Intra-class terrain differences such as water content directly influence a vehicle's ability to traverse terrain, yet RGB vision systems may fail to distinguish these properties. Evaluating a terrain's spectral content beyond red-green-blue wavelengths to the near infrared spectrum provides useful information for intra-class identification. However, accurate analysis of this spectral information is highly dependent on ambient illumination. We demonstrate a system architecture to collect and register multi-wavelength, hyperspectral images from a mobile robot and describe an approach to reflectance calibrate cameras under varying illumination conditions. To showcase the practical applications of our system, HYPER DRIVE, we demonstrate the ability to calculate vegetative health indices and soil moisture content from a mobile robot platform.

According to Hansons, Turing's PhD dissertation and On Computable Numbers are both hailed as foundational works in the field of theoretical computer science. Lichfield-based Rare Book Auctions, sister company to Hansons, had valued both of the papers at between 40,000 and 60,000. But the dissertation from 1938 or 1939, called Systems of Logic Based on Ordinals, sold for 110,500. Other top selling lots included Computability and λ-Definability and The World Problem in Semi-Groups with Cancellation, which sold for 26,000 and 28,600 respectively. Turing's final major work from 1952, called The Chemical Basis of Morphogenesis, went for 19,500, while his first published paper from 1935, Equivalence of Left and Right Almost Periodicity, sold for 7,800.

Current artificial intelligence systems -- from large language models to autonomous robots -- excel at narrow tasks but lack key qualities of sentient beings: intrinsic motivation, affective interiority, autobiographical sense of self, deep creativity, and abili ties to autonomously evolve and adapt over time. Here we introduce Sentience Quest, an open research initiative to develop more capable artificial general intelligence lifeforms (AGIL) that achieve these grand challenges with an embodied, emotionally adaptive, self - determining, living AI, with core drives that ethically align with human s and the future of life. Our vision builds on ideas from cognitive science and neuroscience -- from Baars' Global Workspace Theory and Damasio's somatic mind, to Tononi's Integrated Information Theory and Hofstadter's narrative self -- synthesizing these into a novel cognitive architecture. We describe an approach that integrates intrinsic drives (e.g., survival, social bonding, curiosity), a global "Story Weaver" workspace for internal narrative and adaptive goal pursuit, and a hybrid neuro - symbolic memory that logs the AI's life events as structured "story objects." Implemented in humanoid robots like Sophia, this architecture enables adaptive behavior grounded in a human - like body, in pursuit of experiential learning homologous to human experiences. Early resu lts are promising, with a driver - based goal system generating self - motivated actions, a narrative memory allowing the robot to refer to its own experiences, and integrated information measures (Φ) quantifying evolving cognitive integration. We discuss ethi cal implications, exploring how co - evolution with humans via an information - centric ethics ("SuperGood" principle) may guide both developers and AI systems to ensure value alignment. Sentience Quest is presented as a call to action: a collaborative, open - source effort to imbue machines with accelerating sentience in a safe, transparent, and beneficial manner. 2

Without a human-centric approach, OpenAI ChatGPT runs on the data available on the various channels, which can also deliver services without meeting the context requirements. Sometimes, it writes plausible-sounding content but can be trustworthy. The new kid on the block, AI-powered ChatGPT offers numerous exceptional services and is claimed to be useful for coding, content writing, etc., minimizing human intervention. As erudite machinery becomes a trending sensation, companies can also see AI biases, security risks, and less personalized CX. The uncapped accessibility, and unrestricted usage of ChatGPT have increased the cybersecurity risks that can hamper the whole organization. Through ChatGPT, cybercriminals can draft a fraudulent email carrying unsecured links, attachments providing sensitive data, or instructions regarding transferring money into specific accounts from a reputed company or person.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The Justice Department charged a California man of hurling threats of violence against the Massachusetts-based Merriam-Webster Inc., while he allegedly accused the dictionary of promoting "lies and anti-science propaganda" regarding its gender definition entries for the words "woman" and "girl." Jeremy David Hanson, 34, of Rossmoor, California, was charged in federal court in Springfield, Massachusetts, by criminal complaint with one count of interstate communication of threats to commit violence. He was arrested and made an initial federal court appearance in the Central District of California on Wednesday.

And the abstract structures get to hang around a lot longer. I mean one of the …as in the single layer hidden unit networks, I mean one problem was this complexity business about loading data. So you have enough capacity with good representation, but then you couldn't learn anything.

It was difficult to sleep at night, wishing good men dead. This was but one of the hurtful things I felt in my bones and wished I could ignore. It was an ugly truth waving its arms that I turned my gaze from, that I didn't like to admit even to myself. But while my bag warmed me with the last of its power and my breath spilled out in white plumes toward the roof of our tent, while the flicker of a whisper stove melted snow for midnight tea, I lay in that dead zone above sixty thousand feet and hoped not just for the failure of those above me, but that no man summit and live to tell the tale. Not before I had my chance.

Hanson: OK Geoff, thanks for joining me in this chat. This is for AIhub, and I've recorded three or four different conversations and it sort of started out thinking about– what is AI, but it really started out with an old friend of mine (we overlapped in Graduate School), Michael Jordan, who had written several articles (one in Medium) and I wrote a reply, which got some attention, mainly from Mike. He and I had this discussion and I disagreed so much with him I wanted to just see what was going on. Even if you haven't been paying attention you'll notice that something is happening. He was basically saying that the deep-learning phenomenon that's happening right now is – I almost think of it as like The Beatles, when Beatlemania started, we're in deep-learning mania. But, there's a lot of good things happening too, and as I pointed out to him, protein folding.. He said "I agree, but of course, they didn't solve the problem!". I said "you're creating these diminishing comments to create an atmosphere of'this is going to fail, the AI winter is going to come'. Why are you doing this? Don't you realize you're like the only person who doesn't get this". Hanson: Now I know that because I've debated him back in the 80s!, I will warn you this is a Gary Marcus free zone and I'm not going to talk about him. Hinton: In 2015 he made a prediction that computers wouldn't be able to do machine translation. Hanson: Yes, I know, but Gary is the most inconsistent… consistently inconsistent person I know. I wish people would stop taking him so seriously. Anyway, I knew Michael back in grad school and at that point he was always focused on the margins of things – I mean, important things. There's a sense in which he really is rejecting the whole DL thing strongly, and he's an interesting character in this. Now, you on the other hand have had, at least what I've heard you say in other contexts, that deep learning concerns you. I think that Yoshua Bengio has had a lot of concerns as well about DL. So we're doing classification and it works well, but how does it compare to human thought and reasoning, and all the wonderful things humans do?

Hanson: Terry, thanks so much for joining this videocast or podvideo, I don't really know what to call it. When I started trying to conceptualize what I was getting at, I wanted to talk to people who had a clear and obvious perspective on what they thought AI is. And you're particularly unique, and special in this context, because you have been consistent since… Well, there's a great book that you have a chapter in and I think Jim Anderson edited in 1981, called "Parallel Models of Associative Memory". Sejnowski: It's interesting you brought that up because I met Geoff Hinton in San Diego in 1979 at a workshop he and Jim organized that resulted in that book. It was my first neural network workshop. We were all interested the same things. There was no neural network organization or community at that time – We were a bunch of isolated researchers working on our own. Hanson: And probably not well appreciated, by talking about neural networks, or neural modelling. Sejnowski: We were the outliers. But we had a great time talking with each other. Hanson: Going back to the book, you had a chapter called skeleton filters in the brain. I think that was the name of it. Perhaps not the best title in the world, but still… "Skeleton filters" is a little scary, I gotta say. But, it was a really incredibly easy read – I just read it the other day again. And, in it, you're really going in a subtle way from biophysics, modelling a neuron and referencing everybody, you know Cowen, and everybody who'd developed a differential equation, or anything up to semantics and cognition. But biophysical modeling, this kind of category you might associate with biophysics of neural modelling, in that neurons and circuits matter and that's what we're modelling, for that purpose – that's the purpose of it. For example, I think you mentioned Hartline and Ratliff, and Limulus crab retina. And this provided an enormous amount of data well into the 60s where people were actually modelling and there were predictions and it was very tightly tied to the crab. Sejnowski: By the way, although it's called a Horseshoe Crab, and looks like one, Limulus has eight legs, so it's an arachnid.

PROVO (ABC4) – Algorithm can be a scary word, as Brigham Young University computer science professor Quinn Snell admits. The term, which has since reached a commonplace status in the modern-day lexicon, can conjure up imaginings of intrusive data analysis by artificial intelligence-led supercomputers that can understand human nature better than we as a species understand ourselves. Snell acknowledges that the expression can be a scary one for many, but when used properly, algorithms can be used to create impactful and positive change in human society. "When we say algorithm it can throw people off and make people nervous with artificial intelligence and all the hype surrounding that. But what we're really talking about is, the data is telling us a story," Quinn explains to ABC4.com.