi, ten, and Journey, eight, with her ex-husband Brian Austin Green — has confirmed via social media that she's expecting her first child with Machine Gun Kelly.

As many as 3.32 crore voters will cast their votes in 37,809 polling stations across State

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AutMedAI, a new medlinkmachine learning/medlink (ML) model, enhances early detection of medlinkAutism Spectrum Disorder/medlink (ASD) with minimal medical and background data.

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