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Sam Altman Reveals This Prior Flaw In OpenAI Advanced AI o1 During ChatGPT Pro Announcement But Nobody Seemed To Widely Notice
A hidden flaw or inconvenience in OpenAI o1 got recently aired and though fixed it raises … [+]
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In today’s column, I examine a hidden flaw in OpenAI’s advanced o1 AI model that Sam Altman revealed during the recent “12 Days Of OpenAI” video-streamed ChatGPT Pro announcement. His acknowledgment of the flaw was not especially noted in the media since he covered it quite nonchalantly in a subtle hand-waving fashion and claimed too that it was now fixed. Whether the flaw or some contend “inconvenience” was even worthy of consideration is another intriguing facet that gives pause for thought about the current state of AI and how far or close we are to the attainment of artificial general intelligence (AGI).
Let’s talk about it.
This analysis of an innovative proposition is part of my ongoing Forbes column coverage on the latest in AI including identifying and explaining various impactful AI complexities (see the link here). For my analysis of the key features and vital advancements in the OpenAI o1 AI model, see the link here and the link here, covering various aspects such as chain-of-thought reasoning, reinforcement learning, and the like.
How Humans Respond To Fellow Humans
Before I delve into the meat and potatoes of the matter, a brief foundational-setting treatise might be in order.
When you converse with a fellow human, you normally expect them to timely respond as based on the nature of the conversation. For example, if you say “hello” to someone, the odds are that you expect them to respond rather quickly with a dutiful reply such as hello, hey, howdy, etc. There shouldn’t be much of a delay in such a perfunctory response. It’s a no-brainer, as they say.
On the other hand, if you ask someone to explain the meaning of life, the odds are that any seriously studious response will start after the person has ostensibly put their thoughts into order. They would presumably give in-depth consideration to the nature of human existence, including our place in the universe, and otherwise assemble a well-thought-out answer. This assumes that the question was asked in all seriousness and that the respondent is aiming to reply in all seriousness.
The gist is that the time to respond will tend to depend on the proffered remark or question.
A presented simple comment or remark involving no weighty question or arduous heaviness ought to get a fast response. The responding person doesn’t need to engage in much mental exertion in such instances. You get a near-immediate response. If the presented utterance has more substance to it, we will reasonably allow time for the other person to undertake a judicious reflective moment. A delay in responding is perfectly fine and fully expected in that case.
That is the usual cadence of human-to-human discourse.
Off-Cadence Timing Of Advanced o1 AI
For those that had perchance made use of the OpenAI o1 AI advanced model, you might have noticed something that was outside of the cadence that I just mentioned. The human-to-AI cadence bordered on being curious and possibly annoying.
The deal was this.
You were suitably forewarned when using o1 that to get the more in-depth answers there would be more extended time after entering a prompt and before getting a response from the AI. Wait time went up. This has to do with the internally added capabilities of advanced AI functionality including chain-of-thought reasoning, reinforcement learning, and so on, see my explanation at the link here. The response latency time had significantly increased.
Whereas in earlier and less advanced generative AI and LLMs we had all gotten used to near instantaneous responses, by and large, there was a willingness to wait longer to get more deeply mined responses via advanced o1 AI. That seems like a fair tradeoff. People will wait longer if they can get better answers. They won’t wait longer if the answers aren’t going to be better than when the response time was quicker.
You can think of this speed-of-response as akin to playing chess. The opening move of a chess game is usually like a flash. Each side quickly makes their initial move and countermove. Later in the game, the time to respond is bound to slow down as each player puts concentrated thoughts into the matter. Just about everyone experiences that expected cadence when playing chess.
What was o1 doing in terms of cadence?
Aha, you might have noticed that when you gave o1 a simple prompt, including even merely saying hello, the AI took about as much time to respond as when answering an extremely complex question. In other words, the response time was roughly the same for the simplest of prompts and the most complicated and deep-diving fully answered responses.
It was a puzzling phenomenon and didn’t conform to any reasonable human-to-AI experience expected cadence.
In coarser language, that dog don’t hunt.
Examples Of What This Cadence Was Like
As an illustrative scenario, consider two prompts, one that ought to be quickly responded to and the other that fairly we would allow more time to see a reply.
First, a simple prompt that ought to lead to a simple and quick response.
- My entered prompt: “Hi.”
- Generative AI response: “Hello, how can I help you?”
The time between the prompt and the response was about 10 seconds.
Next, I’ll try a beefy prompt.
- My entered prompt: “Tell me how all of existence first began, covering all known theories.”
- Generative AI response: “Here is a summary of all available theories on the topic…”
The time for the AI to generate a response to that beefier question was about 12 seconds.
I think we can agree that the first and extremely simple prompt should have had a response time of just a few seconds at most. The response time shouldn’t be nearly the same as when responding to the question about all of human existence. Yet, it was.
Something is clearly amiss.
But you probably wouldn’t have complained since the aspect that you could get in-depth answers was worth the irritating and eyebrow-raising length of wait time for the simpler prompts. I dare say most users just shrugged their shoulders and figured it was somehow supposed to work that way.
Sam Altman Mentioned That This Has Been Fixed
During the ChatGPT Pro announcement, Sam Altman brought up the somewhat sticky matter and noted that the issue had been fixed. Thus, you presumably should henceforth expect a fast response time to simple prompts. And, as already reasonably expected, only prompts requiring greater intensity of computational effort ought to take up longer response times.
That’s how the world is supposed to work. The universe has been placed back into proper balance. Hooray, yet another problem solved.
Few seemed to catch onto his offhand commentary on the topic. Media coverage pretty much skipped past that portion and went straight to the more exciting pronouncements. The whole thing about the response times was likely perceived as a non-issue and not worthy of talking about.
Well, for reasons I’m about to unpack, I think it is worthy to ruminate on.
Turns out there is a lot more to this than perhaps meets the eye. It is a veritable gold mine of intertwining considerations about the nature of contemporary AI and the future of AI. That being said, I certainly don’t want to make a mountain out of a molehill, but nor should we let this opportune moment pass without closely inspecting the gold nuggets that were fortuitously revealed.
Go down the rabbit hole with me, if you please.
Possible Ways In Which This Happened
Let’s take a moment to examine various ways in which the off-balance cadence in the human-to-AI interaction might have arisen. OpenAI considers their AI to be proprietary and they don’t reveal the innermost secrets, ergo I’ll have to put on my AI-analysis detective hat and do some outside-the-box sleuthing.
First, the easiest way to explain things is that an AI maker might decide to hold back all responses until some timer says to release the response.
Why do this?
A rationalization is that the AI maker wants all responses to come out roughly on the same cadence. For example, even if a response has been computationally determined in say 2 seconds, the AI is instructed to keep the response at bay until the time reaches say 10 seconds.
I think you can see how this works out to a seemingly even cadence. A tough-to-answer query might require 12 entire seconds. The response wasn’t ready until after the timer was done. That’s fine. At that juncture, you show the user the response. Only when a response takes less than the time limit will the AI hold back the response.
In the end, the user would get used to seeing all responses arising at above 10 seconds and fall into a mental haze that no matter what happens, they will need to wait at least that long to see a response. Boom, the user is essentially being behaviorally trained to accept that responses will take that threshold of time. They don’t know they are being trained. Nothing tips them to this ruse.
Best of all, from the AI maker’s perspective, no one will get upset about timing since nothing ever happens sooner than the hidden limit anyway. Elegant and the users are never cognizant of the under-the-hood trickery.
The Gig Won’t Last And Questions Will Be Asked
The danger for the AI maker comes to the fore when software sophisticates start to question the delays. Any proficient software developer or AI specialist would right away be suspicious that the simplest of entries is causing lengthy latency. It’s not a good look. Insiders begin to ask what’s up with that.
If a fake time limit is being used, that’s often frowned upon by insiders who would shame those developers undertaking such an unseemly route. There isn’t anything wrong per se. It is more of a considered low-brow or discreditable act. Just not part of the virtuous coding sense of ethos.
I am going to cross out that culprit and move toward a presumably more likely suspect.
It goes like this.
I refer to this other possibility as the gauntlet walk.
A brief tale will suffice as illumination. Imagine that you went to the DMV to get up-to-date license tags for your car. In theory, if all the paperwork is already done, all you need to do is show your ID and they will hand you the tags. Some modernized DMVs have an automated kiosk in the lobby that dispenses tags so that you can just scan your ID and viola, you instantly get your tags and walk right out the door. Happy face.
Sadly, some DMVs are not yet modernized. They treat all requests the same and make you wait as though you were there to have surgery done. You check in at one window. They tell you to wait over there. Your name is called, and you go to a pre-processing window. The agent then tells you to wait in a different spot until your name is once again called. At the next processing window, they do some of the paperwork but not all of it. On and on this goes.
The upshot is that no matter what your request consists of you are by-gosh going to walk the full gauntlet. Tough luck to you. Live with it.
A generative AI app or large language model (LLM) could be devised similarly. No matter what the prompt contains, an entire gauntlet of steps is going to occur. Everything must endure all the steps. Period, end of story.
In that case, you would typically have responses arriving outbound at roughly the same time. This could vary somewhat because the internal machinery such as the chain of thought mechanism is going to pass through the tokens without having to do nearly the same amount of computational work, see my explanation at the link here. Nonetheless, time is consumed even when the content is being merely shunted along.
That could account for the simplest of prompts taking much longer than we expect them to take.
How It Happens Is A Worthy Question
Your immediate thought might be why in the heck would a generative AI app or LLM be devised to treat all prompts as though they must walk the full gauntlet. This doesn’t seem to pass the smell test. It would seem obvious that a fast path like at Disneyland should be available for prompts that don’t need the whole kit-and-kaboodle.
Well, I suppose you could say the same about the DMV. Here’s what I mean. Most DMVs were probably set up without much concern toward allowing multiple paths. The overall design takes a lot more contemplation and building time to provide sensibly shaped forked paths. If you are in a rush to get a DMV underway, you come up with a single path that covers all the bases. Therefore, everyone is covered. Making everyone wait the same is okay because at least you know that nothing will get lost along the way.
Sure, people coming in the door who have trivial or simple requests will need to wait as long as those with the most complicated of requests, but that’s not something you need to worry about upfront. Later, if people start carping about the lack of speediness, okay, you then try to rejigger the process to allow for multiple paths.
The same might be said for when trying to get advanced AI out the door. You are likely more interested in making sure that the byzantine and innovative advanced capabilities work properly, versus whether some prompts ought to get the greased skids.
A twist to that is the idea that you are probably more worried about maximum latencies than you would be about minimums. This stands to reason. Your effort to optimize is going to focus on trying to keep the AI from running endlessly to generate a response. People will only wait so long to get a response, even for highly complex prompts. Put your elbow grease toward the upper bounds versus the lower bounds.
The Tough Call On Categorizing Prompts
An equally tough consideration is exactly how you determine which prompts are suitably deserving of quick responses.
Well, maybe you just count the number of words in the prompt.
A prompt with just one word would seem unlikely to be worthy of the full gauntlet. Let it pass through or maybe skip some steps. This though doesn’t quite bear out. A prompt with a handful of words might be easy-peasy, while another prompt with the same number of words might be a doozy. Keep in mind that prompts consist of everyday natural language, which is semantically ambiguous, and you can open a can of worms with just a scant number of words.
This is not like sorting apples or widgets.
All in all, a prudent categorization in this context cannot do something blindly such as purely relying on the number of words. The meaning of the prompt comes into the big picture. A five-word prompt that requires little computational analysis is likely only discerned as a small chore by determining what the prompt is all about.
Note that this means you indubitably have to do some amount of initial processing to gauge what the prompt constitutes. Once you’ve got that first blush done, you can have the AI flow the prompt through the other elements with a kind of flag that indicates this is a fly-by-night request, i.e., work on it quickly and move it along.
You could also establish a separate line of machinery for the short ones, but that’s probably more costly and not something you can concoct overnight. DMVs often kept the same arrangement inside the customer-facing processing center and merely adjusted by allowing the skipping of windows. Eventually, newer avenues were developed such as the use of automated kiosks.
Time will tell in the case of AI.
There is a wide variety of highly technical techniques underlying prompt-assessment and routing issues, which I will be covering in detail in later postings so keep your eyes peeled. Some of the techniques are:
- (1) Prompt classification and routing
- (2) Multi-tier model architecture
- (3) Dynamic attention mechanisms
- (4) Adaptive token processing
- (5) Caching and pre-built responses
- (6) Heuristic cutoffs for contextual expansion
- (7) Model layer pruning on demand
I realize that seems relatively arcane. Admittedly, it’s one of those inside baseball topics that only heads-down AI researchers and developers are likely to care about. It is a decidedly niche aspect of generative AI and LLMs. In the same breath, we can likely agree that it is an important arena since people aren’t likely to use models that make them wait for simple prompts.
AI makers that seek widespread adoption of their AI wares need to give due consideration to the gauntlet walk problem.
Put On Your Thinking Cap And Get To Work
A few final thoughts before finishing up.
The prompt-assessment task is crucial in an additional fashion. The AI could inadvertently arrive at false positives and false negatives. Here’s what that foretells. Suppose the AI assesses that a prompt is simple and opts to therefore avoid full processing, but then the reality is that the answer produced is insufficient and the AI misclassified the prompt.
Oops, a user gets a shallow answer.
They are irked.
The other side of the coin is not pretty either. Suppose the AI assesses that a prompt should get the full treatment, shampoo and conditioner included, but essentially wastes time and computational resources such that the prompt should have been categorized as simple. Oops, the user waited longer than they should have, plus they paid for computational resources they needn’t have consumed.
Awkward.
Overall, prompt-assessment must strive for the Goldilocks principle. Do not be too cold or too hot. Aim to avoid false positives and false negatives. It is a dicey dilemma and well worth a lot more AI research and development.
My final comment is about the implications associated with striving for artificial general intelligence (AGI). AGI is considered the aspirational goal of all those pursuing advances in AI. The belief is that with hard work we can get AI to be on par with human intelligence, see my in-depth analysis of this at the link here.
How do the prompt-assessment issue and the vaunted gauntlet walk relate to AGI?
Get yourself ready for a mind-bending reason.
AGI Ought To Know Better
Efforts to get modern-day AI to respond appropriately such that simple prompts get quick response times while hefty prompts take time to produce are currently being devised by humans. AI researchers and developers go into the code and make changes. They design and redesign the processing gauntlet. And so on.
It seems that any AGI worth its salt would be able to figure this out on its own.
Do you see what I mean?
An AGI would presumably gauge that there is no need to put a lot of computational mulling toward simple prompts. Most humans would do the same. Humans interacting with fellow humans would discern that waiting a long time to respond is going to be perceived as an unusual cadence when in discourse covering simple matters. Humans would undoubtedly self-adjust, assuming they have the mental capacity to do so.
In short, if we are just a stone’s throw away from attaining AGI, why can’t AI figure this out on its own? The lack of AI being able to self-adjust and self-reflect is perhaps a telltale sign. The said-to-be sign is that our current era of AI is not on the precipice of becoming AGI.
Boom, drop the mic.
Get yourself a glass of fine wine and find a quiet place to reflect on that contentious contention. When digging into it, you’ll need to decide if it is a simple prompt or a hard one, and judge how fast you think you can respond to it. Yes, indeed, humans are generally good at that kind of mental gymnastics.
¡Prepárate para montar la ola de suerte y expansión!
El 6 de abril, mientras transmite el signo audaz y ardiente de Aries, el Sol se reunirá con Júpiter en Géminis en un afortunado sextil, creando una oportunidad emocionante para el crecimiento, la prosperidad y los nuevos y audaces comienzos.
Si ha estado ansiando algo nuevo y emocionante, este tránsito podría ser la luz verde que ha estado esperando. Es el momento perfecto para arriesgarse, expandir sus horizontes y celebrar hitos.
El Sol en Aries tiene que ver con la acción, el coraje y el liderazgo. Como el primer signo en el zodiaco, Aries encarna una chispa de iniciación, al igual que su gobernante planetario, Marte. Entonces, con el sol viajando a través de este intrépido signo de fuego, es hora de avanzar y adoptar desafíos con confianza y coraje.
Júpiter, por otro lado, es el planeta de abundancia, optimismo y expansión. En el signo cerebral de Géminis (curiosidad, comunicación y adaptabilidad, Júpiter abre un mundo de posibilidades. Amplifica la necesidad de exploración intelectual, nuevas ideas y conexiones sociales.
Este tránsito nos invita a ampliar nuestras perspectivas, pensar fuera de la caja y sumergirnos en nuevas aventuras que expanden nuestras vidas personales y profesionales.
El sextil del sol a Júpiter el 6 de abril es una poderosa combinación de pasión ardiente y expansión intelectual. Es un momento en que las oportunidades de crecimiento y exploración se sienten abundantes, y el cosmos recompensa a aquellos que están dispuestos a correr riesgos y adoptar el cambio.
Ya sea que esté comenzando un nuevo proyecto, tomando una gran decisión o sentirse inspirado para hacer algo nuevo, este tránsito ofrece el potencial de prosperidad y abundancia.
Siga leyendo para lo que esto significa para su signo del zodiaco.
Aries (del 20 de marzo al 19 de abril)
¡Eres la estrella del espectáculo, Aries! Además de que es su temporada de regreso solar, con el sol encendido por su primera casa, está uniendo fuerzas con Lucky Júpiter … y bueno, ¡eres imparable! Este es el momento perfecto para lanzar un proyecto personal o renovar su imagen. Considere actualizar su marca o presencia en las redes sociales para que coincida con su energía. Este tránsito se trata de ti, poseerlo y brillar.
Tauro (del 19 de abril del 20 de mayo)
Este es un momento para la reflexión y el crecimiento espiritual, Tauro. Aries gobierna su introspectiva casa 12 de patrones subconscientes y la iluminación de Júpiter en su segunda casa de finanzas y valores, lo que le brinda la claridad de liberarse de los viejos patrones mentales que ya no le sirven. Tal vez es hora de dejar de lado esos sistemas de creencias limitantes en torno al dinero o su autoestima. Confíe en que las nuevas y prósperas oportunidades esperan una vez que lo haga.
Géminis (del 20 de mayo al 20 de junio)
¡Es tu día de suerte, Géminis! A medida que el Sol energiza a su 11ª Casa de Asuntos Comunitarios, Júpiter aporta expansión y oportunidad a su letrero (¡y en la puerta de entrada!), Lo que lo convierte en un excelente momento para conectarse con personas influyentes y aquellos que comparten objetivos y sueños similares. Una oportunidad de establecer contactos podría llegar en su camino, o podría unirse inesperadamente a un grupo que se alinee perfectamente con sus valores.
Cáncer (del 20 de junio al 22 de julio)
Alcance las estrellas: su carrera está bajo el centro de atención, el cáncer. A medida que el Sol energiza y revitaliza su décima casa de autoridad pública, Lucky Júpiter lo hace más receptivo y sintonizado con su crecimiento personal y profesional. Esto no solo ofrece ideas espirituales, sino que también te empuja a hacer movimientos audaces en tu vida profesional. El éxito está en el horizonte.
Leo (22 de julio al 22 de agosto)
¡La aventura te espera, Leo! El sol está gobernado por el sol, y mientras enciende su novena casa de expansión filosófica, unirá fuerzas con Lucky Júpiter en su 11ª Casa de Asociaciones, Asuntos Comunitarios y visiones futuras. Ya sea que se trate de un viaje de último minuto, una clase en la que se está inscribiendo o un pasatiempo nuevo que está explorando, su mente y su corazón están abiertos a nuevas experiencias. Carpe Diem.
Virgo (22 de agosto al 22 de septiembre)
Este es un gran problema: confía en que la transformación que está experimentando es para su más alto bien, Virgo. Con el sol sacudiendo su octava casa de intimidad y recursos compartidos, se reunirá con Lucky Júpiter en su décima casa de carrera y reputación pública. Una ganancia inesperada financiera o un profundo avance emocional podría estar en camino, ayudándole a entrar en su poder personal. Estar abierto a lo inesperado.
Libra (del 22 de septiembre al 22 de octubre)
Sus asociaciones y acuerdos contractuales están bajo el foco de este tránsito, Libra. A medida que el sol energiza su sector de relaciones, se reunirá con Audacy Júpiter en un sextil armonioso. Ya sea amor, negocios o amistades, una nueva conexión podría sentirse destinada. Es un buen momento para trabajar con otros en empresas conjuntas o colaboraciones que contribuyen a su crecimiento personal y profesional.
Scorpio (22 de octubre a Nov. 21)
Sus hábitos de salud pueden mejorar drásticamente bajo esta sinergia empoderadora, Scorpio. Si bien el trabajo y los asuntos de salud están a la vanguardia, el sextil de Sun a Júpiter, activando su sexta casa de bienestar y octava casa de empresas conjuntas, podría inspirarlo con la confianza y la energía que necesita para asumir nuevos desafíos. Tal vez es hora de sacudir su rutina o asumir un nuevo objetivo de salud. Una nueva oportunidad de trabajo o reconocimiento podría llegar a su manera, haciendo que sus esfuerzos se sientan más gratificantes.
Sagitario (22 de noviembre al déco de 21)
Sus jugos creativos fluyen, y eso es un eufemismo, Sagitario. Después de todo, no es todos los días que su gobernante planetario de la suerte, Júpiter, une fuerzas con el sol en su quinta casa de amor, pasión y autoexpresión. Aplastar por alguien especial? Ya sea que esté trabajando en un proyecto de pasión, atrapando sentimientos románticos o entrando en el centro de atención con un esfuerzo creativo, su aura está radiante y está listo para brillar. El amor también podría ser espontáneo y emocionante.
Capricornio (del 21 de diciembre al 19 de enero)
El hogar es donde está tu corazón, entonces, ¿por qué no darle el amor que merece, Capricornio? Con el sol que abarca su cuarta casa doméstica del hogar y la familia, mientras que en el flujo de energía armonioso con Júpiter en su sexta casa de mejora, logística y responsabilidad, puede sentirse llamado a mejorar su espacio vital o crear más espacio para sus pertenencias. Tal vez es hora de redecorar, mudarse a un nuevo espacio o incluso fortalecer los lazos familiares.
Acuario (19 de enero de 18 años)
Usa tus palabras sabiamente: son más poderosos de lo que te das cuenta, Acuario. Con el sol energizando su curiosa tercera casa de comunicación, sus pensamientos e intercambios serán clave durante este tiempo. Aún así, a medida que el Sol se armoniza con Júpiter en su quinta casa de fertilidad y expresión creativa, eres igualmente inspirador y seguro en tu enfoque. Es posible que tenga una conversación esclarecedora que lleva su relación al siguiente nivel o provoca una nueva idea para un proyecto.
Piscis (del 18 de febrero al 20 de marzo)
¡El dinero fluye y la abundancia está llamando, Piscis! A medida que el sol energiza su segunda casa de comodidad, finanzas y valores que busca la estabilidad, se reunirá con Júpiter en su cuarta casa del hogar, la familia y los lazos emocionales. ¿Listo para hacer esa inversión en su espacio vital? Otros podían sentirse llamados para gastar un poco de efectivo extra en una excursión familiar. Confía en tu intuición: la prosperidad podría venir de manera sorprendente.
A finales de 2023, escuchamos que Google estaba trabajando en un nuevo asistente digital que evidentemente se planeó debutar en el Pixel 9 en 2024. Evidentemente llamado Pixie, el asistente habría manejado tareas de IA en el dispositivo usando un modelo de Gemini Nano. Pero la IA nunca se materializó. Entonces, ¿qué pasó con Pixie, y lo volveremos a ver?
Bienvenido al compiladorsu resumen semanal de Goings-On. Paso mis días mientras el editor de Google leyendo y escribiendo sobre lo que Google está haciendo a través de Android, Pixel, Gemini y más, y hablo de todo aquí en esta columna. Esto es lo que ha estado en mi mente esta semana.
¿Qué era Pixie?
Según los informes de la información, Pixie estaba destinado a ser un asistente de IA en el dispositivo exclusivo de los teléfonos de Google Pixel, concebido antes de que el chatbot Gemini llegara a la escena (todavía era Google Bard en aquel entonces). Usando un modelo local de Géminis Nano, la IA habría extraído datos de las aplicaciones de Google en su teléfono para ofrecer asistencia más personalizada. La información dijo en 2023 que Pixie podría haber evolucionado “a una versión mucho más personalizada del Asistente de Google”.
Aparentemente, Pixie estaba planeado para asumir las tareas del asistente del Asistente de Google en los teléfonos de Google. Obviamente, eso nunca sucedió, aunque Google ha anunciado oficialmente sus planes para eliminar gradualmente al Asistente Legacy a favor de Gemini en el futuro cercano.
Relacionado
Géminis está reemplazando oficialmente al Asistente de Google, esto es lo que eso significa para ti
Google Assistant se está jubilando este año
¿Qué pasó con Pixie?
La serie Pixel 9 aterrizó el año pasado sin mencionar el asistente de Pixie que se rumoreaba el año anterior. El informe posterior de la información (llamado a nuestra atención por 9to5Google) arroja algo de luz sobre lo que pudo haberle sucedido a Pixie.
Según los informes, el CEO de Google, Sundar Pichai, ordenó personalmente un cambio en la estrategia con Pixie para evitar la competencia con el asistente de IA prioritario de Google, Gemini. La marca Pixie parece estar bien y verdaderamente retirada, pero algunas características de Pixie terminaron llegando al Pixel 9. Capturas de pantalla de Pixel, una característica de IA local impulsada por el modelo Gemini Nano XS, evidentemente comenzó su vida como funcionalidad de Pixie. 9to5Google también ha informado que la extensión de servicios públicos de Gemini que permite a la IA controlar directamente la configuración del dispositivo, la reproducción de medios, las alarmas y más, al mismo tiempo, se planeó ser parte de la experiencia de Pixie.
La información ha informado que estas características aparentemente salieron de Pixie no hacen una experiencia tan similar a lo que Pixie habría sido. Sin embargo, es posible que veamos más de lo que Pixie podría haber sido en el futuro.
Relacionado
La pequeña aplicación de capturas de pantalla de Pixel de Google finalmente me convenció de que AI podría ser un gran problema
Las capturas de pantalla de Pixel pueden ser la forma más clara de Google de demostrar que la IA es útil
¿Podría Pixie regresar?
Con Gemini tan claramente una prioridad para Google, parece poco probable que veamos el lanzamiento de un asistente de inteligencia artificial exclusivo de píxeles que se superpone con el conjunto de funciones de Gemini; de hecho, con Gemini listo para reemplazar el Asistente de Google en la mayoría de los dispositivos en el futuro cercano, Google parece que va en la dirección opuesta. Pero un informe de marzo de Android Authority que hace referencia a “Una fuente dentro de Google” dice que más funcionalidad de Pixie llegará a la serie Pixel 10 en forma de una nueva aplicación llamada Pixel Sense.
Parece que Pixel Sense no competirá directamente con Gemini, sino que intentará proporcionar sugerencias “predictivas” basadas en el contexto de sus aplicaciones de Google conectadas, incluidos Calendar, Chrome, Gmail, Keep, Maps y más. Pixel Sense aparentemente también podrá organizar sus capturas de pantalla en un archivo de búsqueda como lo hace la aplicación de capturas de pantalla Pixel actual, insinuando que puede reemplazar las capturas de pantalla por completo.
Pixel Sense funcionará completamente en el dispositivo; Android Authority cita una fuente diciendo que “sus datos permanecen privados, visibles solo para usted, ni siquiera Google puede verlo”.
Los informes de AA no pintan una imagen completa de cómo funcionará realmente la aplicación Pixel Sense, pero como se describe, parece que podría funcionar de manera similar a la de Google Now, o la función S25 similar de Samsung, ahora breve. Esas características también tienen como objetivo proporcionar información según lo necesite, informado por el contexto de sus cuentas conectadas. Con el acceso a lo que parece esencialmente toda la información almacenada en su cuenta de Google y alimentada por Gemini Nano, Pixel Sense podría hacer un mejor trabajo para ofrecer actualizaciones útiles a medida que las necesita.
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Todavía bastante agradable, aunque
Géminis es tu asistente de IA de Android para lo previsible
El asistente de Pixie probablemente nunca verá la luz del día en su forma inicialmente planificada; Google está poniendo todos sus huevos de asistente en la canasta de Géminis. Pero parece que obtendremos más de la funcionalidad de Pixie en la serie Pixel 10 en forma de la nueva aplicación Pixel Sense. En cuanto a qué es exactamente el sentido de Pixel, tendremos que esperar y ver. Es posible que escuchemos más en Google I/O, que comienza el próximo mes.
After correctly predicting Denny Hamlin to win his first race of the NASCAR Cup Series season, we’re turning to AI to help us predict the winner and full finishing order for the Goodyear 400 at Darlington Raceway today at 3 p.m. ET (FS1).
We asked ChatGPT for its NASCAR at Darlington predictions based on historical data, betting odds, and statistical trends – including its pick to win, best prop bet, and favorite long shot, as well as the results for every driver for today’s 38-car field.
Along with our 2025 Goodyear 400 predictions at Darlington, here are our AI-powered NASCAR best bets and full AI-simulated finishing order:
NASCAR AI picks & predictions for Goodyear 400 at Darlington
We’ve previously used ChatGPT to predict its March Madness bracket, Super Bowl picks, and even its Canada vs. USA predictions, and we’re once again turning to OpenAI’s popular chatbot to predict the winner of today’s Goodyear 400 at Darlington.
We trained ChatGPT’s latest and most advanced AI model to study the latest NASCAR odds, betting history, and relevant trends before predicting this weekend’s winner:
ChatGPT’s pick to win Goodyear 400 at Darlington
ChatGPT predicts Denny Hamlin will win the Goodyear 400 at Darlington. His best odds are +800 via BetMGM, which would turn a winning $10 bet into an $80 profit with an implied win probability of 11.11%.
Here’s why the AI model is predicting Hamlin will win today’s race:
Short-track savvy: Hamlin’s long history on short, punishing tracks gives him the experience needed to navigate Darlington’s notorious “Track Too Tough to Tame,” avoiding the mishaps and attrition that often plague less experienced drivers.
Team excellence & strategy: Joe Gibbs Racing consistently fields strong cars at Darlington, and Hamlin’s ability to manage cautions, fuel strategy, and late-race restarts is a big advantage on this challenging circuit.
Proven consistency: Despite the inherent chaos of Darlington, Hamlin’s track record as a veteran who can execute under pressure makes him a standout in a field where even the favorites have slim win probabilities.
AI confidence level: 
(15% win probability)
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ChatGPT’s best prop bet for Goodyear 400 at Darlington
ChatGPT predicts Chase Briscoe will finish in the top three as its best NASCAR prop bet for Darlington. Briscoe’s best odds to score a top-3 finish are +550 via bet365, which would turn a winning $10 bet into a $55 profit with an implied win probability of 15.38%.
Here’s why the AI model is predicting Briscoe to finish in the top three today:
Strong starting position & package: Briscoe qualified 4th with Joe Gibbs Racing – a team known for building competitive cars at Darlington – and his starting position puts him right in the mix.
Experience on tough tracks: JGR drivers have a good track record at Darlington, and Briscoe’s aggressive yet measured driving style makes him well-suited to navigate Darlington’s unique challenges.
Ability to capitalize on caution-fueled restarts: Darlington is notorious for its unpredictable cautions and restarts. If the leaders are caught up in strategy or on-track incidents, Briscoe’s aptitude for seizing opportunities could see him climbing into the top three.
AI confidence level: (25% chance of top-3 finish)
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ChatGPT’s best long shot to win Goodyear 400 at Darlington
ChatGPT predicts Alex Bowman to win the Goodyear 400 as its best long shot at Darlington. His best odds to win are +5000 via Caesars, which would turn a winning $10 bet into a $500 profit with an implied win probability of 1.96%.
Here’s why the AI model is predicting Bowman as its best long shot today:
Undervalued starting position vs. team quality: Although Bowman qualified 33rd, his Hendrick Motorsports car is typically one of the best on the circuit. A deep starting position can mask his true race pace if his team brings a strong setup for Darlington.
Opportunity in Darlington’s chaos: Darlington’s reputation for attrition, unpredictable cautions, and strategic gambles means that drivers starting deep can climb the order dramatically if they avoid early incidents.
Proven ability to overperform: Bowman has shown in past races that he can make up significant ground when conditions favor his driving style. His experience and skill could allow him to exploit a chaotic race and deliver a surprise win.
AI confidence level: (4% win probability)
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Goodyear 400 AI projections for full finishing order
Here is our predicted Goodyear 400 finishing order and results for every driver based on AI projections. While ChatGPT is a large language model and not specifically designed for predicting sporting events, it can spot trends or insights that NASCAR bettors might overlook.
ChatGPT’s full NASCAR finishing order predictions at Darlington
1. Denny Hamlin 
(No. 11, Joe Gibbs Racing Toyota) – Using his veteran short‐track savvy and ability to manage fuel and cautions, Hamlin pulls away to take the win.
2. William Byron 
(No. 24, Hendrick Motorsports Chevrolet) – Byron’s strong car and clean air from starting first help him finish near the front.
3. Chase Briscoe 
(No. 19, Joe Gibbs Racing Toyota) – A well-handled race by Briscoe capitalizing on strategic restarts secures him a podium finish.
4. Bubba Wallace (No. 23, 23XI Racing Toyota) – Wallace’s aggressive style suits Darlington’s unpredictable nature, keeping him in the top four.
5. Kyle Busch (No. 8, Richard Childress Racing Chevrolet) – Busch’s experience and ability to avoid trouble make him a steady presence in the top five.
6. Joey Logano (No. 22, Team Penske Ford) – Logano overcomes a deeper starting position with a series of strong restarts to climb into the top six.
7. Chase Elliott (No. 9, Hendrick Motorsports Chevrolet) – Despite a mid-pack start, Elliott’s racecraft and Hendrick’s setup allow him to finish strongly.
8. Tyler Reddick (No. 45, 23XI Racing Toyota) – Reddick’s speed and determination help him navigate the short-track mayhem for a top-10 finish.
9. Austin Cindric (No. 2, Team Penske Ford) – Cindric’s consistency and a smart pit strategy keep him in contention among the leaders.
10. Christopher Bell (No. 20, Joe Gibbs Racing Toyota) – Bell capitalizes on clean air and a well-timed move to round out the top 10.
11. Ryan Blaney (No. 12, Team Penske Ford) – Blaney stays in the mix and finishes solidly in the upper group.
12. Kyle Larson (No. 5, Hendrick Motorsports Chevrolet) – Larson’s talent sees him fighting through traffic for a top-12 finish.
13. Todd Gilliland (No. 34, Front Row Motorsports Ford) – In the midst of the short-track chaos, Gilliland manages to keep a respectable position.
14. Ryan Preece (No. 60, RFK Racing Ford) – Preece, starting near the front, is jostled around early and slips to 14th.
15. Michael McDowell (No. 71, Spire Motorsports Chevrolet) – McDowell’s early speed is tempered by the attrition typical of Darlington, landing him mid-pack.
16. Ty Gibbs (No. 54, Joe Gibbs Racing Toyota) – The young gun shows promise but finishes behind the veterans as the race unfolds.
17. Carson Hocevar (No. 77, Spire Motorsports Chevrolet) – Hocevar’s package keeps him in contention, but he ultimately settles in the lower mid-field.
18. Chris Buescher (No. 17, RFK Racing Ford) – Buescher’s RFK setup allows him to cruise steadily, finishing just outside the top 15.
19. Justin Haley (No. 7, Spire Motorsports Chevrolet) – Haley makes a late charge but is held back by traffic, finishing in the upper mid-field.
20. Ross Chastain (No. 1, Trackhouse Racing Chevrolet) – Chastain’s bold moves see him climb significantly – but contact and cautions slow his progress, putting him 20th.
21. Austin Dillon (No. 3, Richard Childress Racing Chevrolet) – Dillon’s car struggles with the track’s relentless demands, dropping him into the lower mid-field.
22. Josh Berry (No. 21, Wood Brothers Racing Ford) – Berry’s knack for close-quarters racing keeps him around the 20th–22nd range.
23. Brad Keselowski (No. 6, RFK Racing Ford) – Keselowski battles through on-track incidents to finish in the low 20s.
24. Zane Smith (No. 38, Front Row Motorsports Ford) – Smith’s less competitive package and a few missteps push him slightly back.
25. A.J. Allmendinger (No. 16, Kaulig Racing Chevrolet) – Allmendinger’s aggressive style yields mixed results, and he ends up mid-pack.
26. Noah Gragson (No. 4, Front Row Motorsports Ford) – Gragson is caught in the frequent Darlington cautions, finishing in the mid-field.
27. John Hunter Nemechek (No. 42, Legacy Motor Club Toyota) – Nemechek’s strategic driving helps him inch forward, but he remains in the lower mid-field.
28. Ricky Stenhouse Jr. (No. 47, Hyak Motorsports Chevrolet) – Stenhouse Jr. is involved in a couple of incidents, dropping him further back.
29. Ty Dillon (No. 10, Kaulig Racing Chevrolet) – With a modest package, Ty Dillon finishes in the latter part of the field.
30. Daniel Suarez (No. 99, Trackhouse Racing Chevrolet) – Suarez’s volatile style sees him struggle with consistency, landing him near the back.
31. Cole Custer (No. 41, Haas Factory Team Ford) – Custer’s car isn’t well-suited for Darlington, resulting in a lower-field finish.
32. Riley Herbst (No. 35, 23XI Racing Toyota) – Inexperience and a lack of track finesse see Herbst fade in the latter half.
33. Alex Bowman (No. 48, Hendrick Motorsports Chevrolet) – Bowman’s pace drops off amid the chaos, and he falls toward the back.
34. Erik Jones (No. 43, Legacy Motor Club Toyota) – Jones is hampered by on-track contact and ends up further down the order.
35. Cody Ware (No. 51, Rick Ware Racing Ford) – With one of the least competitive packages, Ware is forced into a deep back finish.
36. Shane van Gisbergen (No. 88, Trackhouse Racing Chevrolet) – The international star struggles to adapt to Darlington’s brutal demands and falls out of contention.
37. Austin Hill (No. 33, Richard Childress Racing Chevrolet) – Hill’s inexperience on this demanding track sends him near the rear.
38. J.J. Yeley (No. 44, NY Racing Team Chevrolet) – Yeley’s limited Cup experience sees him finish in the final stretch.
NASCAR best bets for Goodyear 400 at Darlington
| Bet | Driver | Odds | Implied win probability |
|---|---|---|---|
| Pick to win |
Denny Hamlin | +800 | 11.11% |
| Best prop bet |
Chase Briscoe (top-3) | +550 | 15.38% |
| Best long shot |
Alex Bowman | +5000 | 1.96% |
How to watch the 2025 Goodyear 400 at Darlington
Race date: Sunday, April 6
Start time: 3 p.m. ET
Track: Darlington Raceway (Darlington, S.C.) TV: FS1 | Streaming: Fox Sports App
Best NASCAR betting sites for Goodyear 400 at Darlington
Looking to bet on the Goodyear 400 at Darlington Raceway? Here are our top-rated NASCAR best sports betting sites as determined by our expert team at Sportsbook Review, along with our best sportsbook promos ahead of today’s race at 3 p.m. ET.
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