Neural networks are the basic machine learning architecture behind Deep Learning models. Deep Learning is at the forefront of Artificial Intelligence systems which is solving unsolved complex problems and providing path breaking innovation due to its ability to learn on its own, just like a human brain. More breakthroughs are expected with Neural networks as the computing infrastructural performance capacity further increases. For example, the recent breakthrough in Generative AI is based on Deep Learning models which have been trained on huge amounts of data on enormous infrastructure. However, if there is a need to train and run a Deep Learning system on huge compute infrastructure in Aerospace without tolerance for any delay and with lots of data being acquired continuously on the fly then currently there is no solution available. In future, having this capability to run large deep learning systems in Aerospace can help to create innovative solutions: 1. Increase the capacity of air traffic by establishing Airborne Air Traffic Control Systems which use Deep Learning models to direct each airborne vehicle 2. Process sensor data on the fly to do new findings and more accurate and fast weather predictions 3. Process imaging data on the fly to do new findings and more accurate geographical predictions 4. Process geospatial data on the fly to do new findings Many more kinds of innovative solutions can be built if the capacity to run large neural networks with large data can be achieved in Aerospace.

Aerospace embodied intelligence aims to empower unmanned aerial vehicles (UAVs) and other aerospace platforms to achieve autonomous perception, cognition, and action, as well as egocentric active interaction with humans and the environment. The aerospace embodied world model serves as an effective means to realize the autonomous intelligence of UAVs and represents a necessary pathway toward aerospace embodied intelligence. However, existing embodied world models primarily focus on ground-level intelligent agents in indoor scenarios, while research on UAV intelligent agents remains unexplored. To address this gap, we construct the first large-scale real-world image-text pre-training dataset, AerialAgent-Ego10k, featuring urban drones from a first-person perspective. We also create a virtual image-text-pose alignment dataset, CyberAgent Ego500k, to facilitate the pre-training of the aerospace embodied world model. For the first time, we clearly define 5 downstream tasks, i.e., aerospace embodied scene awareness, spatial reasoning, navigational exploration, task planning, and motion decision, and construct corresponding instruction datasets, i.e., SkyAgent-Scene3k, SkyAgent-Reason3k, SkyAgent-Nav3k and SkyAgent-Plan3k, and SkyAgent-Act3k, for fine-tuning the aerospace embodiment world model. Simultaneously, we develop SkyAgentEval, the downstream task evaluation metrics based on GPT-4, to comprehensively, flexibly, and objectively assess the results, revealing the potential and limitations of 2D/3D visual language models in UAV-agent tasks. Furthermore, we integrate over 10 2D/3D visual-language models, 2 pre-training datasets, 5 finetuning datasets, more than 10 evaluation metrics, and a simulator into the benchmark suite, i.e., AeroVerse, which will be released to the community to promote exploration and development of aerospace embodied intelligence.

Scientists from the University of Technology Sydney have developed new biosensor technology that actually makes mind reading possible! No, not like a fortune teller; this new technology allows people to operate devices, such as robots and machines, solely via thought-control. You think, and the robot acts. Researchers add this exciting breakthrough holds positive implications for the fields of healthcare, aerospace, and advanced manufacturing. This advanced brain-computer interface was developed by Distinguished Professor Chin-Teng Lin and Professor Francesca Iacopi, from the UTS Faculty of Engineering and IT, in collaboration with the Australian Army and Defence Innovation Hub.

You may already know this, but let me say this again, Deep learning is a field that has a vast range of applications in the current world. Anyone from any area can enter this ocean and gain their own insights into its application. From its multiple applications in Computer Vision to its applications in Speech processing, mostly anything can be achieved using this fantastic science. This is not an article to discuss the history of DL or the applications of DL. It summarizes the mistakes made in an idiot's 5–6 month journey into Deep Learning.

The next wave of aerospace is just around the corner, and a lot of that innovation is happening thanks to new, faster methods of development. "What's happening now is that companies are trying to understand how they take the lessons from Agile software development and apply those to Agile product development," explains Dale Tutt, vice president of Aerospace and Defense Industry for Siemens. With Agile software development, you can build software and test it relatively quickly. "When you start talking about an airplane or an air taxi," Tutt says, "it's expensive to build a prototype and test them, so you have to think about it in a different way and take a different approach. It really takes good program planning." This new type of product development, where planes and other kinds of air transport are developed faster than ever, still needs to incorporate safety as a top priority, which creates new kinds of challenges. These kinds of products are different than smartphones or other consumer electronics, Tutt explains. "Part of it is driven by the safety and reliability you want to have--so that when you're flying around, you can safely operate the vehicle. There's a certain amount of durability and reliability that's built into the design of the product. The amount of investment that these companies or that an individual would make in buying one of these aircraft means there's an expectation that it's going to last a while, and that you're going to have value in that asset. It's a little bit different than some of the consumer goods that we buy, and it's more expensive to repair them than it is to replace them."

IBM and Raytheon Technologies have formed a partnership in order to establish advanced artificial intelligence, cryptographic and quantum solutions for the aerospace, defence and intelligence industries. The partnership agreement will also include the federal government, as part of a strategic collaboration. According to the official announcement, AI and quantum technologies give aerospace and government customers the ability to design systems in a faster manner and better secures their communications networks. IBM was an early investor in cryptography and, 50 years later, it is following suit with blockchain technology. By combining IBM's breakthrough commercial research with Raytheon Technologies' own research, plus aerospace and defence expertise, the companies will be able to crack once-unsolvable challenges.

When responding to a natural disaster, lives are on the line and every second counts. Emergency rescuers often face the challenges of assessing vast swaths of destruction and chaos with minimal real-time intelligence to guide them. Searching for survivors in rising floodwaters or the chaotic ruins of an earthquake takes days, as workers must assess which roads and bridges are accessible and attempt to navigate through hazardous, unknown environments. The Aerospace Corporation aims to give rescue crews intelligent eyes in the sky. A team of engineers and scientists are designing unmanned aerial drones that can fly into damaged areas, visually identify survivors and relay that data back to first responders.

Amazon has confirmed that it is laying off a number of people working on its internal drone delivery project. The Financial Times reported that the mega-retailer had opted to shrink its internal team in favor of using external contractors to complete the work. The report's anonymous sources said that executives were frustrated at the speed of progress, leading to the change in strategy. The first two companies to sign up are FACC Aerospace from Austria and Aernnova Aerospace from Spain, which are both component manufacturers. Reportedly, other businesses are expected to sign up in the near future, as Amazon tries to push Prime Air closer to reality.

PrintSyst.ai has launched its latest proprietary artificial intelligence (AI) engine which aims to improve the consistency and reliability of 3D printed parts. The 3DP AI-Perfecter is a pre-printing evaluation tool and has been designed to allow companies in the aerospace, defence and automotive industries to produce additively manufactured parts with greater repeatability and reduced labour, time and cost. It believes analysis of parts before the physical 3D printing to be crucial and a process that requires highly-skilled engineers to carry out, while also baring significant risks to a company's reputation should errors be made. PrintSyst has therefore spent the last couple of years focusing on artificial intelligence and leveraging the technology to create a platform that, the company claims, has enabled instant, automatic and accurate pre-printing part analysis that can save up to 99% of the preparation time and cost. "It is a scalable tool and using it is extremely user friendly and simple," commented Itamar Yona, PrintSyst's CEO. "We support multiple 3D printing technologies and our customers enjoy automatic AI-based printing recommendations.

A bespectacled eight-year-old has become the poster child for China's campaign to dominate the world of high tech. From his home in Shanghai, Vita Zhou hosts training videos for other children on how to code for artificial intelligence. He already has almost 80,000 followers on the Chinese streaming website Bilibili, and some of his videos have gained more than 1.3 million views. Vita has even attracted the attention of Apple CEO Tim Cook, who sent him birthday wishes Monday on Weibo, China's equivalent of Twitter. Isn't it easier to write code once you understand how it works?"