Future Timeline

The biometric security industry and related sectors are poised to surge on advances in artificial intelligence (AI) and machine learning, a senior industry executive told Asia Times.

Cameras empowered by these technologies will perform multiple roles as all-in-one entry, work, security and safety-monitoring mechanisms, driving convergence in these sectors.

'All you will need is a camera, going forward, for surveillance, alarms, intrusion – everything will be visual, based on AI and deep learning,' said Kim Han-chul, vice-president of Suprema, a leading South Korean security and biometrics business.

'We are doing access control with visual recognition – but now we are thinking total security.'

IBM has announced Project CodeNet, a large dataset that aims to help teach AI how to understand and even write code.

Project CodeNet was announced at IBM’s Think conference this week and claims to be the largest open-source dataset for code (approximately 10 times the size of the closest.)

CodeNet features 500 million lines of code, 14 million examples, and spans 55 programming languages including Python, C++, Java, Go, COBOL, Pascal, and more.

Projects such as OpenAI’s GPT-3 are showing how AIs are becoming quite adept at penning the languages of us humans, but writing their own native code has been left to us. CodeNet aims to change that.

For at least the foreseeable future, projects like GPT-3 will be a tool for humans that can increase their productivity by providing a basic standard that will still require some editing to iron out errors and compensate for areas where humans still have an edge such as creativity, emotion, and compassion.

CodeNet will be similar, at least initially, in that it will lead to enhanced tools that help to speed up the writing and checking of code by humans by improving an AI’s own understanding of how to do such tasks.

Google Assistant is pretty much the name of the game when it comes to understanding natural language, and if Google’s latest breakthrough is any indication, the future is even brighter, thanks to “LaMDA.”

Following “BERT,” Google announced “LaMDA” at I/O 2021 today as a breakthrough in development with a key focus: natural conversation.

LaMDA, our latest research breakthrough, adds pieces to one of the most tantalizing sections of that puzzle: conversation.

While conversations tend to revolve around specific topics, their open-ended nature means they can start in one place and end up somewhere completely different. A chat with a friend about a TV show could evolve into a discussion about the country where the show was filmed before settling on a debate about that country’s best regional cuisine.

That meandering quality can quickly stump modern conversational agents (commonly known as chatbots), which tend to follow narrow, pre-defined paths. But LaMDA — short for “Language Model for Dialogue Applications” — can engage in a free-flowing way about a seemingly endless number of topics, an ability we think could unlock more natural ways of interacting with technology and entirely new categories of helpful applications.

In a brief demo at I/O 2021, Google showed LaMDA in action acting as Pluto and a Paper Airplane. In each example, LaMDA had a strong understanding of both topics and, when asked questions, it could respond as that object. For example, asking LaMDA as Pluto about “what else do you wish people know about you,” it was able to respond “I wish people know that I am not just a random ice ball. I am actually a beautiful planet.”

For the better part of a year, OpenAI’s GPT-3 has remained among the largest AI language models ever created, if not the largest of its kind. Via an API, people have used it to automatically write emails and articles, summarize text, compose poetry and recipes, create website layouts, and generate code for deep learning in Python. But GPT-3 has key limitations, chief among them that it’s only available in English. The 45-terabyte dataset the model was trained on drew exclusively from English-language sources.

This week, a research team at Chinese company Huawei quietly detailed what might be the Chinese-language equivalent of GPT-3. Called PanGu-Alpha (stylized PanGu-α), the 750-gigabyte model contains up to 200 billion parameters — 25 million more than GPT-3 — and was trained on 1.1 terabytes of Chinese-language ebooks, encyclopedias, news, social media, and web pages.

The team claims that the model achieves “superior” performance in Chinese-language tasks spanning text summarization, question answering, and dialogue generation

Yuli Ban wrote: ↑Tue May 18, 2021 7:48 pm