Domain-specific AI models are easy to create with the right experience and knowledge of AI and data
Domain-specific AI models are in high demand in the global tech market to work efficiently and effectively. There are some key steps to creating perfect domain-specific AI models with data assets and active learning. Tech companies focus on large-scale artificial intelligence implementations to solve multiple B2B problems and drive high return on investment while delivering decision-centric outcomes based on data assets. Domain-specific AI models help to adapt the artificial intelligence implementation cycles with more relevance and efficiency. The global tech market consists of multiple steps for efficient domain-specific AI models. It is expected that artificial intelligence can reach its potential space by being domain-specific AI models in 2022 and beyond.
A brief guide to key steps to creating domain-specific AI models
- Open mindset to data assets
Organizations must have an open mindset to explore some of the key domain-specific data assets to create domain-specific AI models. The group of data scientists should seek suitable data assets as per the business goals and objectives to boost productivity and yield more profit. A business can look out for multiple different strategies and methodologies to improve the quality of data assets efficiently and effectively.
- Transfer learning
Businesses must leverage transfer learning through the basic knowledge from the source domain to have a deeper understanding of new knowledge for the domain-specific AI models. A pre-trained network can act as a feature extractor while using only lower-level features of the neural network if there are different domains.
- Active learning
Active learning in creating domain-specific AI models is important as a type of semi-supervised learning. Businesses can require a small amount of labeled data for reducing manual annotation cost-efficiently. One needs to divide the data assets into seed and unlabeled data with artificial intelligence.
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AI Systems Are Not Humans! US Court Denies The Patent Right To Machines
US law allows only ‘natural persons’ to be registered as individuals; individuals who can take an oath swearing their paternity to the product
Even as the debate around AI gaining sentience hasn’t subsided, artificial intelligence. US patent court struck down Stephen Thaler’s petition seeking a patent for the products developed by the AI system purportedly developed by him. CAFC, the US appeals court, in Thaler V. Vidal’s case ruled out the machine’s candidature to qualify as an inventor under the US Patent Act. The decision is in similar lines to the series of judgments delivered by courts from around the world for the same case. The legal tiff dates back to 2019 when he filed for a patent for the two products, an AI food container developed based on fractal geometry and a torch light. Instead of using his name in the patent document, he mentioned the neural network’s name, DABUS, claiming that the products are its brainchildren.
Back then US Patent and Trademark Office rejected his plea, reasoning that US laws allow only ‘natural persons’ to be registered as individuals; individuals who can take an oath swearing their paternity to the product, which machines or computer software are incapable of. This time the Court of Appeals, supporting the USPTO’s stand, was unambiguous in stating the obsolescence of “metaphysical matters” about “the nature of invention or rights, if any, of AI systems.” It stressed the repeated reference to the term “individuals” is very well in alignment with its interpretation. “While we do not decide whether an AI system can form beliefs, nothing in our record shows that one can, as reflected in the fact that Thaler submitted the requisite statements himself, purportedly on DABUS’ behalf,” CAFC explained. Cutting the game to chase, the judgment hinges on legal requirements which has little to do with the court’s understanding of AI’s capability.
Refusing to give up on his mission, he wants to contest the judgment in the US Supreme court. He says, “The court ignores the purpose of the Patent Act and the outcome that AI-generated inventions are now unpatentable, will have real negative social consequences.” This brings us to the question if artificial intelligence deserves patent rights and why.
When Thaler says, negative consequences, he means the patent laws being misused under the cover of patent law. For example, AI has been responsible for inventions in the medical field which left unpatented could give leeway to idea hoggers, leaving businesses and funders discouraged to pursue research, preventing cutting-edge research to happen in the first place. Experts too are of the opinion that instead of rewriting the IP Acts completely, it would be a reasonable option to create a custom law including artificial intelligence, because, at the end of the day, AI learns by itself and makes decisions for itself. As artificial intelligence is becoming an omnipresent and inevitable part of day-to-day lives, it is highly imperative to consider if AI qualifies to be considered as an entity similar to a POSA (person with Ordinary Skills in Art). The lack of benchmarks to determine if AI’s invention is unique might be another challenge, given the AI’s ability to constantly improve itself.
This argument finds support in Australian Court’s initial assumptions that led to a favorable judgment only to be overruled later by the appeals court. The judges considered the possibility of AI setting its goal, freedom to choose options and pathways towards the goals, and the ability to trawl for data it requires. The court did spell out why AI’s autonomy is not a misnomer, as long as artificial neural networks can choose the algorithms and interact with other networks. If only this argument could be taken forward to establish AI as a legit entity – which would probably be Thaler’s next line of argument – the question of ‘persona’ could be put to rest.
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Meta Testing Its Latest Chatbot With The Public
BlenderBot 3 can converse informally and respond to the kinds of questions you might ask a virtual assistant
In order to gather input on the system’s functionality, Meta’s AI research laboratories have developed a brand-new, cutting-edge chatbot and are enabling members of the public to communicate with it.
The chatbot is available online as BlenderBot 3. According to Meta, BlenderBot 3 can converse informally and respond to the kinds of questions you might ask a virtual assistant, such as “talking about healthy food recipes or discovering kid-friendly services in the city.”
The prototype bot is based on Meta’s prior work with massive language models, or LLMs, which are capable but unreliable text-generation programs, the most well-known of which is OpenAI’s GPT-3. Like other LLMs, BlenderBot undergoes extensive training on massive text datasets, which it then mines for analytical patterns to produce language. Such systems have proven to be remarkably flexible and have been used for a multitude of activities, such as assisting authors in writing their upcoming best-selling books and producing code for programmers. These models do, however, have serious flaws: they frequently generate user-generated answers and repeat errors in their training data, which is a big problem if they are to function as useful digital assistants.
Meta is particularly interested in using BlenderBot to examine this latter issue. A crucial element of the chatbot is its capacity to look up specific topics online. More significantly, people can click on the answers to see where the information came from by doing so. In other words, BlenderBot 3 can list its references.
By making the chatbot accessible to a larger audience, Meta aims to receive input regarding the various problems that large language models encounter. BlenderBot users can report any suspicious responses from the system, and Meta claims to have made every effort to “minimize the bots’ use of foul language, insults, and culturally insensitive comments.”
For IT businesses, making prototype AI chatbots available to the general public has traditionally been considered a risky step. Microsoft published Tay, a chatbot on Twitter, in 2016. Tay was able to learn from the people it interacted with. As was somewhat expected, Tay soon came under pressure from Twitter users to repeat a variety of racist, antisemitic, and misogynistic sentiments. Less than 24 hours after being notified, Microsoft reacted by pulling the bot offline.
Since Tay’s malfunction, the field of AI has undergone significant development, according to Meta, and BlenderBot includes a variety of safety features that should prevent Meta from making the same mistakes Microsoft did.
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Are You Sure Your Favorite Celebrity Is A Human? What If They Are Digital Humans?
Think twice before you go and pine over your celebrity crush who might be a digital human.
Hey there, are you daydreaming over your celebrity crush again? Well, it’s time to crash that dream cause the one you are swooning over might not even be a real human. Wait, what? Yes, you read it right. Their beautiful face, pretty eyes, and everything else that is picture-perfect might just be a digital human rendition mastered by some totally grumpy developer-controlled emotionless AI machine.
Guess, who came up with this! Well, who else if not China with their long list of weird AI innovations? In June 2022, a new music video was released by one of the greatest celebrities in China, Simon Gong, getting around 15 million views. But the hawk-eyed fans were not fooled by it. The news was spread quickly that the singer in the video was not actually Simon Gong, but his digital replica made by Baidu. Netizens all over the world are left baffled and confused over the fact that they should be happy over this digital advancement or disappointed over the lack of human touch.
It also raises the question of why is there such a need for digital humans when there is already a population of 7 billion humans. Well, the answer might make you lose sleep; it is the “round-the-clock” services. After 8 or 9 hours of work, a human will need rest as well as food, but these digital humans can work 24×7. From retail to healthcare and finance, the yearning for digital humans is growing to such an extent that experts predict the global market for digital humans will jump to about $530 billion in 2030, from $10 billion in 2023.
Coming back to the Simon Gong case, what makes this digital human different from its other prototypes is its unique interactiveness. The virtual Gong’s digital duties are not limited to singing. On the latest update of the Baidu App, China’s leading search-plus-feed app, Gong appears on users’ phones, helping with searches and queries using the model-actor’s real voice. Since this interactive search experience was launched in 2021, it has boosted the number of voice search queries on Baidu App by 18.2%.
So, think twice before you fall for a ‘digital idol’!!
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