There's a moment in the 1967 film The Graduate that has become renowned. At a party thrown by his parents to celebrate his graduation, Benjamin (Dustin Hoffman) is approached by Mr McGuire, an elderly bore who wants to say "just one word" to him: "plastics". "Exactly how do you mean?", asks the hapless Ben. "There's a great future in plastics," says McGuire. "Think about it." Listening last week to the spending plans of the techlords who run Microsoft, Alphabet, Amazon and Meta leads one to wonder if something analogous might have happened to them on their graduation nights. Except that in their cases, the magic word would have been "AI".

University of Toronto professor Geoffrey Hinton, often called the " Godfather of AI" for his pioneering research on neural networks, recently became the industry's unofficial watchdog. He quit working at Google this spring to more freely critique the field he helped pioneer. He saw the recent surge in generative AIs like ChatGPT and Bing Chat as signs of unchecked and potentially dangerous acceleration in development. Google, meanwhile, was seemingly giving up its previous restraint as it chased competitors with products like its Bard chatbot. At this week's Collision conference in Toronto, Hinton expanded his concerns.

In a promotional video actress Michelle Yeoh walks through a bustling urban landscape at night with a smart phone in her hand as she talks to us about a coming transformation. "Every day Qualcomm is transforming the way we work, live and communicate, pushing the limits of technologies like artificial intelligence," she says. It's a dramatic statement by Qualcomm, as they try to connect the company to artificial intelligence in the mind of the market. "Whether our technology is going into a smartphone or whether it's going into a factory or a robot or a drone flying around Mars, our AI is a horizontal that permeates all of those device categories and applications," said Don McGuire, Qualcomm's chief marketing officer. The technology Qualcomm brings to artificial intelligence is a digital platform, based on Snapdragon computer chips.

For the last five or so years, Qualcomm has bet big on bringing more artificial intelligence to smartphones, laptops, vehicles, smart infrastructure and other devices in the field--or what the company calls the "connected intelligent edge." It's Don McGuire's job to tell Qualcomm's technology and artificial intelligence story in a way that's not scary. Recently, that's been harder to do. Last fall's launch of ChatGPT--a generative AI chatbot that answers prompts with polished essays, poetry, computer code and other human-like content--has thrust artificial intelligence into the public spotlight, with decidedly mixed reactions. While there's been plenty of positive hype, many people view the launch of ChatGPT--and AI overall --with a good amount of hand-wringing.

This paper is concerned with forecast error, particularly in relation to loss reserving. This is generally regarded as consisting of three components, namely parameter, process and model errors. The first two of these components, and their estimation, are well understood, but less so model error. Model error itself is considered in two parts: one part that is capable of estimation from past data (internal model error), and another part that is not (external model error). Attention is focused here on internal model error. Estimation of this error component is approached by means of Bayesian model averaging, using the Bayesian interpretation of the LASSO. This is used to generate a set of admissible models, each with its prior probability and the likelihood of observed data. A posterior on the model set, conditional on the data, results, and an estimate of model error (contained in a loss reserve) is obtained as the variance of the loss reserve according to this posterior. The population of models entering materially into the support of the posterior may turn out to be thinner than desired, and bootstrapping of the LASSO is used to gain bulk. This provides the bonus of an estimate of parameter error also. It turns out that the estimates of parameter and model errors are entangled, and dissociation of them is at least difficult, and possibly not even meaningful. These matters are discussed. The majority of the discussion applies to forecasting generally, but numerical illustration of the concepts is given in relation to insurance data and the problem of insurance loss reserving.

This paper discusses the development of an audio-visual composition based on genetic algorithms strategies. The genetic algorithm's fitness function dynamically adjusts the optimisation targets linked to the mechanisms responsible for the generating of drone soundscapes. The fitness function continuously changes based on the results of an analysis of the visual elements of the artwork thus acting as disturbance factor. In doing so, the audio material never achieves full optimisation and constantly shapes itself. The paper offers both a technical and aesthetic analysis of the development of the composition.

In recent years, machine learning has been very successful in solving a wide range of problems. In particular, neural networks have reached human, and sometimes super-human, levels of ability in tasks such as language translation, object recognition, game playing, and even driving cars. Prevent out-of-control infrastructure and remove blockers to deployments. With this growth in capability has come a growth in complexity. Data scientists and machine learning engineers must perform feature engineering, design model architectures, and optimize hyperparameters. Since the purpose of the machine learning is to automate a task normally done by humans, naturally the next step is to automate the tasks of data scientists and engineers. This area of research is called automated machine learning, or AutoML. There have been many exciting developments in AutoML recently, and it's important to take a look at the current state of the art and learn about what's happening now and what's coming up in the future. InfoQ reached out to the following subject matter experts in the industry to discuss the current state and future trends in AutoML space. InfoQ: What is AutoML and why is it important? Francesca Lazzeri: AutoML is the process of automating the time consuming, iterative tasks of machine learning model development, including model selection and hyperparameter tuning.

In recent years, machine learning has been very successful in solving a wide range of problems. In particular, neural networks have reached human, and sometimes super-human, levels of ability in tasks such as language translation, object recognition, game playing, and even driving cars. Aerospike is the global leader in next-generation, real-time NoSQL data solutions for any scale. Aerospike's patented Hybrid Memory Architecture delivers an unbreakable competitive advantage by unlocking the full potential of modern hardware, delivering previously unimaginable value from vast amounts of data at the edge, to the core and in the cloud. With this growth in capability has come a growth in complexity. Data scientists and machine learning engineers must perform feature engineering, design model architectures, and optimize hyperparameters. Since the purpose of the machine learning is to automate a task normally done by humans, naturally the next step is to automate the tasks of data scientists and engineers. This area of research is called automated machine learning, or AutoML. There have been many exciting developments in AutoML recently, and it's important to take a look at the current state of the art and learn about what's happening now and what's coming up in the future. InfoQ reached out to the following subject matter experts in the industry to discuss the current state and future trends in AutoML space. InfoQ: What is AutoML and why is it important?

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. New York City's police department is reportedly considering the use of "pandemic drone" technology to determine if people are infected with coronavirus, even though a Connecticut town has already scrapped its plan due to privacy concerns. Westport residents were successful in stymying plans for a drone that could detect a person's temperature, along with their heart and respiratory rates, from as high as 190 feet in the air, The New York Post reported. The NYPD reached out to Westport police for contact information regarding Draganfly, the Canadian company that manufactures the drone.

Artificial intelligence for marketing is at the peak of inflated expectations, while customer data platforms (CDPs) and real-time marketing are near peak in Gartner's projections, which means expectations for these technologies are the highest they'll ever be. Nevertheless, along with blockchain for advertising, Gartner said these four technologies have the ability to transform how marketers do their jobs and deliver meaningful customer experiences. The Hype Cycle starts with what Gartner calls the Innovation Trigger, which is where technology emerges from labs and quickly rises to the Peak of Inflated Expectations. From there, the cycle drops nearly as fast into the Trough of Disillusionment, which is about where Gartner placed tech like multitouch attribution, native advertising and personalization engines. That's not to say this is bad tech, but rather as customers start to use the tools, there will inevitably be bugs and other challenges like incompatibility with existing platforms, said Mike McGuire, a vice president analyst in Gartner's marketing practice.