AI Trends for 2025

00:07:32
https://www.youtube.com/watch?v=5zuF4Ys1eAw

Zusammenfassung

TLDRVideoen diskuterer afgørende AI-trends i 2025. For det første fokuseres på Agental AI, hvor intelligent systemer kan planlægge og udføre handlinger. Dernæst behandles inference time compute, som justerer tiden en model tænker før svar. Der er også fokus på væksten i både meget store og små modeller, med opmærksomhed på parametre og brugsscenarier. Near infinite memory giver AI-komponenter muligheden for at huske kontinuerlig brugerkommunikation. Der lægges også vægt på forbedring af systemer, hvor mennesker og AI arbejder sammen mere effektivt end separat. Sidst opfordres publikum til at forudse 2025's trends, hvilket indikerer et interaktivt brugersamfund.

Mitbringsel

  • 🤖 Agental AI vil revolutionere komplekse problemstillinger gennem planlægning.
  • ⏱️ Inference time compute forbedrer AI's beslutningsprocesser ved justering af tænkningstid.
  • 💡 Meget store modeller vil have trillioner af parametre, men små modeller vil være mere effektive på mindre enheder.
  • 🛠️ Avancerede AI-brugsscenarier forventes at optimere IT og kundeservice.
  • 📚 Near infinite memory vil gøre chatbots mere kvalificerede til personlige interaktioner.
  • 👥 Forbedringer i menneskelig interaktionsaugumentering nødvendig for bedre AI-partnerskaber.
  • 📈 Efterspørgslen efter smartere AI-systemer vil stige med avancerede brugsscenarier.
  • 🧠 LLM vil have større kontekstvinduer, hvilket forbedrer forståelsen af kontinuerlig dialog.
  • 🔧 Enklere AI-værktøjer forventes for professionelle, der ikke er AI-eksperter.
  • 🤔 Publikum opfordres til at deltage i diskussionen om fremtidige AI-trends.

Zeitleiste

  • 00:00:00 - 00:07:32

    I denne video diskuterer taleren vigtige AI-tendenser for 2025, baseret på personlige gæt uden hemmelig information. For det første er genetisk AI, evnen for AI-agenter til at planlægge og handle, en tendens. Aktuelle modeller har problemer med kompleks logisk ræsonnering. For det andet, inference time compute introducerer variabel tid for AI at tænke før svar, hvilket forbedrer ræsonnering uden ny modellæring. Tredje tendens er meget store modeller med op til 50 billioner parametre, som kan forbedre præcision og funktioner. Fjerde tendens, meget små modeller kræver mindre computerkraft og kan køre på personlige enheder. Herefter omtales en udvikling i AI anvendelser inden for kundeservice og cybersikkerhed. En nærmest ubegrænset hukommelse for AI er også på horisonten, med kontekstvinduer der husker tidligere interaktioner. Menneskelig- og AI-samarbejde er kritisk, men udfordret, især når AI klarer sig bedre end mennesker i tests. Sidst opfordres seerne til at dele deres tanker om fremtidens AI-tendenser i kommentarerne.

Mind Map

Video-Fragen und Antworten

  • Hvad er Agental AI?

    Agental AI er intelligente systemer, der kan ræsonnere, planlægge og udføre handlinger ved at bryde komplekse problemer ned i multi-trins planer.

  • Hvordan fungerer inference time compute?

    Inference time compute giver modeller mulighed for at bruge variabel tid på at ræsonnere og tænke over en opgave baseret på dens kompleksitet, hvilket kan forbedres uden at ændre grundmodellens træning.

  • Hvad er forskellen mellem store og små AI-modeller?

    Store AI-modeller besidder mange parametre, ofte trillioner, mens små modeller har færre parametre og kan køre effektivt på mindre hardware som bærbare computere og telefoner.

  • Hvilke brugsscenarier forventes for AI i 2025?

    Avancerede brugsscenarier som kundeservice bots, IT-netværksoptimering og dynamiske sikkerhedsværktøjer forventes at blive mere fremtrædende.

  • Hvad betyder 'near infinite memory' i AI-sammenhæng?

    'Near infinite memory' refererer til chatbots med evnen til at huske og bruge al tidligere interaktion i deres beslutningstagning.

  • Hvad er menneskelig interaktionsaugumentering i AI?

    Det indebærer at integrere AI-værktøjer i arbejdsprocesser så professionel og AI kan komplementere og forbedre hinandens præstation.

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Untertitel
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Automatisches Blättern:
  • 00:00:00
    What will be the most important trends in AI in 2025?
  • 00:00:04
    Well, I'm going to share my own educated guesses.
  • 00:00:08
    I don't have any kind of top secret classified information or anything.
  • 00:00:11
    But also, this isn't my first rodeo.
  • 00:00:15
    I did take a shot at predicting AI trends for 2024 and well, I think I did alright.
  • 00:00:22
    Although a little confession about that video.
  • 00:00:25
    I waited until March of 2024 to shoot it.
  • 00:00:29
    So I already had like a quarter of the year to go on.
  • 00:00:33
    But that's not the case this time.
  • 00:00:35
    So let's get cracking with eight important AI trends in 2025.
  • 00:00:40
    Let's start with an obvious one.
  • 00:00:43
    Number one.
  • 00:00:44
    Agenetic AI. Every time we post the video about agents to this channel,
  • 00:00:51
    viewership spikes.
  • 00:00:52
    So there's clearly an appetite for understanding this tech.
  • 00:00:55
    So what are AI agents?
  • 00:00:59
    Well, the intelligence systems that can reason they can plan and they can take action.
  • 00:01:06
    An agent can break down complex problems to create multi step plans
  • 00:01:11
    and that can interact with tools and databases to achieve goals.
  • 00:01:16
    And I think most people are on board with the utility of a well-performing AI agent.
  • 00:01:21
    Trouble is, today's models, well, they struggle with consistent logical reasoning.
  • 00:01:28
    They can usually execute simple plans,
  • 00:01:30
    but when it comes to handling complex scenarios with multiple variables,
  • 00:01:34
    they have to lose track and they make decisions that don't quite add up.
  • 00:01:39
    So we'll need better models in 2025.
  • 00:01:44
    Speaking of which, trends number two is inference time compute.
  • 00:01:52
    Now, during inference and our model goes to work on real time data,
  • 00:01:56
    comparing the user's query with information processed during training and stored in its weights.
  • 00:02:01
    New AI models are extending inference processing to essentially spend some time
  • 00:02:07
    thinking before giving you an answer, and the amount of time it spends.
  • 00:02:12
    Thinking is variable based on how much reasoning it needs to do so.
  • 00:02:16
    A simple request that might take a second or two or something larger and harder might take several minutes.
  • 00:02:23
    And what makes inference time compute models interesting is the inference
  • 00:02:27
    reasoning is something that can be tuned and improved without having to train and tweak the underlying model.
  • 00:02:35
    So there are now two places in the development of an LLM where
  • 00:02:38
    reasoning can be improved at training time with better quality training data,
  • 00:02:43
    but now also inference time with better chain of thought training, which could ultimately lead to smarter AI agents.
  • 00:02:54
    All right.
  • 00:02:54
    Trend number three is very large models.
  • 00:03:01
    Large language models consist of many parameters which are refined over the training process.
  • 00:03:07
    Now, the frontier models in 2024.
  • 00:03:09
    They're in the range of like 1 to 2 trillion parameters in size.
  • 00:03:14
    The next generation of models are expected to be many times larger than that, perhaps upwards of 50 trillion parameters.
  • 00:03:22
    But if 2025 is the year of enormous models,
  • 00:03:26
    it may also be the year of number four, very small models, models that are only a few billion parameters in size.
  • 00:03:37
    And yet you don't hear the phrase only a few billion very often,
  • 00:03:41
    but there you go,
  • 00:03:42
    and these models, they don't need huge data centers loaded with stacks of GPUs to operate.
  • 00:03:49
    They can run on your laptop or even on your phone.
  • 00:03:52
    Actually, I have the 2 billion parameter IBM Granite three model running on my laptop,
  • 00:03:57
    and my device doesn't even have to break a sweat to run it.
  • 00:04:00
    So expect to see more models of this size tuned to complete specific tasks without requiring large compute overhead.
  • 00:04:09
    Now, do you know what the most common enterprise use cases were for AI in 2024?
  • 00:04:15
    Well, according to a Harris poll, it's improving customer experience,
  • 00:04:20
    IT operations and automation, virtual assistants and cyber security.
  • 00:04:27
    Looking ahead to 2025, we will see more advanced use cases.
  • 00:04:33
    So think customer service bots that can actually solve complex problems instead of just routing ticket.
  • 00:04:39
    So think about AI systems that can proactively optimize entire IT networks, or
  • 00:04:45
    think about security tools that can adapt to new threats in real time.
  • 00:04:50
    Now, when I first used generative AI back in the day to help me build a beer recipe,
  • 00:04:56
    the context window for the LLM was a mere 2000 tokens.
  • 00:05:02
    Today's models have context when those measured in the hundreds of thousands or even the millions of tokens.
  • 00:05:08
    We are getting close to number six near infinite memory
  • 00:05:15
    where bots can keep everything they know about us in memory at all times.
  • 00:05:20
    We'll soon be in the era of customer service chat bots that can recall
  • 00:05:24
    every conversation it has ever had with us, which hopefully we'll consider a good thing.
  • 00:05:32
    Okay. Trend number seven.
  • 00:05:34
    That is human in the loop augmentation.
  • 00:05:38
    Now, perhaps you heard about the study where a chat bot outperformed physicians in clinical reasoning.
  • 00:05:44
    So 50 doctors were asked to diagnose medical conditions from examining case reports.
  • 00:05:49
    A chat bot presented with the same cases actually scored higher than the doctors,
  • 00:05:55
    but where this gets really interesting is some doctors were randomly assigned to use a chat bot to help them in this study.
  • 00:06:03
    Now the doctor plus chat bot group also scored lower than when the chat bot was asked to solve the cases alone.
  • 00:06:10
    And that is a failing of AI and human augmentation.
  • 00:06:15
    An expert paired with an effective AI system should be smarter together
  • 00:06:20
    than either of those two entities operating by themselves.
  • 00:06:23
    But look, prompting LLM chat bots can be hard.
  • 00:06:27
    You got to tailor the right prompts.
  • 00:06:29
    You've got to ask for things in the right way.
  • 00:06:32
    So we need better systems that allow professionals to augment AI tools into their workflow
  • 00:06:38
    without those professionals needing to be experts in how to use AI.
  • 00:06:41
    So expect more to come in this area.
  • 00:06:45
    Now, the final trend in my 2024 Trends video, I actually turn this one over to the audience
  • 00:06:52
    asking which AI trend do you think will be important in the year ahead?
  • 00:06:56
    And I'm so glad I did.
  • 00:06:59
    Hundreds of viewers shared their thoughts.
  • 00:07:01
    Well, I know when I'm on to a good thing.
  • 00:07:03
    So trend number eight, that one is over to you.
  • 00:07:09
    What do you think will be an important AI trend in 2025?
  • 00:07:15
    Let me know in the comments.
Tags
  • AI-trends 2025
  • Agental AI
  • Inference compute
  • Store modeller
  • Små modeller
  • Near infinite memory
  • Menneskelig augmentering
  • AI-systemer