Point-and-Click Coding: An eGame Boom Booster
November 22, 2024
TheNextWeb explains “How AI Can Help You Make a Computer Game Without Knowing Anything About Coding.” That’s great—unless one is a coder who makes one’s living on computer games. Writer Daniel Zhou Hao begins with a story about one promising young fellow:
“Take Kyo, an eight-year-old boy in Singapore who developed a simple platform game in just two hours, attracting over 500,000 players. Using nothing but simple instructions in English, Kyo brought his vision to life leveraging the coding app Cursor and also Claude, a general purpose AI. Although his dad is a coder, Kyo didn’t get any help from him to design the game and has no formal coding education himself. He went on to build another game, an animation app, a drawing app and a chatbot, taking about two hours for each. This shows how AI is dramatically lowering the barrier to software development, bridging the gap between creativity and technical skill. Among the range of apps and platforms dedicated to this purpose, others include Google’s AlphaCode 2 and Replit’s Ghostwriter.”
The write-up does not completely leave experienced coders out of the discussion. Hao notes tools like Tabnine and GitHub Copilot act as auto-complete assistance, while Sourcery and DeepCode take the tedium out of code cleanup. For the 70-ish percent of companies that have adopted one or more of these tools, he tells us, the benefits include time savings and more reliable code. Does this mean developers will to shift to “higher value tasks,” like creative collaboration and system design, as Hao insists? Or will it just mean firms will lighten their payrolls?
As for building one’s own game, the article lists seven steps. They are akin to basic advice for developing a product, but with an AI-specific twist. For those who want to know how to make one’s AI game addictive, contact benkent2020 at yahoo dot com.
Cynthia Murrell, November 22, 2024
China Smart, US Dumb: LLMs Bad, MoEs Good
November 21, 2024
Okay, an “MoE” is an alternative to LLMs. An “MoE” is a mixture of experts. An LLM is a one-trick pony starting to wheeze.
Google, Apple, Amazon, GitHub, OpenAI, Facebook, and other organizations are at the top of the list when people think about AI innovations. We forget about other countries and universities experimenting with the technology. Tencent is a China-based technology conglomerate located in Shenzhen and it’s the world’s largest video game company with equity investments are considered. Tencent is also the developer of Hunyuan-Large, the world’s largest MoE.
According to Tencent, LLMs (large language models) are things of the past. LLMs served their purpose to advance AI technology, but Tencent realized that it was necessary to optimize resource consumption while simultaneously maintaining high performance. That’s when the company turned to the next evolution of LLMs or MoE, mixture of experts models.
Cornell University’s open-access science archive posted this paper on the MoE: “Hunyuan-Large: An Open-Source MoE Model With 52 Billion Activated Parameters By Tencent” and the abstract explains it is a doozy of a model:
In this paper, we introduce Hunyuan-Large, which is currently the largest open-source Transformer-based mixture of experts model, with a total of 389 billion parameters and 52 billion activation parameters, capable of handling up to 256K tokens. We conduct a thorough evaluation of Hunyuan-Large’s superior performance across various benchmarks including language understanding and generation, logical reasoning, mathematical problem-solving, coding, long-context, and aggregated tasks, where it outperforms LLama3.1-70B and exhibits comparable performance when compared to the significantly larger LLama3.1-405B model. Key practice of Hunyuan-Large include large-scale synthetic data that is orders larger than in previous literature, a mixed expert routing strategy, a key-value cache compression technique, and an expert-specific learning rate strategy. Additionally, we also investigate the scaling laws and learning rate schedule of mixture of experts models, providing valuable insights and guidance for future model development and optimization. The code and checkpoints of Hunyuan-Large are released to facilitate future innovations and applications.”
Tencent has released Hunyuan-Large as an open source project, so other AI developers can use the technology! The well-known companies will definitely be experimenting with Hunyuan-Large. Is there an ulterior motive? Sure. Money, prestige, and power are at stake in the AI global game.
Whitney Grace, November 21, 2024
Does Smart Software Forget?
November 21, 2024
A recent paper challenges the big dogs of AI, asking, “Does Your LLM Truly Unlearn? An Embarrassingly Simple Approach to Recover Unlearned Knowledge.” The study was performed by a team of researchers from Penn State, Harvard, and Amazon and published on research platform arXiv. True or false, it is a nifty poke in the eye for the likes of OpenAI, Google, Meta, and Microsoft, who may have overlooked the obvious. The abstract explains:
“Large language models (LLMs) have shown remarkable proficiency in generating text, benefiting from extensive training on vast textual corpora. However, LLMs may also acquire unwanted behaviors from the diverse and sensitive nature of their training data, which can include copyrighted and private content. Machine unlearning has been introduced as a viable solution to remove the influence of such problematic content without the need for costly and time-consuming retraining. This process aims to erase specific knowledge from LLMs while preserving as much model utility as possible.”
But AI firms may be fooling themselves about this method. We learn:
“Despite the effectiveness of current unlearning methods, little attention has been given to whether existing unlearning methods for LLMs truly achieve forgetting or merely hide the knowledge, which current unlearning benchmarks fail to detect. This paper reveals that applying quantization to models that have undergone unlearning can restore the ‘forgotten’ information.”
Oops. The team found as much as 83% of data thought forgotten was still there, lurking in the shadows. The paper offers a explanation for the problem and suggestions to mitigate it. The abstract concludes:
“Altogether, our study underscores a major failure in existing unlearning methods for LLMs, strongly advocating for more comprehensive and robust strategies to ensure authentic unlearning without compromising model utility.”
See the paper for all the technical details. Will the big tech firms take the researchers’ advice and improve their products? Or will they continue letting their investors and marketing departments lead them by the nose?
Cynthia Murrell, November 21, 2024
Entity Extraction: Not As Simple As Some Vendors Say
November 19, 2024
No smart software. Just a dumb dinobaby. Oh, the art? Yeah, MidJourney.
Most of the systems incorporating entity extraction have been trained to recognize the names of simple entities and mostly based on the use of capitalization. An “entity” can be a person’s name, the name of an organization, or a location like Niagara Falls, near Buffalo, New York. The river “Niagara” when bound to “Falls” means a geologic feature. The “Buffalo” is not a Bubalina; it is a delightful city with even more pleasing weather.
The same entity extraction process has to work for specialized software used by law enforcement, intelligence agencies, and legal professionals. Compared to entity extraction for consumer-facing applications like Google’s Web search or Apple Maps, the specialized software vendors have to contend with:
- Gang slang in English and other languages; for example, “bumble bee.” This is not an insect; it is a nickname for the Latin Kings.
- Organizations operating in Lao PDR and converted to English words like Zhao Wei’s Kings Romans Casino. Mr. Wei has been allegedly involved in gambling activities in a poorly-regulated region in the Golden Triangle.
- Individuals who use aliases like maestrolive, james44123, or ahmed2004. There are either “real” people behind the handles or they are sock puppets (fake identities).
Why do these variations create a challenge? In order to locate a business, the content processing system has to identify the entity the user seeks. For an investigator, chopping through a thicket of language and idiosyncratic personas is the difference between making progress or hitting a dead end. Automated entity extraction systems can work using smart software, carefully-crafted and constantly updated controlled vocabulary list, or a hybrid system.
Automated entity extraction systems can work using smart software, carefully-crafted and constantly updated controlled vocabulary list, or a hybrid system.
Let’s take an example which confronts a person looking for information about the Ku Group. This is a financial services firm responsible for the Kucoin. The Ku Group is interesting because it has been found guilty in the US for certain financial activities in the State of New York and by the US Securities & Exchange Commission.
Content Conversion: Search and AI Vendors Downplay the Task
November 19, 2024
No smart software. Just a dumb dinobaby. Oh, the art? Yeah, MidJourney.
Marketers and PR people often have degrees in political science, communications, or art history. This academic foundation means that some of these professionals can listen to a presentation and struggle to figure out what’s a horse, what’s horse feathers, and what’s horse output.
Consequently, many organizations engaged in “selling” enterprise search, smart software, and fusion-capable intelligence systems downplay or just fib about how darned easy it is to take “content” and shove it into the Fancy Dan smart software. The pitch goes something like this: “We have filters that can handle 90 percent of the organization’s content. Word, PowerPoint, Excel, Portable Document Format (PDF), HTML, XML, and data from any system that can export tab delimited content. Just import and let our system increase your ability to analyze vast amounts of content. Yada yada yada.”
Thanks, Midjourney. Good enough.
The problem is that real life content is often a problem. I am not going to trot out my list of content problem children. Instead I want to ask a question: If dealing with content is a slam dunk, why do companies like IBM and Oracle sustain specialized tools to convert Content Type A into Content Type B?
The answer is that content processing is an essential step because [a] structured and unstructured content can exist in different versions. Figuring out the one that is least wrong and most timely is tricky. [b] Humans love mobile devices, laptops, home computers, photos, videos, and audio. Furthermore, how does a content processing get those types of content from a source not located in an organization’s office (assuming it has one) and into the content processing system? The answer is, “Money, time, persuasion, and knowledge of what employee has what.” Finding a unicorn at the Kentucky Derby is more likely. [c] Specialized systems employ lingo like “Export as” and provide some file types. Yeah. The problem is that the output may not contain everything that is in the specialized software program. Examples range from computational chemistry systems to those nifty AutoCAD type drawing system to slick electronic trace routing solutions to DaVinci Resolve video systems which can happily pull “content” from numerous places on a proprietary network set up. Yeah, no problem.
Evidence of how big this content conversion issue is appears in the IBM write up “A New Tool to Unlock Data from Enterprise Documents for Generative AI.” If the content conversion work is trivial, why is IBM wasting time and brainpower figuring out something like making a PowerPoint file smart software friendly?
The reason is that as big outfits get “into” smart software, the people working on the project find that the exception folder gets filled up. Some documents and content types don’t convert. If a boss asks, “How do we know the data in the AI system are accurate?”, the hapless IT person looking at the exception folder either lies or says in a professional voice, “We don’t have a clue?”
IBM’s write up says:
IBM’s new open-source toolkit, Docling, allows developers to more easily convert PDFs, manuals, and slide decks into specialized data for customizing enterprise AI models and grounding them on trusted information.
But one piece of software cannot do the job. That’s why IBM reports:
The second model, TableFormer, is designed to transform image-based tables into machine-readable formats with rows and columns of cells. Tables are a rich source of information, but because many of them lie buried in paper reports, they’ve historically been difficult for machines to parse. TableFormer was developed for IBM’s earlier DeepSearch project to excavate this data. In internal tests, TableFormer outperformed leading table-recognition tools.
Why are these tools needed? Here’s IBM’s rationale:
Researchers plan to build out Docling’s capabilities so that it can handle more complex data types, including math equations, charts, and business forms. Their overall aim is to unlock the full potential of enterprise data for AI applications, from analyzing legal documents to grounding LLM responses on corporate policy documents to extracting insights from technical manuals.
Based on my experience, the paragraph translates as, “This document conversion stuff is a killer problem.”
When you hear a trendy enterprise search or enterprise AI vendor talk about the wonders of its system, be sure to ask about document conversion. Here are a few questions to put the spotlight on what often becomes a black hole of costs:
- If I process 1,000 pages of PDFs, mostly text but with some charts and graphs, what’s the error rate?
- If I process 1,000 engineering drawings with embedded product and vendor data, what percentage of the content is parsed for the search or AI system?
- If I process 1,000 non text objects like videos and iPhone images, what is the time required and the metadata accuracy for the converted objects?
- Where do unprocessable source objects go? An exception folder, the trash bin, or to my in box for me to fix up?
Have fun asking questions.
Stephen E Arnold, November 19, 2024
After AI Billions, a Hail, Mary Play
November 19, 2024
Now it is scramble time. Reuters reports, “OpenAI and Others Seek New Path to Smarter AI as Current Methods Hit Limitations.” Why does this sound familiar? Perhaps because it is a replay of the enterprise search over-promise and under-deliver approach. Will a new technique save OpenAI and other firms? Writers Krystal Hu and Anna Tong tell us:
“After the release of the viral ChatGPT chatbot two years ago, technology companies, whose valuations have benefited greatly from the AI boom, have publicly maintained that ‘scaling up’ current models through adding more data and computing power will consistently lead to improved AI models. But now, some of the most prominent AI scientists are speaking out on the limitations of this ‘bigger is better’ philosophy. … Behind the scenes, researchers at major AI labs have been running into delays and disappointing outcomes in the race to release a large language model that outperforms OpenAI’s GPT-4 model, which is nearly two years old, according to three sources familiar with private matters.”
One difficulty, of course, is the hugely expensive and time-consuming LLM training runs. Another: it turns out easily accessible data is finite after all. (Maybe they can use AI to generate more data? Nah, that would be silly.) And then there is that pesky hallucination problem. So what will AI firms turn to in an effort to keep this golden goose alive? We learn:
“Researchers are exploring ‘test-time compute,’ a technique that enhances existing AI models during the so-called ‘inference’ phase, or when the model is being used. For example, instead of immediately choosing a single answer, a model could generate and evaluate multiple possibilities in real-time, ultimately choosing the best path forward. This method allows models to dedicate more processing power to challenging tasks like math or coding problems or complex operations that demand human-like reasoning and decision-making.”
OpenAI is using this approach in its new O1 model, while competitors like Anthropic, xAI, and Google DeepMind are reportedly following suit. Researchers claim this technique more closely mimics the way humans think. That couldn’t be just marketing hooey, could it? And even if it isn’t, is this tweak really enough?
Cynthia Murrell, November 19, 2024
AI and Efficiency: What Is the Cost of Change?
November 18, 2024
No smart software. Just a dumb dinobaby. Oh, the art? Yeah, MidJourney.
Companies are embracing smart software. One question which gets from my point of view little attention is, “What is the cost of changing an AI system a year or two down the road?” The focus at this time is getting some AI up and running so an organization can “learn” whether AI works or not. A parallel development is taking place in software vendors enterprise and industry-centric specialized software. Examples range from a brand new AI powered accounting system to Microsoft “sticking” AI into the ASCII editor Notepad.
Thanks, MidJourney. Good enough.
Let’s tally the costs which an organization faces 24 months after flipping the switch in, for example, a hospital chain which uses smart software to convert a physician’s spoken comments about a patient to data which can be used for analysis to provide insight into evidence based treatment for the hospital’s constituencies.
Here are some costs for staff, consultants, and lawyers:
- Paying for the time required to figure out what is on the money and what is not good or just awful like dead patients
- The time required to figure out if the present vendor can fix up the problem or a new vendor’s system must be deployed
- Going through the smart software recompete or rebid process
- Getting the system up and running
- The cost of retraining staff
- Chasing down dependencies like other third party software for the essential “billing process”
- Optimizing the changed or alternative system.
The enthusiasm for smart software makes talking about these future costs fade a little.
I read “AI Makes Tech Debt More Expensive,” and I want to quote one passage from the pretty good essay:
In essence, the goal should be to unblock your AI tools as much as possible. One reliable way to do this is to spend time breaking your system down into cohesive and coherent modules, each interacting through an explicit interface. A useful heuristic for evaluating a set of modules is to use them to explain your core features and data flows in natural language. You should be able to concisely describe current and planned functionality. You might also want to set up visibility and enforcement to make progress toward your desired architecture. A modern development team should work to maintain and evolve a system of well-defined modules which robustly model the needs of their domain. Day-to-day feature work should then be done on top of this foundation with maximum leverage from generative AI tooling.
Will organizations make this shift? Will the hyperbolic AI marketers acknowledge the future costs of pasting smart software on existing software like circus posters on crumbling walls?
Nope.
Those two year costs will be interesting for the bean counters when those kicked cans end up in their workspaces.
Stephen E Arnold, November 18, 2024
Salesforce to MSFT: We Are Coming, Baby Cakes
November 18, 2024
No smart software. Just a dumb dinobaby. Oh, the art? Yeah, MidJourney.
Salesforce, an outfit that hopped on the “attention” bandwagon, is now going whole hog with smart software. “Salesforce to Hire More Than 1,000 Workers to Boost AI Product Sales” makes clear that AI is going to be the hook for the company for the next hype cycle riding toward Next Big Thing theme park.
The write up says:
Agentforce is a new layer on the Salesforce platform, designed to enable companies to build and deploy AI agents that autonomously perform tasks.
Now that’s a buzz packed sentence: “Layer,” sales call data as a “platform”, “AI agents”, “autonomously”, and smart software that can “perform tasks.”
The idea is that sales are an important part of a successful organization. The exception is that monopolies really don’t need too many sales professionals. Lawyers? Yes. Forward deployed engineers? Yes. Marketers? Yes. Door knockers? Well, probably fewer going forward.
How does the Salesforce AI system work? The answer is simple it seems:
These AI agents operate independently, triggered by data changes, business rules, pre-built automations, or API signals.
Who writes the rules? I wonder if AI writes it own rules or do specialists get an opportunity to demonstrate their ability to become essential cogs in the Salesforce customers’ machines?
What do customers do with smart Salesforce? Once again, the answer is easy to provide. The write up says:
Companies such as OpenTable, Saks and Wiley are currently utilizing Agentforce to augment their workforce and enhance customer experiences. Over the past two years, Salesforce has focused on controlling sales expenses by reducing jobs and encouraging customers to use self-service or third-party purchasing options.
I think I understand. Get rid of pesky humans, their vacations, health care, pension plans, and annoying demands for wage increases. Salesforce delivers “efficiency.”
I am not sure what to make of this set of statements. Underpinning Salesforce is a database. The stuff on top of the database are interfaces. Now smart software promises to deliver efficiency and obviously another layer of “smart stuff” to provide what software and services have been promising since the days of the punched card.
Smart software, like Web search, is a natural monopoly unless specific deep pocket outfits can create a defensible niche and sell enough smart software to that niche before some other company eats their lunch.
But that’s what some companies do? Eat other individual’s lunch. So whose taking those lunches tomorrow? Amazon, Google, Microsoft, or Salesforce? Maybe the lunch thief will be a pesky start up essentially off the radar of the big hungry dogs?
With AI development shifting East, is the Silicon Valley AI way the future. Heck, even Google is moving smart software to London which is a heck of a lot easier flight to some innovative locations.
Hopefully one of the AI companies can convert billions in AI investment into new revenue and big profits in a sprightly manner. So far, I see marketing and AI dead ends. Is Salesforce, as the long gone Philco radio company used to say, “The leader”? On one hand, Salesforce is hiring. On the other, get rid of employees. Okay, I think I understand.
Stephen E Arnold, November 18, 2024
Management Brilliance or Perplexing Behavior
November 15, 2024
Sorry to disappoint you, but this blog post is written by a dumb humanoid. The art? We used MidJourney.
TechCrunch published “Perplexity CEO Offers AI Company’s Services to Replace Striking NYT Staff.” The New York Times Tech Guild went on strike. Aravind Srinivas, formerly at OpenAI and founder of Perplexity, made an interesting offer. According to the cited article, Mr. Srinivas allegedly said he would provide services to “mitigate the effect of a strike by New York Times tech workers.”
A young startup luminary reacts to a book about business etiquette. His view of what’s correct is different from what others have suggested might win friends and influence people. Thanks, MidJourney. Good enough.
Two points: Crossing the picket lines seemed okay if the story is correct and assuming that Perplexity’s smart software would “mitigate the effect” of the strike.
According to the article, “many” people criticized Mr. Srinivas’ offer to help a dead tree with some digital ornaments in a time of turmoil. What the former OpenAI wizard suggested he wanted to do was:
to provide technical infra support on a high traffic day.
Infra, I assume, is infrastructure. And a high-traffic day at a dead tree business is? I just don’t know. The Gray Lady has an online service and it bought an eGame which lacks the bells and whistles of Hamster Kombat. I think that Hamster Kombat has a couple of hundred million users and a revenue stream from assorted addictive elements jazzed with tokens. Could Perplexity help out Telegram if its distributed network ran into more headwinds that the detainment of its founder in France?
Furthermore, the article reminded me that the Top Dog of the dead tree outfit “sent Perplexity a cease and desist letter in October [2024] over the startup’s scraping of articles for use by its AI models.”
What interests me, however, is the outstanding public relations skills that Mr. Srinivas demonstrated. He has captured headlines with his “infra” offer. He is getting traction on Twitter, now the delightfully named X.com. He is teaching old-school executives like Tim Apple how to deal with companies struggling to adapt to the AI, go fast approach to business.
Perplexity’s offer illustrates a conceptual divide between old school publishing, labor unions, and AI companies. Silicon Valley outfits have a deft touch. (I almost typed “tone deaf”. Yikes.)
Stephen E Arnold, November 15, 2024
A Digital Flea Market Tests Smart Software
November 14, 2024
Sales platform eBay has learned some lessons about deploying AI. The company tested three methods and shares its insights in the post, “Cutting Through the Noise: Three Things We’ve Learned About Generative AI and Developer Productivity.” Writer Senthil Padmanabhan explains:
“Through our AI work at eBay, we believe we’ve unlocked three major tracks to developer productivity: utilizing a commercial offering, fine-tuning an existing Large Language Model (LLM), and leveraging an internal network. Each of these tracks requires additional resources to integrate, but it’s not a matter of ranking them ‘good, better, or best.’ Each can be used separately or in any combination, and bring their own benefits and drawbacks.”
The company could have chosen from several existing commercial AI offerings. It settled on GitHub Copilot for its popularity with developers. That and the eBay codebase was already on GitHub. They found the tool boosted productivity and produced mostly accurate documents (70%) and code (60%). The only problem: Copilot’s limited data processing ability makes it impractical for some applications. For now.
To tweak and train an open source LLM, the team chose Code Llama 13B. They trained the camelid on eBay’s codebase and documentation. The resulting tool reduced the time and labor required to perform certain tasks, particularly software upkeep. It could also sidestep a problem for off-the-shelf options: because it can be trained to access data across internal services and within non-dependent libraries, it can get to data the commercial solutions cannot find. Thereby, code duplication can be avoided. Theoretically.
Finally, the team used an Retrieval Augmented Generation to synthesize documentation across disparate sources into one internal knowledge base. Each piece of information entered into systems like Slack, Google Docs, and Wikis automatically received its own vector, which was stored in a vector database. When they queried their internal GPT, it quickly pulled together an answer from all available sources. This reduced the time and frustration of manually searching through multiple systems looking for an answer. Just one little problem: Sometimes the AI’s responses were nonsensical. Were any just plain wrong? Padmanabhan does not say.
The post concludes:
“These three tracks form the backbone for generative AI developer productivity, and they keep a clear view of what they are and how they benefit each project. The way we develop software is changing. More importantly, the gains we realize from generative AI have a cumulative effect on daily work. The boost in developer productivity is at the beginning of an exponential curve, which we often underestimate, as the trouble with exponential growth is that the curve feels flat in the beginning.”
Okay, sure. It is all up from here. Just beware of hallucinations along the way. After all, that is one little detail that still needs to be ironed out.
Cynthia Murrell, November 14, 2024
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