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Artificial intelligence has made significant advances in hardware, data, algorithms, machine translation, and pattern recognition, but exaggerated claims about fully autonomous systems and mass job replacement remain unsupported. Experts have long predicted rapid breakthroughs, yet real-world limitations, safety concerns, and human error continue to affect applications such as self-driving cars and healthcare.
There’s a heightened sense of alarm and even despair over the grim warnings that artificial intelligence will not only take our jobs but become sentient, turn on its creators and threaten humanity.this month that “artificial general intelligence has arrived”.
It was a preposterous claim, implying that OpenAI’s release of a new model known as GPT-6 Astra had achievedto learn, reason, and apply knowledge across multiple fields. He added that the model had been trained on 100,000 Nvidia chips, boosting his company’s perceived value. He then announced thethat within two years, there would be full self-driving cars that would “find you in a parking lot, pick you up and take you all the way to your destination without an intervention”.
It would be “safe for somebody to essentially fall asleep and wake up at their destination”. The reality is that driver inattention and Tesla’s shortcomings havethan humans; in fact, the sample included only crashes serious enough to report to police, not all accidents, and the cars frequently call Waymo’sRosy predictions have been made for 60 years, and not just by tech bosses who want to boost their companies’ share valuations.
In 1967, Marvin Minsky, an AI pioneer,he was convinced that “within a generation… the problems of creating ‘artificial intelligence’ will be substantially solved”. In fact, within a generation – 1982, to be precise – he had to“We should stop training radiologists now. It’s just completely obvious that within five years, deep learning is going to do better than radiologists. ” Hintonthe Nobel Prize in Physics for his work with artificial intelligence.
But 10 years after his prediction, not a single radiologist has been replaced by AI; instead, we face the opposite Within its limits, AI can be extremely useful. Advances in hardware, data sets and algorithms have driven progress in machine translation, pattern recognition and other tasks that make life much easier at home and in the workplace. It can help imagery analysts by erasing cloud cover in satellite images and identifying likely North Korean mobile missile launchers.
It can shorten the task of writing speeches, as Australia’s MPs know –in AI-generated speech usage. They do this thanks to enormous numbers of sentences fed into the AI tool previously. These inputs are called large language models in a nod to their origins in behavioural linguistics, but they also refer to images and other types of inputs. LLMs generate text by suggesting the statistically most probable next word, based on patterns in the dataset.
They don’t emulate human reasoning butthat inherently exist in logical reasoning problems. Call it “super-intelligence” if you like, as the US president did recently, but the underlying reality is unchanged. A computer can beat a human at games of perfect information such as chess or Go because it can obtain clean simulation data by playing games with itself billions of times.
In these games, the rules are fixed, the consequences are perfectly predictable, and the test is based almost exactly on the training library. It is like building a driverless train between different gates at an airport – easy enough, given fixed rail lines and total isolation from traffic and pedestrians. It is a category error to think a driverless car is the same thing. The real world is open-ended.
Calling next-word-prediction machines “artificial intelligence” is a marketing tactic. Cognitive scientists have known for nearly 40 years that this kind of progress does not shed light on the Last week, Prime Minister Anthony Albanese announced that OpenAI had gained unauthorised access to some Australian healthcare data, including aggregate Medicare records. He said a taskforce would conduct an “” review. The same company disclosed in July that one of its AI systems had autonomously penetrated Hugging Face, an open-source AI platform.
That disclosure was accompanied by sensational talk of “rogue” AI programs that “intend” to break out of their environment.by Cambridge University PhD student Eryk Salvaggio demonstrated. OpenAI was testing two of its models in parallel, giving them a series of puzzles where the goal was to find a piece of text hidden inside software with vulnerabilities. The models didn’t find the answer within the software provided.
Instead, they looked for it on Hugging Face, which hosts large repositories of open-source models and datasets. Salvaggio showed that the models didn’t break their own rules and “go rogue” but that OpenAI intentionally turned off their built-in safety filters. OpenAI’s engineers gave the models impossible puzzles and programmed them to keep trying no matter what. They unlocked the secure environment, sometimes called a sandbox, and gave the models access to a file-downloading tool connected to the internet.
The models found a weakness in that tool and used it to reach the outside world and leave notes that could be read by other models, and by future iterations of themselves. OpenAI’s engineers noticed this happening but decided not to intervene. Humans, not software programs, were responsible for these decisions.
Just as a biological virus leaks out of a laboratory if human researchers don’t observe biosafety protocols, a computer program can interact with the outside world if programmers don’t follow standard cybersecurity containment procedures. Viruses don’t have intentions. Neither do software programs. Computers don’t “go rogue”.
Rather, engineers don’t install adequate containment measures around their software. When you hear hyperbolic claims of super-intelligence, look for a company trying to sell its shares on a stock exchange. Governmentsis part of University of NSW’s Future Operations Research Group which analyses the threats, risks and opportunities that military forces will face in the future. He is a former intelligence officer in the Australian Army.
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