By David Stephen
Artificial intelligence still hallucinates a lot. Even chatbots that are said to lead most benchmarks mislead consumers, sometimes at stages where there is reluctance or haste to extra-verify information. There has been progress, using fine-tuning, against hallucinations, but what Google should be asking is this, seeing how hallucinations persist, how can Tensor Processing Units [TPUs] be the answer, so that it becomes embedded into new and existing AI training compute, anywhere on earth?
Sedona, AZ — Simply, if AI remains unreliable because of its mistakes, how can Google explore TPUs as answers that would seal permanence for Google, wherever AI is run and trained? To completely rival Nvidia, Google should look at the conceptual mechanism of human memory, to pursue a new memory architecture for TPUs. This means that even if artificial general intelligence [AGI] is not achieved yet, it can at least solve AI hallucinations, becoming the oxygen of accurate AI.
Human intelligence is completely reliant on human memory. Human intelligence is defined here as the use of human memory for expected, advantageous or desired outcomes. This means that memory is at destinations, the relays across relevant aspects of those destinations for outcomes is called intelligence.
Now, why do humans have better intelligence than other organisms, given that studies have shown similarities – with many – in navigating habitats? What makes it possible that human memory can bear complex languages, not those of other organisms?
To solve hallucinations, pursue theoretical mechanisms of how human memory works. The same applies to solving artificial general intelligence. When human memory breaks, it does so in [or with] recollection, maybe by forgetting or by misrepresentation. It is the architecture of memory that makes both possible.
This means that while it is necessary to remember people, things, events and so forth, human memory prioritizes for intelligence [needs or outcomes] in the instance. So, even with forgetting or misrepresentation, it does, with the pathway that it is supposed to be used for intelligence, not recollection. Hallucinations [or say confabulations] are therefore more common for humans with a mental disorder than not.
This indicates that human memory is a pointer to solving AI hallucinations – if a parallel digital memory can be built for training data. How so? By an extra layer of memory collection, with [next generation] TPUs, such that instead of having new training, existing training data can pass through the collection architecture of new iterations of TPUs, then have data heaved again into collections, to have hallucinations solved, while plowing on to AGI.
Simply, human memory does not store exact details of everything in the external world. Door is a collection for everything text, audio, image, video or motion about door, the same applies to table and so forth. So, having collections by the brain makes it easier for the transportation of intelligence to have fewer roads to travel for on-demand usefulness.
Assuming human memory stored every single thing separately, the mix up would be unbearable, since intelligence would have to find a blue door, or a red door or the audio of a door and so forth, to make instantaneous interpretations.
It is this mechanism that Google can pursue as research, for initial results within 4 months, including for AI safety.
Since the mechanism is set, the math framework, hardware design, algorithms and experiments can be accelerated fast. The mechanism is available from the postulate in Conceptual Biomarkers and Theoretical Biological Factors for Psychiatric and Intelligence Nosology.
This should be central mission for Sergey Brin, Koray Kavukcuoglu and the rest of the Gemini team, towards the future of AI overviews. How Google would not have another missed opportunity, after ChatGPT, especially in an area Google already has a head start.
Also, as Pixel 11 Pro Fold takes off, Google can seek writing, with a path towards an AI pen that can sell up to two billion units before August 31, 2028.
AI that can augment human abilities is the campaign of human-centered artificial intelligence [HCAI]. What if there is an AI pen that can reverse handedness or laterality for humanity, removing the dominance and precision of one hand, through enabling the other with an autonomous pen?
Google can get there before others. Even as Gemini 3.5 Pro is speculated to be expected before January, 2027.
The directions that hold commercial and technical dominance for Google are adjacent to TPUs and Pixel 11 Pro Fold.
There is a recent [August, 2026] blog by Google, Google’s most sophisticated foldable: Pixel 11 Pro Fold, stating that, “Introducing Pixel 11 Pro Fold, our latest foldable with a thinner and lighter design, brand-new main camera, added durability inside and out, and enhanced intelligence tools.”
“Pixel 11 Pro Fold is here, and it’s our most sophisticated foldable yet. With a fresh and durable design, stunning (and helpful) new camera visor with HiLight, and unique features that help you throughout your day, this phone is next-level.”
“Pixel 11 Pro Fold is almost 10% lighter and about 1mm thinner than Pixel 10 Pro Fold, so it’s more comfortable to hold in your hand and in your pocket. 1 With even thinner bezels than before for a sleek look, the new Pro Fold comes in our classic Obsidian and brand-new Olive for a pop of color.”
