We already know Apple is secretly preparing its first ever foldable iPhone. But now, keen tech fans have just got a new hint at what it may look like. According to a leak from a veteran Apple analyst, Mark Gurman of Bloomberg, the'iPhone Fold' will see some major design changes. And Mr Gurman claims that anyone who tries the new foldable device will'never want to go back'. Although it has been rumoured for years, it now looks almost certain that Apple is planning to unveil the folding iPhone in September 2026.

In the age of technology, many people fear that the rise of artificial intelligence and machine learning will make human workers obsolete. When it comes to UX design, this has been a hot topic. Many people have surmised that the role of the UX designer will soon go the way of the dodo, assuming that soon we can simply enter a prompt craft by a Chat GPT into an AI imaging platform like Midjourney, and bam -- all of a sudden we have our user interface. This couldn't be further from the truth… unless you want your user interface to behave as if it was downloaded from dribbble, which would look very pretty, but would most likely cause more usability friction and user frustration than it would solve. In fact, the rise of technology makes UX designers more crucial than ever before.

AI for supporting designers needs to be rethought. It should aim to cooperate, not automate, by supporting and leveraging the creativity and problem-solving of designers. The challenge for such AI is how to infer designers' goals and then help them without being needlessly disruptive. We present AI-assisted design: a framework for creating such AI, built around generative user models which enable reasoning about designers' goals, reasoning, and capabilities.

A sustainable burn platform through inertial confinement fusion (ICF) has been an ongoing challenge for over 50 years. Mitigating engineering limitations and improving the current design involves an understanding of the complex coupling of physical processes. While sophisticated simulations codes are used to model ICF implosions, these tools contain necessary numerical approximation but miss physical processes that limit predictive capability. Identification of relationships between controllable design inputs to ICF experiments and measurable outcomes (e.g. yield, shape) from performed experiments can help guide the future design of experiments and development of simulation codes, to potentially improve the accuracy of the computational models used to simulate ICF experiments. We use sparse matrix decomposition methods to identify clusters of a few related design variables. Sparse principal component analysis (SPCA) identifies groupings that are related to the physical origin of the variables (laser, hohlraum, and capsule). A variable importance analysis finds that in addition to variables highly correlated with neutron yield such as picket power and laser energy, variables that represent a dramatic change of the ICF design such as number of pulse steps are also very important. The obtained sparse components are then used to train a random forest (RF) surrogate for predicting total yield. The RF performance on the training and testing data compares with the performance of the RF surrogate trained using all design variables considered. This work is intended to inform design changes in future ICF experiments by augmenting the expert intuition and simulations results.

I started this article with the hopes of confronting a few misconceptions about Deep Learning (DL), a field of Machine Learning that is simultaneously labelled a silver bullet and research hype. The truth lies somewhere in the middle, and I hope I can un-muddy the waters -- at least a little bit. Importantly, I hope to clarify some processes to attack DL problems and also discuss why it performs so well in some areas such as Natural Language Processing (NLP), image recognition, and machine-translation while failing at others. Media often portrays Deep Learning as a magical recipe to the end of the world or the solution to all life's problems. In reality, it is anything but. Moreover, while DL has its fair share of strange behaviour and unexplained results, it is ultimately meritocratically driven.

I started this article with the hopes of confronting a few misconceptions about Deep Learning (DL), a field of Machine Learning that is simultaneously labelled a silver bullet and research hype. The truth lies somewhere in the middle, and I hope I can un-muddy the waters -- at least a little bit. Importantly, I hope to clarify some processes to attack DL problems and also discuss why it performs so well in some areas such as Natural Language Processing (NLP), image recognition, and machine-translation while failing at others. Media often portrays Deep Learning as a magical recipe to the end of the world or the solution to all life's problems. In reality, it is anything but. Moreover, while DL has its fair share of strange behaviour and unexplained results, it is ultimately meritocratically driven.

Many tasks in which humans excel are extremely difficult for robots and computers to perform. Especially challenging are decision-making tasks that are non-deterministic and, to use human terms, are based on experience and intuition rather than on predetermined algorithmic response. A good example of a task that is difficult to formalize and encode using procedural programming is image recognition and classification. For instance, teaching a computer to recognize that the animal in a picture is a cat is difficult to accomplish using traditional programming. Artificial intelligence (AI) and, in particular, machine learning technologies, which date back to the 1950s, use a different approach.

I have written a little bit about artifical intelligence in recent months. Enough to improve my superficial knowledge of its application in marketing. What seems obvious is that much like most martech, AI will not endanger many jobs in the medium term. It merely creates more sophistication and more jobs, perhaps. AI isn't about to write a convincing poem or a convincing marketing email. Okay, deep learning has got recommendations in its sights (e.g.

The developments in Google's Cloud Computing segment, especially the Cloud Machine Learning service, have been so rapid that Google calls it one of its fastest growing product areas. Google has been ramping up their Cloud Platform quite aggressively in recent months. Just a few weeks ago, the Google Cloud Platform opened its newest zone in Tokyo, increasing the total number of regions they are present in to six - three in the US and one each in Belgium and Taiwan and Tokyo. Not long ago, the company announced its acquisition of Orbitera, a cloud commerce company. The developments in Google's Cloud Computing segment, especially the Cloud Machine Learning service, have been so rapid that Google calls it one of its fastest growing product areas.

The holodeck as depicted in the TV series "Star Trek" is a hologram-powered simulation environment. Its use is primarily recreational. But the show also proposes pragmatic applications of the holodeck's technology. "The Star Trek: Voyager" series features a holographic medical doctor, the Emergency Medical Hologram Mark I. In some episodes, the holodeck is pressed into service as a platform for battle training and forensic analysis. The concept of reality-mimicking environments constructed with solid holograms remains theoretical.