Eps 1212: ai

The too lazy to register an account podcast

Host image: StyleGAN neural net
Content creation: GPT-2, transformers, CTRL


Randy Adams

Randy Adams

Podcast Content
Far from being science fiction, artificial intelligence is becoming an everyday sight in today's world. Fueled by enthusiasm about the potential of AI, astonishing advances have been made in areas such as speech recognition, image recognition and speech processing, and machine learning.
A range of AI technologies are also being used to predict, combat and understand pandemics such as COVID-19 and to predict, combat and understand pandemics such as COIDV-19. Although AI is only one of the technologies used in robotics, it helps robots learn new skills and help them get closer to themselves - controlling cars, drones, and even drones. There is IBM Watson, which harnesses the power of research in a variety of areas, while Microsoft Azure Machine Learning and TensorFlow have also made headlines around the world. Among the new areas where AI has taken root are music and art, where its algorithms manifest themselves in music, art and beyond.
Robots are also increasingly being used to support surgery, and artificial intelligence is being used to develop innovative drug therapies and tailored medicine. Combined with machine learning and emerging AI tools, RPA automates a large portion of the workplaces in the company, enabling its tactical bots to pass intelligence to AI and respond to process changes. It is also being developed and reviewed at a pace that, if people work with it, is not possible if only people are entrusted with their tasks. AI in ways we would not have imagined otherwise, such as developing innovative drugs, therapies, or tailored medicines.
The use of artificial intelligence also raises ethical questions, as AI tools offer companies a range of new functionalities, and AI systems, for better or worse, will reinforce what they have already learned. This is problematic, because the machine learning algorithms that underlie many of the most advanced AI tools are inherently biased in the data they are provided for training. Due to the nature of the data used to train AI programs, the potential for machine-learning distortions is inherent and needs to be monitored. Anyone who wants to use machine AI in any part of the real world must incorporate ethical aspects into their decisions in production systems and strive to avoid prejudice.
Weak AI tends to be simple and simple - task-oriented, while strong AI performs more complex and human tasks - such as creating images. This type of AI will be able to infer human intentions and predict behavior with the necessary skills, with some AI systems becoming integral members of human teams. For example, the self-driving car and the underlying AI-powered systems are capable of fully automating the current generation of AI technologies, but this is largely due to the use of machine learning.
As mentioned above, narrow AI is what we see everywhere on computers today - intelligent systems that have been taught and learned to perform certain tasks without being explicitly programmed to do so. This is the first step towards what later became known as rule-based AI, as humans have the ability to develop these systems in addition to the rules of their natural language processing abilities. A.I.s can be classified according to their functioning, which is particularly important considering how complex their systems are. Although there is a natural overlap between robotics and AI, this does not mean that robots are capable of acting autonomously or of understanding and navigating the world around them.
A is carried out by AI, and the resulting combination enables intelligent decision-making through a combination of natural language processing, machine learning, artificial intelligence, and artificial neural networks.
The main drawback of using AI is that it is expensive to process and programming AI requires large amounts of data. Large technology companies offer a variety of tools to build and train their own machine learning models, as well as web services that give you access to AI-based tools. Some even use this approach to help you design AI models, so you can use AI effectively without having to "use" it effectively to build AI. Although hardware, software, and human resources costs for AI can be expensive, many vendors include AI components in their standard offerings and offer a wide range of services such as training, testing, training, and development.
One of the machine learning systems that has caught the public's attention is DeepMind's Deep Mind, an ancient Chinese game whose complexity has puzzled computers for decades. Deepmind has made headlines for breaking new ground in AI research, although it is likely Google and Deepmind have had the biggest impact on public awareness of AI.
Turing followed a few years later with John McCarthy, who first used the term "artificial intelligence" to describe a machine that could think autonomously.
Today, more than six decades later, advances in computer science and robotics are helping automate tasks that previously required physical and cognitive work from humans. As science, technology, and artificial intelligence continue to improve, the expectations and definitions of AI are changing. What we now consider "AI" could become the mundane functions of tomorrow's computers; while AI will not replace all jobs, it seems certain that it will change the nature of work, with the only question being how quickly and how deeply automation will change the workplace. With the industry's leaders like Turing and McCarthy, there is no doubt that general artificial intelligence will unbalance society in the near future.