Let’s talk about AI Art, Lensa, ChatGPT, and why it’s all deeper than you think.
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And how we can use them. “The Landscape of AI Tools” is published by HungryMinded in Seeds For The Future.
Ispace Inc. is a private Japanese company developing robotic landers and rovers for missions to the Moon. It aims to compete for both transportation and exploration mission contracts from space agencies and private industry. If successful, these spacecraft and the accompanying vehicles could enable clients to discover, map, and use the natural resources on Earth’s nearest neighbour.
In addition to its headquarters in Tokyo, the company has offices in the United States and Luxembourg, employing around 200 people. Although founded in 2010, its team of engineers had earlier competed in the Google Lunar X Prize.
Following more than a decade of research and development, ispace yesterday launched Hakuto-R Mission 1 – delivered into space on a partially reusable Falcon 9 Block 5 rocket. The spacecraft will now perform orbital manoeuvres, taking it as far as 1.5 million km (932,000 miles) from Earth, before arriving at the Moon sometime in April 2023.
An accelerometer, camera, and microprocessor make up the hardware of a sensing and autonomy system for tiny flying robots.
Sawyer B. Fuller
Object detection means all the techniques and means for detecting, identifying, and classifying objects in an image. Recently, the field of artificial intelligence has seen many advances thanks to deep learning and image processing. It is now possible to recognize images or even find objects inside an image. With deep learning, object detection has become very popular with several families of models (R-CNN, YOLO, etc.). However, most of the existing methods in the literature adapt to the training database and fail to generalize when faced with images belonging to different domains.
Although most architectures are optimized for well-known benchmarks, significant results have been achieved using CNNs for tasks particular to a certain domain. However, these domain-specific solutions are often well-tuned for a specific target dataset, starting with carefully chosen architecture and training techniques. This method of training models has the drawback of unnecessarily adapting the approaches to a particular dataset. To address this issue, a research team from Intel offers a different strategy that also serves as the foundation of the Intel® Geti™ platform: a dataset-agnostic template for object detection training made up of carefully selected and pre-trained models and a reliable training pipeline for additional training.
The authors experimented with architectures in three categories: lightweight, extremely accurate, and medium, to develop a scope of the models used for the various object detection datasets regardless of complexity and object size. Pretrained weights are employed to reach model convergence quickly and begin with high accuracy. In addition, a data augmentation operation is performed to augment images with a random crop, horizontal flip, and brightness and color distortions. Multiscale training was applied for medium and accurate models to make them more robust. Additionally, to strike a balance between accuracy and complexity, the authors empirically selected particular resolutions for each model after conducting several trials. Early stopping and the adaptive ReduceOnPlateau scheduler are also used to end training if a few epochs of training do not further improve the outcome.
Achieving a long-sought goal of regenerative medicine.
Researchers from the University of California, San Francisco, (UCSF) engineered molecules that function as “cellular glue,” enabling them to precisely direct how cells bond with each other. This is a significant step toward regenerative medicine’s long-term goal of creating new tissues and organs, according to a press release.
Adhesive molecules are naturally present in the body and keep the tens of trillions of cells together in organized patterns. They build neural networks, develop structures, and direct immune cells to specific areas of the body. Adhesion also makes cell communication easier to maintain the body functioning as a self-regulating whole.
The science of meteorology has taken tremendous strides in the past two decades thanks to a confluence of several inputs: improved computing power; better modeling of data; more observational data points ranging from the device in your hand to the satellites orbiting earth; and advanced data science applications. As recently as two decades ago, providing an accurate forecast three to four days out was considered innovative. Today a five-day forecast is accurate about 80 percent of the time. Most weather experts are predicting even more extended accuracy by 2030 with the application of artificial intelligence for numerical weather prediction output. But beyond improving accuracy, here are a few other forecasting trends to watch in 2023.
Hyper-relevant Forecasting
Just like other sets of analytics have become more tailored, or localized to the user, weather intelligence is bringing forecast relevancy to an individual organization or entity. A business can determine which risks are most significant to their operations, such as wind gusts, lightning, heavy rains, and ice accretion, and then be alerted when those risk thresholds are met. While there’s growing use among utilities, municipalities and other infrastructure decision makers, hyper-relevant forecasting is growing in other sectors. For example, by combining weather data with purchasing trends and consumer demand data, one grocery chain learned that even a small change in temperature can result in a significant shift in what people buy. The store improved its revenues by modeling this impact and managing inventory accordingly. Even sports teams are applying hyper-relevant forecasting for everything from daily stadium operations to food and beverage decisions and strategic game plays.
“There’s a lot of cheap knowledge out there. I think this could be a danger in education, and it’s not good for kids,” said one educator of OpenAI’s viral chatbot.
Don’t worry, human drummers. It won’t take your job.
One of the best qualities of talented drummers is to be able to show all their dexterity by staying in the metronome beats. Xiaomi should be aware of this because the humanoid bot it produces is on its way to becoming a rock star.
Search Engine Optimization (SEO) is the process of optimizing on-page and off-page factors that impact how high a web page ranks for a specific search term. This is a multi-faceted process that includes optimizing page loading speed, generating a link building strategy, as well as learning how to reverse engineer Google’s AI by using computational thinking.
Computational thinking is an advanced type of analysis and problem-solving technique that computer programmers use when writing code and algorithms. Computational thinkers will seek the ground truth by breaking down a problem and analyzing it using first principles thinking.
Since Google does not release their secret sauce to anyone, we will rely on computational thinking. We will walk through some pivotal moments in Google’s history that shaped the algorithms that are used, and we will learn why this matters.
