The MMT, first reported by Surrey researchers in 2020, overcomes long-standing challenges associated with transistors and can perform the same operations as more complex circuits. This latest research, published in the peer-reviewed journal Scientific Reports, uses mathematical modelling to prove the concept of using MMTs in artificial intelligence systems, which is a vital step towards manufacturing. Using measured and simulated transistor data, the researchers show that well-designed multimodal transistors could operate robustly as rectified linear unit-type (ReLU) activations in artificial neural networks, achieving practically identical classification accuracy as pure ReLU implementations. They used both measured and simulated MMT data to train an artificial neural network to identify handwritten numbers and compared the results with the built-in ReLU of the software. The results confirmed the potential of MMT devices for thin-film decision and classification circuits.

Surrey University researchers have demonstrated proof-of-concept of using their multimodal transistor (MMT) in artificial neural networks that mimic the human brain. According to the university, the advance marks a key step towards using thin-film transistors as artificial intelligence hardware and moves edge computing forward, with the prospect of reducing power needs and improving efficiency, rather than relying solely on computer chips. The MMT, first reported by Surrey researchers in 2020, is said to overcome long-standing challenges associated with transistors and can perform the same operations as more complex circuits. This latest research, published in Scientific Reports, uses mathematical modelling to prove the concept of using MMTs in artificial intelligence systems. Using measured and simulated transistor data, the researchers show that well-designed multimodal transistors could operate robustly as rectified linear unit-type (ReLU) activations in artificial neural networks, achieving practically identical classification accuracy as pure ReLU implementations.

A new survey found that more than half of B.C's. artificial intelligence companies believe the federal government is not doing enough to boost the sector, and half have considered leaving the province. The non-profit industry association, Artificial Intelligence Network of B.C., says there are more than 150 AI-related firms in B.C. and more than 65 submitted responses to its survey, which was conducted by CityAge and released this week. More than 56 per cent of respondents said the federal government needs to do more to help the local AI sector grow, with 31 per cent saying its efforts were lacking and 24 per cent saying they needed major attention. Half of respondents said they have considered moving their companies out of B.C. They main reasons they gave were a desire to connect to bigger markets (35 per cent) and to operate in a better taxation and regulatory environment (11 per cent).

Artificial intelligence has a bias problem and it's completely our fault. Last week, Amazon decided to ditch the secret AI tool it was using to source new recruits when it discovered their system was actively favouring male over female candidates. It transpires their network had been trained to vet applicants by observing patterns in CVs submitted over a 10-year period and – as one would expect given the perpetuating male dominance across the tech industry – the vast majority of those applications came from men. This caused the AI to perceive male candidates as preferable, going so far as to actively penalise CVs that even mentioned women. Does this infer that the AI was in some way gender biased?

Scientists have developed a neural network using DNA, which can correctly identify numbers encoded in molecules using machine learning, an advance that may pave the way for biological machines with artificial intelligence. Artificial neural networks are mathematical models inspired by the human brain. Despite being much simplified compared to their biological counterparts, artificial neural networks function like networks of neurons and are capable of processing complex information. The ultimate goal for this work is to programme intelligent behaviours (the ability to compute, make choices, and more) with artificial neural networks made out of DNA. "In this work, we have designed and created biochemical circuits that function like a small network of neurons to classify molecular information substantially more complex than previously possible," said Lulu Qian, assistant professor at California Institute of Technology in the US.

Sex robots will soon be able to say'no' to unwanted advances from humans. Dr Sergi Santos, the Spanish inventor of sexbot Samantha, claims he's working on a version of his AI doll that can enter'dummy' mode in certain situations. For instance, 'dummy' mode may be switched on if sensors under Samantha's skin detect that she is being touched in an aggressive or disrespectful way. The robot will also enter into the unresponsive mode if she is bored with the attentions of her potential lover. Sexbot Samantha (pictured) will enter into a'dummy' mode if the sensors under her skin detect she is being touched in an aggressive or disrespectful way Samantha demonstrated her ability to say'no' to overly aggressive sexual partners during a recent presentation held at the Life Science Centre in Newcastle.

WekaIO CEO Liran Zvibel has a two-pronged plan for launching the parallel-file-system startup to success: He intends... You forgot to provide an Email Address. This email address doesn't appear to be valid. This email address is already registered. You have exceeded the maximum character limit.

Not in my lifetime, says Google's cloud chief Stay up-to-date on the topics you care about. We'll send you an email alert whenever a news article matches your alert term. It's free, and you can add new alerts at any time.

Google has published numerous experiments with its cloud AI technologies, but'Quick, Draw' is perhaps the most fun one yet. Using the same technology that interprets written symbols in Google Translate, the game attempts to guess what you are drawing. When you start, you are prompted to draw a specific thing, and the game continues making guesses until it wins or time runs out. At the end of six rounds, it provides you with information about all your drawings, including other guesses and what other users provided. It learns from every drawing, so theoretically, it should become smarter over time.