AI-powered Coding Tools

Andrew NG summarized the state of AI-powered code generators including eBay’s low-code, DeepMind introduced AlphaCode, GitHub’s Copilot that autocompletes your code in real time, and OpenAI’s GPT-3. “The widely available versions of this technology aren’t yet able to write complex programs. Often their output looks right at first glance but turns out to be buggy. Moreover, their legal status may be in jeopardy of violating open source licensing agreements. AI-powered coding tools aren’t likely to replace human programmers in the near future, but they may replace the tech question-and-answer site Stack Overflow as the developer’s favorite crutch.Link

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Cocktail Conversations

Neil Raden published an interesting post at LinkedIn: “Maybe I need to rethink my position on #chatGPT. Does it display humanlike intelligence? Maybe its vapid, content-free prose really is human intelligence. I described “Cocktail Conversation” in InformationWeek in 2008 as chatter one can make about a subject, and seem knowledgeable, but possess only a very superficial grasp of it.”

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Happy Holidays

A Christmas message from Alan Fish to all DMCommunity members, friends, colleagues and collaborators: Wishing you a peaceful break and a prosperous 2023! Listen

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Christmas Model created by ChatGPT

With Christmas around the corner, we offer our readers this Challenge created by ChatGPT. It defines a set of people, a set of gifts, and the happiness level and cost of each gift. The objective is to maximize the total happiness, subject to the budget constraint that the total cost of the gifts must be less than or equal to the budget, and the constraint that each person can only receive one gift. Link

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Demystifying ChatGPT

2 Millions people have already signed up to use ChatGPT that has been generating a lot of buzz in the AI community. What can it create, and where are the humans in the loop? How does this generalize? Cassie Kozyrkov, Chief Decision Scientist at Google, is trying to demystify this “Revolutionary New Tool for Conversation Generation”. Link

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Posted in Artificial Intelligence, Human-Machine Interaction, Machine Learning | 1 Comment

Can’t We Do Better Than This?

BRCommunity.com just published a story written by Bas van der Raadt: “Why did I almost end my 15+ year career in Enterprise architecture? Because I felt like large organizations are just doomed — destined to stay stuck in the complexities they created themselves. And there was nothing I was able to do about it, no matter how hard I tried. But before I decided to drastically change my career path, I thought: Can’t we do better than this? This question triggered an idea I wanted to investigate: Would it help if we could write intelligent software systems in a language that businesspeople themselves can read easily? And what if we could write software systems that are directly deployable after specification, without any technical release steps altogether? Would that be possible? Would that help make our complex world of large enterprises a bit simpler? In this article I share with you the story of my investigative journey to a more comprehensible world.Link Read also his articles “What is the difference between Ontologies and Business Rules?” and “How are an ontology and a business rule connected?

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Natural Language Execution – NLE

There is an ongoing discussion about NLE at LinkedIn started by Bas van der Raadt: “Contrary to Natural Lanuage Processing (NLP), NLE to me is: Going directly from (controlled) natural language to executable software without any intermediate steps of for example generating and compiling technical code, or any other technical deployment steps.What do you thing about this term Natural Language Execution?” People frequently refer to this Challenge. Link

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The Death of Batch?

Forbes recently spoke to Hazelcast about the future of enterprise applications. “Where data exists in environments and sits in applications, databases, web services and other entities that move back and forth, its non real-time status is typically denoted by the fact that it has to be compiled, parsed, managed, saved and so processed in batches at the end of the day, or some other defined period. This is why real-time advocates are fond of talking about the so-called ‘end, or death of batch’ era today. When and where data moves in real-time streams, there is enough computing capacity and crucial enabling real-time software engine intelligence to make a human user perceive that a process has happened instantaneously.” Modern Decision Microservices are frequently used to process real-time streams. Link

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Four Predictions for Practical AI

Dr. Scott Zoldi from FICO published his “4 AI Predictions for 2023: From the Great Correction to Practical AI“. He states: “Welcome to the Great Correction. But what might feel like an unmitigated flameout is actually a correction back to normalcy, nowhere more evident than in more realistic approaches to artificial intelligence and its attendant machine learning (ML) models, algorithms and neural networks. I’m calling this new pragmatism Practical AI, and I predict this technology will rise in 2023 like a phoenix from the ashes of years of irrational exuberance around artificial intelligence.” Link

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2022 Gartner Magic Quadrant for Full Life Cycle API Management

Gartner defines the full life cycle application programming interface (API) management market as the market for software that supports all stages of an API’s life cycle — planning and design, implementation and testing, deployment and operation, and versioning and retirement. APIs provide a basis for digital transformation and enable organizations to build business ecosystems, but are challenging to manage and govern. This Magic Quadrant assesses 17 vendors in the fast-evolving full life cycle API management market to help software engineering leaders pick the right one Link

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