Scientists discover how a vitamin A byproduct may weaken immune responses against cancer, potentially explaining why some immunotherapies fail in patients.

Researchers are working on manipulating the digestive systems of wax worms to create a scalable way of disposing of plastic. Plastics that support modern life are inexpensive, strong, and versatile, but are difficult to dispose of and have a serious impact when released into the environment. Polyethylene, in particular, is the most widely produced plastic in the world, with more than 100 million tons distributed annually. Since it can take decades to decompose--and along the way can harm wildlife and degrade into harmful microplastics --its disposal is an urgent issue for mankind. In 2017, European researchers discovered a potential solution.

Breakthroughs, discoveries, and DIY tips sent every weekday. The golden silk, a luxury once reserved for Roman emperors, has been recreated by modern scientists. In a study published in Advanced Materials, a research team at POSTECH (Pohang University of Science and Technology) announced they have successfully produced the 2,000-year-old textile known as Sea Silk. They accomplished this using threads from the common pen shell, farmed along the Korean coast. The team's work also explains the origin of the material's characteristic golden hue and its famed resistance to fading over millennia.

Measurement-based quantum computation (MBQC) offers a fundamentally unique paradigm to design quantum algorithms. Indeed, due to the inherent randomness of quantum measurements, the natural operations in MBQC are not deterministic and unitary, but are rather augmented with probabilistic byproducts. Yet, the main algorithmic use of MBQC so far has been to completely counteract this probabilistic nature in order to simulate unitary computations expressed in the circuit model. In this work, we propose designing MBQC algorithms that embrace this inherent randomness and treat the random byproducts in MBQC as a resource for computation. As a natural application where randomness can be beneficial, we consider generative modeling, a task in machine learning centered around generating complex probability distributions. To address this task, we propose a variational MBQC algorithm equipped with control parameters that allow to directly adjust the degree of randomness to be admitted in the computation. Our numerical findings indicate that this additional randomness can lead to significant gains in learning performance in certain generative modeling tasks. These results highlight the potential advantages in exploiting the inherent randomness of MBQC and motivate further research into MBQC-based algorithms.

Whereas machine learning models typically learn language by directly training on language tasks (e.g., next-word prediction), language emerges in human children as a byproduct of solving non-language tasks (e.g., acquiring food). Motivated by this observation, we ask: can embodied reinforcement learning (RL) agents also indirectly learn language from non-language tasks? Learning to associate language with its meaning requires a dynamic environment with varied language. Therefore, we investigate this question in a multi-task environment with language that varies across the different tasks. Specifically, we design an office navigation environment, where the agent's goal is to find a particular office, and office locations differ in different buildings (i.e., tasks). Each building includes a floor plan with a simple language description of the goal office's location, which can be visually read as an RGB image when visited. We find RL agents indeed are able to indirectly learn language. Agents trained with current meta-RL algorithms successfully generalize to reading floor plans with held-out layouts and language phrases, and quickly navigate to the correct office, despite receiving no direct language supervision.

Today, companies everywhere are generating unprecedented amounts of data. While data has always grown naturally as a byproduct of economic and business activity, these days, as more and more of our personal and work lives take place online, humans are creating an abundance of data daily. In fact, 90% of all the world's internet data has been created since 2016. For more than a decade, only the so-called FAANG companies (Facebook, Apple, Amazon, Netflix, and Google) were in the position to take advantage of collecting vast amounts of data at scale. For these companies, data is the prime product and inherent to their value proposition, so they invested early in AI teams, servers, network infrastructure, and more.

Human consciousness is one of the grand mysteries of our time on earth. How do you know that you are "you"? Does your sense of being aware of yourself come from your mind or is it your body that is creating it? What really happens when you enter an "altered" state of consciousness with the help of some chemical or plant? While you would think this basic enigma of our self-awareness would be at the forefront of scientific inquiry, science does not yet have strong answers to these questions.

It is said that a successful product is a reflection of who we are. Usually, our unique perspective is what leads to the initial discovery. As the product acquires feedback, our keen desire to respond is what drives further innovation. Time and time again I see technology products starting on the right footing only to deteriorate as companies push them to scale. The internet now is quite mature, so this is becoming to stop using emojis finally experience.

Is AI the technologies people use to make machines intelligent, or, is it the movement towards the goal of achieving machine intelligence? According to John McCarthy, AI is actually a science. But it might be more helpful to think of AI as a goal. If AI is considered to be a collection of technologies, then you can argue all day about what is and what isn't AI. Are software robots AI? Are self-driving cars AI?