Artificial intelligence is rapidly changing many aspects of how we work and live. (How many stories did you read last week about self-driving cars and job-stealing robots? Perhaps your holiday shopping involved some AI algorithms, as well.) But despite the constant flow of news, many misconceptions about AI remain.
AI doesn’t think in our sense of the word at all, Scriffignano explains. “In many ways, it’s not really intelligence. It’s regressive.”
IT leaders should make deliberate choices about what AI can and can’t do on its own. “You have to pay attention to giving AI autonomy intentionally and not by accident,”
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Tomi Engdahl says:
Google built an AI tool that can do research for you / With Deep Research, you can ask Gemini to scour the web on your behalf and write up a report based on its findings.
https://www.theverge.com/2024/12/11/24318217/google-gemini-advanced-deep-research-launch
Tomi Engdahl says:
https://www.marktechpost.com/2024/12/05/google-ai-just-released-paligemma-2-a-new-family-of-open-weight-vision-language-models-3b-10b-and-28b/
Tomi Engdahl says:
Apple reportedly cancels M4 Extreme chip to bet big on AI instead — here’s what we know
https://www.tomsguide.com/computing/hardware/apple-m4-extreme-chip-cancelled-report
Tomi Engdahl says:
Google unveils AI coding assistant ‘Jules,’ promising autonomous bug fixes and faster development cycles
https://venturebeat.com/ai/google-unveils-ai-coding-assistant-jules-promising-autonomous-bug-fixes-and-faster-development-cycles/
Google unveiled “Jules” on Wednesday, an artificial intelligence coding assistant that can autonomously fix software bugs and prepare code changes while developers sleep, marking a significant advancement in the company’s push to automate core programming tasks.
The experimental AI-powered code agent, built on Google’s newly announced Gemini 2.0 platform, integrates directly with GitHub’s workflow system and can analyze complex codebases, implement fixes across multiple files, and prepare detailed pull requests without constant human supervision.
Tomi Engdahl says:
Surveying the LLM application framework landscape
https://www.infoworld.com/article/3617664/surveying-the-llm-application-framework-landscape.html
LangChain, LlamaIndex, Semantic Kernel, and Haystack all help you to construct retrieval-augmented generation and other AI-enabled apps using your favorite large language models.
Large language models by themselves are less than meets the eye; the moniker “stochastic parrots” isn’t wrong. Connect LLMs to specific data for retrieval-augmented generation (RAG) and you get a more reliable system, one that is much less likely to go off the rails and “hallucinate,” which is a relatively nice way of describing LLMs that bullshit you. Connect RAG systems to software that can take actions, even indirect actions like sending emails, and you may have something useful: Agents. These connections, however, don’t spring into being fully grown from their father’s forehead. You need a framework that ties the components together and orchestrates them.
What are LLM application frameworks?
LLM application frameworks are basically plumbing, or, if you like fancier and more specific words, orchestration providers. In a RAG application, for example, LLM application frameworks connect data sources to vector databases via encoders, modify user queries by enhancing them with the result of vector database lookups, pass the enhanced queries to LLM models along with generic system instructions for the models, and pass the models’ output back to the user. Haystack, for example, talks about using components and pipelines to help you assemble LLM applications.
Tomi Engdahl says:
Generative language models exhibit social identity biases
https://www.nature.com/articles/s43588-024-00741-1
Tomi Engdahl says:
Chatbots are designed to mimic human conversation; they go back at least to Joseph Weizenbaum’s ELIZA program, published in 1966. Modern chatbots expand on simple LLM or RAG queries by using some kind of memory to keep track of the conversation, and using previous queries and replies to enhance the context of each new query.
Agents are LLM applications that call other software to perform actions. Microsoft calls them Copilots. Some frameworks differentiate agents from chains, the distinction being that agents use a language model as a reasoning engine to determine which actions to take and in which order, while chains hard-code sequences.
https://www.infoworld.com/article/3617664/surveying-the-llm-application-framework-landscape.html
Tomi Engdahl says:
Business writing
Should You Write with Gen AI?
https://hbr.org/2024/12/should-you-write-with-gen-ai
The promise of sky-high productivity makes the idea of gen AI–assisted writing nearly irresistible: One experimental study found that using ChatGPT made professionals more than 50% faster at their writing tasks, while improving the quality of their work. When you consider how much of our work time is spent on writing in all its forms — a 2023 Microsoft report found that professionals spend a third of their time on email and text messaging, and another third on tasks like note-taking and preparing presentations or documents — you can see how AI-enabled writing might help you tackle the problem of overload and overwork.
Will AI Fix Work?
The pace of work is outpacing our ability to keep up. AI is poised to create a whole new way of working.
https://www.microsoft.com/en-us/worklab/work-trend-index/will-ai-fix-work
Tomi Engdahl says:
‘Generative AI will open opportunities we haven’t even thought of yet’: AWS’s David Levy
Speaking at AWS re:Invent 2024, Levy stressed the role of AWS in democratising AI in public sectors like healthcare, education, and governance.
https://indianexpress.com/article/technology/artificial-intelligence/generative-ai-public-sector-goverment-aws-david-levy-india-9721226/#google_vignette
“Governments, education, and healthcare sectors are leveraging generative AI to save time and speed up processes,” said David Levy, vice president of worldwide public sector at AWS. In the past two years, we have seen how generative AI has evolved as a transformative force steadily reshaping industries and business processes.
Along with businesses, governments around the world are using generative AI and cloud services to improve services and streamline processes, essentially using data to make crucial decisions. At the AWS re:Invent 2024, Levy shed light on how public sector organisations are using technology, the role of AWS in democratising AI, and the ethical and sustainability challenges that are inherent to the ongoing technological wave.
On generative AI uses cases
“One of the most frequent use cases we see is summarising documents—whether it’s for briefings or policies with a high volume of information,” Levy told indianexpress.com.
Levy went on to cite the example of Swindon Borough Council in England that used generative AI to simplify rental agreements for residents whose first language was not English. Levy said that in healthcare, AI helps in expediting hospital discharge processes by automating paperwork, allowing medical practitioners to focus more on patient care.“We’re just at the beginning,” Levy said. “But there’s already a lot of interest and a growing number of use cases for generative AI in the public sector.”
Talking about AWS, Levy said that the leading cloud services provider has positioned itself as a critical enabler of generative AI adoption. The executive outlined the company’s three-tiered approach to this which includes – infrastructure, data and models, and application development, and explained what it comprises of –– custom infrastructure with chips like Trainium and Inferentia for efficient model training and inference; integration of data with third-party or proprietary models via Bedrock; and tools for developing AI applications, ensuring accessibility and scalability.
Tomi Engdahl says:
What Is Agentic AI, and How Will It Change Work?
https://hbr.org/2024/12/what-is-agentic-ai-and-how-will-it-change-work
The way humans interact and collaborate with AI is taking a dramatic leap forward with agentic AI. Think: AI-powered agents that can plan your next trip overseas and make all the travel arrangements; humanlike bots that act as virtual caregivers for the elderly; or AI-powered supply-chain specialists that can optimize inventories on the fly in response to fluctuations in real-time demand. These are just some of the possibilities opened up by the coming era of agentic AI.
Tomi Engdahl says:
Why AI coding assistants are best for experienced developers
https://www.infoworld.com/article/3619505/why-ai-is-best-for-experienced-developers.html
Generative AI tools write code quickly, but need constant supervision and correction. They can be more harmful than helpful in the hands of junior engineers.
Generative AI has hit the mainstream with software developers. According to a recent GitHub survey, more than one million developers actively use GitHub Copilot. More importantly, these developers increasingly use AI as “a new building block in developing applications.” In other words, AI is becoming just as important to software development as tools like Visual Studio Code.
There is, however, a catch. Not everyone benefits equally from AI. As Addy Osmani, an engineering leader with Google’s Chrome team, writes, “AI tools help experienced developers more than beginners.” I’ve talked about this before (here and here), but Osmani makes this argument so lucidly it’s worth repeating, particularly with so many developers adding AI-powered coding assistants to their development process.
All the kids are into tab completion
RedMonk’s Kate Holterhoff has combed through online forums and in-person interviews to identify the “Top 10 Things Developers Want from their AI Code Assistants in 2024.” It’s a great post, filled with useful observations like, “Tab completion is the killer feature in AI code assistants.” Notice, however, that Dr. Holterhoff doesn’t need to talk about whether or not developers want AI coding assistants—they do. The question is, how are they using them?
One reason is to increase productivity. As noted in the GitHub report, GitHub Copilot and similar tools lead to more active contributions to code repositories on GitHub. How much more? “We see higher activity (between 12% to 15% among developers who use GitHub five days a week and 8% to 15% among developers who use GitHub once a week) across open source and public projects.” Lowering the bar to development appears to be A Very Good Thing
Although generative AI can serve as a shortcut for development, such shortcuts are helping the more experienced engineers while hurting the more junior engineers. As Honeycomb co-founder and CTO Charity Majors suggests, generative AI has done nothing “to aid in the work of managing, understanding, or operating … code. If anything, it has only made the hard jobs harder.”
Osmani builds on this idea, arguing that senior developers can look like they’re performing magic when using generative AI tools. (“Scaffold[ing] entire features in minutes”!) But it’s critical to observe carefully what they’re not doing: “They’re not just accepting what the AI suggests.” He says experienced developers are constantly doing things like refactoring AI-generated code, adding things like edge-case handling the AI tool may have missed, etc. In short, he concludes, “They’re applying years of hard-won engineering wisdom to shape and constrain the AI’s output. The AI is accelerating their implementation, but their expertise is what keeps the code maintainable.”
In short, you can’t simply let the AI tool do the work for you. Not if you want well-constructed, maintainable code.
Treat AI like an intern
As I’ve argued, generative AI tools should be regarded as high-functioning interns, not fully autonomous engineers. Osmani makes the same point but takes it further: “The reality is that AI is like having a very eager junior developer on your team. They can write code quickly, but they need constant supervision and correction.”
The very thing that makes an experienced engineer more capable with generative AI has the potential to make less experienced engineers less capable: knowledge. A developer can’t reliably offload work to an AI assistant if she doesn’t first know whether the AI is getting it right. Without that, Osmani notes, developers fall into a pattern of one step forward, two steps back. “You try to fix a small bug. The AI suggests a change that seems reasonable. This fix breaks something else. You ask AI to fix the new issue. This creates two more problems. Rinse and repeat.”
AI tools, in other words, can hurt more than help if engineers don’t first learn the debugging and problem-solving skills of good engineers. As Amazon CEO Andy Jassy used to recite repeatedly, “There is no compression algorithm for experience.” AI doesn’t change this fact.
Tomi Engdahl says:
For developers of all experience levels, this roughly translates into the following approach, according to Osmani:
Start small. Review all AI-generated code, and use it for strictly defined tasks.
Stay modular. Limit the blast radius of AI gone wrong.
Trust your experience. Use AI to accelerate, not replace, your judgment.
These are excellent, actionable suggestions for getting started with AI-driven development—and you should get started. Just don’t expect AI to replace you, whether you view that as a positive or negative thing. Never has experience been more important, even as we hope to augment, not replace, that experience with generative AI.
https://www.infoworld.com/article/3619505/why-ai-is-best-for-experienced-developers.html
Tomi Engdahl says:
Top 10 Things Developers Want from their AI Code Assistants in 2024
https://redmonk.com/kholterhoff/2024/11/18/top-10-things-developers-want-from-their-ai-code-assistants-in-2024/
76% of respondents report relying on AI for tasks like code writing, summarizing information, and code explanation, and 67% of respondents report that AI is helping them improve their code.
There is also a ton more competition in the marketplace. In addition to the options I listed last year of GitHub Copilot, Sourcegraph’s Cody, Amazon CodeWhisperer (now Amazon Q Developer), CodiumAI, IBM’s watsonx Code Assistant, Tabnine, MutableAI, AskCodi, Codiga, and Replit AI, I can now add Aider, Augment Code, Cline, CodeComplete, CodeGeeX, CodeGPT, Codiga, OpenAI Codex, Continue.dev, Cursor, Snyk’s DeepCode AI, Cognition’s Devin, Google’s Gemini Code Assist, Microsoft IntelliCode, JetBrains AI Assistant, Refact.ai, Sourcery, SQLAI, Qodo Gen (formerly Codiumate), and Void (phew). There is also a new breed of agents to accomplish specific tasks in the SDLC such as testing and QA (Copilot Autofix, Graphite Reviewer), as well as agents for bootstrapping entirely new apps like, Replit Agent, Stackblitz Bolt, GitHub Spark, and Vercel v0. These additions (which I do not claim to be encompassing) to my list are not necessarily new since last year, but rather they have appeared on my radar since then. Indeed, staying on top of the AI code assistant offerings and features is a full time job. Relatedly, if you’re reading this, Forrest Brazeal, can I get this list put to the Major-General’s Song, kthx?
Here is some of my evidence that developer sentiment around AI code assistants is evolving. First, when I attended DevNexus in the spring I was struck by the fact that every demo I attended used an AI code assistant in some capacity. Second, developers are forming communities expressly to discuss AI code assistant tooling. While r/GithubCopilot (6.2K members) and r/tabnine (141 members) were founded in 2021, r/cursor (5.5K members) was formed only this February. Beyond providing space to voice their enthusiasm, these and other dedicated communities (Hacker News, Dev.to, Tech conferences and meetups, etc) empower users to troubleshoot bugs, share tips, and negotiate best practices.
Here’s the list:
Tab Completion: According to many developers, tab completion is the killer feature in AI code assistants. These developers call not only for the ability to predict and accept a change using tab, but also to predict the next change after the current completion, enabling them to tab, tab, tab their way to happiness.
Speed: Flow is essential to developers, and nothing pulls them out of this state more completely than lag. The issue of speed appears frequently in forums. For developers, accusations of a “sluggish” experience and being forced to wait is an absolute non-starter. Eliminating latency is the promise of smaller models, and I have heard several complaints that, despite the profound capabilities of OpenAI’s o1, the slowness of this model makes it impracticable for AI code assistant use cases
High-Level: Code assistants are not just for writing scripts and pushing pixels—they assist at app building’s planning stage. Tom Yedwab, Data Architect at Khan Academy, argues (via Reddit’s r/ChatGPTCoding), that one benefit he gets from Cursor is the high-level perspective it offers of his projects:
this tool feels like it is reading my mind, guessing at my next action, and allowing me to think less about the code and more about the architecture … I am building.
Superb Suggestions: Obvious? Maybe. Developers naturally want the first suggestion to be the right one, but this requirement usually has more to do with the technical capabilities of the model than the assistant itself.
Context: Context is King. Developers frequently post questions to Reddit like “How to feed/provide documentations to Github Copilot for context?” and “Looking for an LLM Fully Aware of My Entire Project – Alternatives to GitHub Copilot?” Similarly on Hacker News, Lucas Jans, VP of Product at Agency Revolution, explains:
I want to build my own agents so I can [have] my private domain specific awareness in the coding environment: PRDs, product docs, API docs, business goals, etc.
IDE Fork or No Fork?: There is no consensus about the VS Code fork that Cursor and Void use, except that forks add friction and some developers are mad about it. The folks at Cursor chose to fork rather than build an extension because:
VSCode extensions have very limited control over the UI of the editor. Our Command-K and Copilot++ features aren’t possible as extensions. Same for much of what we want to build in the future!
Multiple LLM Support: I’m cheating a little here as this point is a simplified version of points 9 and 10 that I made last year, but, hoo boy, has it become more relevant. Developers are opinionated about their preferred models. A lot of developers are raving about Claude in 2024, it’s been a huge market impact story. For example, Wes Bos, host of the Syntax podcast, goes into raptures about it. Probably the biggest announcement at Universe was the new models integrated into Copilot of Anthropic’s Claude 3.5 Sonnet, Google’s Gemini 1.5 Pro, and OpenAI’s o1-preview. Thomas Dohmke is right to frame this move as one of “developer choice,” as it is very much something practitioners demand.
Multiple LLMs Simultaneously: Developers want to use two or more models at the same time in order to leverage the strengths of each. According to one Redditor, who uses ChatGPT and Cursor for unit testing:
My ideal outcome is to have OpenAI specify the test plan, chat with Cursor who would execute the test, interpret the output and ask OpenAi any questions. This would be repeated until the unit test is passed.
Multi-file Creation and Editing: The ability to create and edit files is tablestakes. What makes some assistants stand out is how contextually aware these created files are (see point 5 above). According to one Cline user:
I’ve been using Cline and really like it, especially the way I can say “make a new function that does XYZ” and it can easily review all existing ones, and create as many files as necessary. Same with if something isn’t working, I can paste an error code and it goes through the files and comes back with “I see what the issue is…” and so on.
Mitigate Unintended Deletions: Multi-file editing and creation is supported by more AI code assistants today, but with this capability has come a wave of disgruntled developers that are now grappling with unintended deletions.
Tomi Engdahl says:
FuzzyAI: Open-source tool for automated LLM fuzzing
FuzzyAI is an open-source framework that helps organizations identify and address AI model vulnerabilities in cloud-hosted and in-house AI models, like guardrail bypassing and harmful output generation.
https://www.helpnetsecurity.com/2024/12/13/fuzzyai-automated-llm-fuzzing/
FuzzyAI offers organizations a systematic approach to testing AI models against various adversarial inputs, uncovering potential weak points in their security systems, and making AI development and deployment safer. At the heart of FuzzyAI is a powerful fuzzer – a tool that reveals software defects and vulnerabilities – capable of exposing vulnerabilities found via more than ten distinct attack techniques, from bypassing ethical filters to exposing hidden system prompts.
Key features
Comprehensive fuzzing: FuzzyAI probes AI models with various attack techniques to expose vulnerabilities like bypassing guardrails, information leakage, prompt injection, or harmful output generation.
Extensible framework: Organizations and researchers can add their attack methods to tailor tests for domain-specific vulnerabilities.
Community collaboration: A growing community-driven ecosystem ensures continuous adversarial techniques and defense mechanisms advancements.
Supported cloud APIs
OpenAI
Anthropic
Gemini
Huggingface (Downloading models)
Azure Cloud
AWS Bedrock
Ollama
Custom REST API
FuzzyAI is available for free download on GitHub.
https://github.com/cyberark/FuzzyAI
Tomi Engdahl says:
AI Thinks Differently Than People Do. Here’s Why That Matters.
https://hbr.org/2024/12/ai-thinks-differently-than-people-do-heres-why-that-matters
Artificial intelligence (AI) has emerged as a transformative force reshaping business landscapes across industries. While generative AI represents the latest breakthrough in this realm of technological innovation, it stands apart by fundamentally challenging traditional strategic decision-making paradigms. Unlike its predecessors, this technology offers unprecedented capabilities in language processing and content generation, enabling organizations to synthesize complex information, generate nuanced insights, and accelerate decision-making with remarkable precision and depth.
Tomi Engdahl says:
Banish Limiting Beliefs: ChatGPT Prompts To Unlock Your Potential
https://www.forbes.com/sites/jodiecook/2024/12/06/banish-limiting-beliefs-chatgpt-prompts-to-unlock-your-potential/
Tomi Engdahl says:
Generative AI Is My Research and Writing Partner. Should I Disclose It?
In this installment of WIRED’s AI advice column, “The Prompt,” we answer questions about giving AI tools proper attribution and teaching future generations how to interact with chatbots.
https://www.wired.com/story/prompt-disclose-at-in-creative-work-teach-kids-about-chatbots/
Tomi Engdahl says:
Large language models: how the AI behind the likes of ChatGPT actually works
https://theconversation.com/large-language-models-how-the-ai-behind-the-likes-of-chatgpt-actually-works-244701
The arrival of AI systems called large language models (LLMs), like OpenAI’s ChatGPT chatbot, has been heralded as the start of a new technological era. And they may indeed have significant impacts on how we live and work in future.
But they haven’t appeared from nowhere and have a much longer history than most people realise. In fact, most of us have already been using the approaches they are based on for years in our existing technology.
LLMs are a particular type of language model, which is a mathematical representation of language based on probabilities. If you’ve ever used predictive text) on a mobile phone or asked a smart speaker a question, then you have almost certainly already used a language model. But what do they actually do and what does it take to make one?
Language models are designed to estimate how likely it would be to see a particular sequence of words. This is where probabilities come in. For example, a good language model for English would assign a high probability to a well formed sentence like “the old black cat slept soundly” and a low probability to a random sequence of words such as “library a or the quantum some”.
Tomi Engdahl says:
https://www.forbes.com/sites/jodiecook/2024/12/08/5-hard-hitting-chatgpt-prompts-that-will-change-the-way-you-think/
Tomi Engdahl says:
https://venturebeat.com/ai/servicenow-open-sources-fast-llm-in-a-bid-to-help-enterprises-train-ai-models-20x-quicker/
Tomi Engdahl says:
https://blog.google/technology/google-deepmind/google-gemini-ai-update-december-2024/
Tomi Engdahl says:
Tekoälyä vaivaa paha ongelma – ratkaisu “useiden vuosien päässä”
Suvi Korhonen11.12.202406:44TekoälyDigitalous
Tekoälyn kehitys nojaa Huangin mukaan lähitulevaisuudessa yhä raa’an laskentatehonkasvattamiseen.
https://www.tivi.fi/uutiset/tekoalya-vaivaa-paha-ongelma-ratkaisu-useiden-vuosien-paassa/6aa87698-6d86-4bce-ad8f-26b5c92790fd
Tomi Engdahl says:
Edge Impulse Partners with STMicroelectronics for STM32N6 Support in Edge Impulse Studio
Partnership, unveiled during this wee’s STM32 Summit, brings day one support for the Neural-ART coprocessor to Edge Impulse Studio
https://www.hackster.io/news/edge-impulse-partners-with-stmicroelectronics-for-stm32n6-support-in-edge-impulse-studio-dd9f842d7e39
Tomi Engdahl says:
Python-skripteillä syntyy uraauurtavaa tutkimusta – ”ChatGPT on älyttömän hyvä työkalu ei-koodarille”
Tivi13.12.202422:10SupertietokoneetTiedeTutkimusTekoäly
Elektroniikan komponenttien pinnoille tarvitaan hyvin ohuita kalvoja. Mario Mäkinen tutkii supertietokoneiden ja Python-skriptien avulla, mistä aineista syntyvät parhaat pinnoitteet.
https://www.tivi.fi/uutiset/python-skripteilla-syntyy-uraauurtavaa-tutkimusta-chatgpt-on-alyttoman-hyva-tyokalu-ei-koodarille/b0001c1c-fdfd-4ee3-a618-84ee4d6c2823
Ohutta pintaa. ”Ohutkalvoja voi käyttää moniin eri asioihin, kuten akkukomponentteihin tai puolijohteisiin. Vaikka sovelluskohteet ovat erilaisia, mallinnustyöni atomi- ja molekyylikerrosten kasvatuksessa on aina samanlaista reaktiopolkujen määrittämistä”, Mario Mäkinen kertoo.
Tomi Engdahl says:
AGI pyrkii aivoiksi aivojen paikalle
https://www.tivi.fi/uutiset/agi-pyrkii-aivoiksi-aivojen-paikalle/87ebcdd3-e8bd-4e4d-b25d-f1c76fb1fb83
Suuret kielimallit ovat mullistaneet tekoälykehityksen. Vanha unelma koneesta, joka pystyisi toimimaan kuten ihminen, on muuttumassa todellisuudeksi. Mutta milloin tekoälystä tulee ihmisen tasoinen, ja voiko se mennä jopa ohi?
Marraskuussa 2022 esitelty ChatGPT 3.5 avasi tekoälyn portit tutkijoiden lisäksi kaikille kiinnostuneille. Sen jälkeen on ilmestynyt toinen toistaan kehittyneempiä tekoälypalveluita. Kielimallin ideaa on sovellettu tekstin lisäksi ohjelmointiin sekä kuvien ja musiikin tuottamiseen.
Tomi Engdahl says:
Kuin ChatGPT, mutta kolmella ulottuvuudella – mullistaako tämä palvelu peliteollisuuden?
https://www.mikrobitti.fi/uutiset/kuin-chatgpt-mutta-kolmella-ulottuvuudella-mullistaako-tama-palvelu-peliteollisuuden/aaf36dce-10b1-48d8-acd5-7e989271c467
12.12.202412:32|päivitetty12.12.202413:52
Yhdysvaltalainen World Labs on julkaissut uuden tekoälypohjaisen palvelun, jolla tavallisesta kuvasta voi tehdä kolmiulotteisen. Yhtiön käyttämä teknologia perustuu laajoihin maailmamalleihin. Palvelua pääsevät tässä vaiheessa hyödyntämään vain valitut yhteistyökumppanit.
Tomi Engdahl says:
TCL made five short films to help normalize AI-generated movies and TV shows
AI isn’t ready to take over Hollywood just yet
https://www.techspot.com/news/105957-tcl-made-five-short-films-help-normalize-ai.html
The big picture: TCL, one of the world’s largest television manufacturers, has produce five AI-generated short films to air on its free, ad-supported streaming service. The movies, made over a 12-week period, are part of a pilot program meant to normalize AI-created TV shows and movies.
While technically impressive at times, the flicks are plagued by the same shortcomings we’ve seen in other modern AI-generated content. For example, most human characters in the movies exhibit vacant expressions, struggle to vocalize emotion, and move unnaturally. Speech often isn’t synched properly with mouth movements. Written words and text are also problematic, usually resulting in in a jumbled mess of undecipherable characters and symbols. And that’s just scratching the surface.
There are some positives to consider with the current state of AI video – namely, the fact that quality is improving at an impressive rate. As 404 Media highlights, “this is the worst it will ever be” and that’s at least something. They were made using a variety of well-known AI tools including Runway, ComfyUI, and Nuke.
For its part, TCL didn’t simply phone it in. Each film had lots of real people working on them in pre-production and post-production roles.
The fact that humans had a significant role in shaping the films helps push the narrative that AI isn’t going to replace real actors in Hollywood – at least, not right away. Assuming AI continues to evolve like it has in recent years, the need for human oversight could taper.
Tomi Engdahl says:
Google aikoo lisätä tekoälyn kaikkialle – julkisti uuden kielimallin
Google arvioi, että tekoälyn seuraava askel ovat agentit. Niitä varten Gemini 2.0 onkin suunniteltu.
https://www.tekniikkatalous.fi/uutiset/google-aikoo-lisata-tekoalyn-kaikkialle-julkisti-uuden-kielimallin/fabb7632-2f17-4be0-9bad-578e064ed11c
Tomi Engdahl says:
Uudet älylasit markkinoille – toimivat yhdessä ChatGPT:n kanssa
https://mobiili.fi/2024/12/11/uudet-alylasit-markkinoille-toimivat-yhdessa-chatgptn-kanssa/
Solos AirGo Vision -älylaseissa on sisäänrakennettu ChatGPT-integraatio. Lasit tunnistavat kohteita ympärillään ja vastailevat kysymyksiin.
Solos on esitellyt uudet AirGo Vision -älylasit, joiden merkittävin vetonaula on tekoälypalvelu ChatGPT:stä tuttu GPT-4o AI -kielimalli. Sen ja sisäänrakennettujen kameroiden avulla lasit osaavat havainnoida ympäristöään ja vaikkapa kääntää tekstiä kieleltä toiselle, tarjota lisätietoa turistinähtävyyksistä tai opastaa kohteelta toiselle.
Laseja hallinnoidaan äänikomennoilla, minkä ohella sangoissa on kosketusta tunnistavia sensoreita. Myös valokuvia voi halutessaan tallentaa, Solos kuvailee.
Tomi Engdahl says:
STMicroelectronics’ STM32N6 Brings Its In-House Neural-ART NPU to Bear on TinyML, Computer Vision
Neural-ART, the company’s first in-house coprocessor for tinyML, delivers 600 GOPS of compute alongside an 800MHz Arm Cortex-M55 core.
https://www.hackster.io/news/stmicroelectronics-stm32n6-brings-its-in-house-neural-art-npu-to-bear-on-tinyml-computer-vision-0be055f0bdc5
STMicro launches ‘edge’ AI microcontroller
https://www.reuters.com/technology/artificial-intelligence/stmicro-launches-edge-ai-microcontroller-2024-12-10/
Tomi Engdahl says:
https://techxplore.com/news/2024-12-shot-approach-robots-articulated.html
Tomi Engdahl says:
OpenAI’s o1 model doesn’t show its thinking, giving open source an advantage
https://venturebeat.com/ai/heres-how-openai-o1-might-lose-ground-to-open-source-models/
Tomi Engdahl says:
Profile of an MCU promising AI at the tiny edge
https://www.edn.com/profile-of-an-mcu-promising-ai-at-the-tiny-edge/
The common misconception about artificial intelligence (AI) often relates this up-and-coming technology to data center and high-performance compute (HPC) applications. This is no longer true, says Tom Hackenberg, principal analyst for the Memory and Computing Group at Yole Group. He said this while commenting on STMicroelectronics’ new microcontroller that embeds a neural processing unit (NPU) to support AI workloads at the tiny edge.
ST has launched its most powerful MCU to date to cater to a new range of embedded AI applications. “The explosion of AI-enabled devices is accelerating the inference shift from the cloud to the tiny edge,” said Remi El-Ouazzane, president of Microcontrollers, Digital ICs and RF Products Group (MDRF) at STMicroelectronics.
Tomi Engdahl says:
Profile of an MCU promising AI at the tiny edge
https://www.edn.com/profile-of-an-mcu-promising-ai-at-the-tiny-edge/#google_vignette
The common misconception about artificial intelligence (AI) often relates this up-and-coming technology to data center and high-performance compute (HPC) applications. This is no longer true, says Tom Hackenberg, principal analyst for the Memory and Computing Group at Yole Group. He said this while commenting on STMicroelectronics’ new microcontroller that embeds a neural processing unit (NPU) to support AI workloads at the tiny edge.
ST has launched its most powerful MCU to date to cater to a new range of embedded AI applications. “The explosion of AI-enabled devices is accelerating the inference shift from the cloud to the tiny edge,” said Remi El-Ouazzane, president of Microcontrollers, Digital ICs and RF Products Group (MDRF) at STMicroelectronics.
Tomi Engdahl says:
Avoimen koodin projektit hukkuvat uuteen ongelmaan – “tarvitaan perusteellisia muutoksia”
Suvi Korhonen11.12.202413:24Avoin lähdekoodiDigitalousTekoälyOhjelmistokehitys
Haavoittuvuuksien raportoinnista on avoimen koodin yhteisöille hyötyä, kunhan niitä lähetetään vain hyvästä syystä ja selkeästi laadittuina. Kehittäjät toivovat, että buginmetsästäjät tekisivät raportit huolellisesti.
https://www.tivi.fi/uutiset/avoimen-koodin-projektit-hukkuvat-uuteen-ongelmaan-tarvitaan-perusteellisia-muutoksia/ab2fe1a2-c738-4bed-ab1a-16523065b3b6
Tekoälytyökaluilla on nopeaa hutaista virhe- ja muita ilmoituksia. Tasoltaan roskapostin kaltaiset, kielimalleilla houritut raportit kuitenkin kuormittavat avoimen koodin projektien vapaaehtoisia osallistujia.
Tomi Engdahl says:
https://www.quora.com/What-is-the-most-BASIC-A-I-program-that-can-be-made-If-somebody-asked-you-to-make-the-cheapest-and-most-basic-A-I-program-what-would-it-be
Tomi Engdahl says:
Open source maintainers are drowning in junk bug reports written by AI
Python security developer-in-residence decries use of bots that ‘cannot understand code’
https://www.theregister.com/2024/12/10/ai_slop_bug_reports/
Tomi Engdahl says:
https://x.ai/blog/grok-image-generation-release
Tomi Engdahl says:
Open source maintainers are drowning in junk bug reports written by AI
Python security developer-in-residence decries use of bots that ‘cannot understand code’
iconThomas Claburn
Tue 10 Dec 2024 // 08:30 UTC
Software vulnerability submissions generated by AI models have ushered in a “new era of slop security reports for open source” – and the devs maintaining these projects wish bug hunters would rely less on results produced by machine learning assistants.
https://www.theregister.com/2024/12/10/ai_slop_bug_reports/
Tomi Engdahl says:
https://techxplore.com/news/2024-12-ai-complex-problems-faster-supercomputers.html
Tomi Engdahl says:
https://simonwillison.net/2024/Dec/9/llama-33-70b/
Tomi Engdahl says:
NetBSD bans all commits of AI-generated code
https://www.reddit.com/r/programming/comments/1ctvfh9/netbsd_bans_all_commits_of_aigenerated_code/?rdt=51662
Tomi Engdahl says:
Linux distros ban ‘tainted’ AI-generated code — NetBSD and Gentoo lead the charge on forbidding AI-written code
News
By Christopher Harper published May 18, 2024
Not all FOSS (Free and Open Source Software) developers want AI messing with their code.
https://www.tomshardware.com/software/linux/linux-distros-ban-tainted-ai-generated-code
Tomi Engdahl says:
Two-Thirds of Security Leaders Consider Banning AI-Generated Code, Report Finds
https://www.techrepublic.com/article/leaders-banning-ai-generated-code/
Tomi Engdahl says:
Is Microsoft Editor Reliable?
https://www.howtogeek.com/is-microsoft-editor-reliable/#Echobox=1733630472
Summary
Microsoft Editor catches most essential errors and helps with inclusiveness.
It supports many languages and suggests ways to write more concisely.
However, it can be glitchy, miss some glaring mistakes, and sometimes suggest illogical amendments.
Tomi Engdahl says:
How to Build a General-Purpose LLM Agent
https://towardsdatascience.com/build-a-general-purpose-ai-agent-c40be49e7400
Tomi Engdahl says:
The cognitive cost of AI
Artificial intelligence can help with many types of work, but learning how to mitigate the ‘mind tax’ is important.
https://www.fastcompany.com/91242373/the-cognitive-cost-of-ai
Tomi Engdahl says:
In Tests, OpenAI’s New Model Lied and Schemed to Avoid Being Shut Down
https://futurism.com/the-byte/openai-o1-self-preservation
Tomi Engdahl says:
How to Create Value Systematically with Gen AI
https://hbr.org/2024/12/how-to-create-value-systematically-with-gen-ai
Since ChatGPT’s enterprise launch in March 2023, organizations of all sizes and industries have been racing to unlock value with generative AI (gen AI). While the capabilities of the technology itself are expanding quickly, most enterprises’ ability to realize this value has improved little, if at all.
Few organizations have developed a coherent strategy to create and capture value from gen AI, so it shouldn’t be surprising that most programs lack structure or planning. Most of the time, they acquire access to gen AI services, make the technology available to employees, and hope for the best.
Tomi Engdahl says:
Snowflake Releases Arctic Embed L 2.0 and Arctic Embed M 2.0: A Set of Extremely Strong Yet Small Embedding Models for English and Multilingual Retrieval
https://www.marktechpost.com/2024/12/07/snowflake-releases-arctic-embed-l-2-0-and-arctic-embed-m-2-0-a-set-of-extremely-strong-yet-small-embedding-models-for-english-and-multilingual-retrieval/