A selection of the most important recent news, articles, and papers about AI.
News, Articles, and Analyses
IBM text-to-SQL generator tops leaderboard – IBM Research
(Tuesday, July 02, 2024) “IBM’s generative AI solution takes a top spot on the BIRD benchmark for handling complex database queries”
Reaffirming IBM’s commitment to the Rome Call for AI ethics – IBM Research
(Monday, July 15, 2024) “IBM joined representatives from many of the world’s major religions in Japan to discuss ethical AI development.”
AMD takes a deep dive into architecture for the AI PC chips | VentureBeat
Author: Dean Takahashi
(Monday, July 15, 2024) “Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Advanced Micro Devices executives revealed the details of the chipmaker’s latest AI PC architecture, which includes a new neural processing unit (NPU) in the company’s latest AMD Ryzen AI chips. The company announced the latest AMD Ryzen […]”
MathΣtral | Mistral AI | Frontier AI in your hands
(Tuesday, July 16, 2024) “As a tribute to Archimedes, whose 2311th anniversary we’re celebrating this year, we are proud to release our first Mathstral model, a specific 7B model designed for math reasoning and scientific discovery. The model has a 32k context window published under the Apache 2.0 license.”
AI in gaming: Developers worried by generative tech
“In a struggling games industry AI has been hailed as a possible saviour. But not everyone’s convinced.”
Technical Papers and Preprints
[2407.12690] The Dual Imperative: Innovation and Regulation in the AI Era
Author: Carvão, Paulo
(Thursday, May 23, 2024) “This article addresses the societal costs associated with the lack of regulation in Artificial Intelligence and proposes a framework combining innovation and regulation. Over fifty years of AI research, catalyzed by declining computing costs and the proliferation of data, have propelled AI into the mainstream, promising significant economic benefits. Yet, this rapid adoption underscores risks, from bias amplification and labor disruptions to existential threats posed by autonomous systems. The discourse is polarized between accelerationists, advocating for unfettered technological advancement, and doomers, calling for a slowdown to prevent dystopian outcomes. This piece advocates for a middle path that leverages technical innovation and smart regulation to maximize the benefits of AI while minimizing its risks, offering a pragmatic approach to the responsible progress of AI technology. Technical invention beyond the most capable foundation models is needed to contain catastrophic risks. Regulation is required to create incentives for this research while addressing current issues.”
[2407.12043] The Art of Saying No: Contextual Noncompliance in Language Models
Authors: Brahman, Faeze; Kumar, Sachin; Balachandran, Vidhisha; Dasigi, Pradeep; Pyatkin, Valentina; Ravichander, Abhilasha; Wiegreffe, Sarah; Dziri, Nouha; Chandu, Khyathi; Hessel, Jack; Tsvetkov, Yulia; Smith, Noah A.; Choi, Yejin; Hajishirzi, Hannaneh
(Tuesday, July 02, 2024) “Chat-based language models are designed to be helpful, yet they should not comply with every user request. While most existing work primarily focuses on refusal of “unsafe” queries, we posit that the scope of noncompliance should be broadened. We introduce a comprehensive taxonomy of contextual noncompliance describing when and how models should not comply with user requests. Our taxonomy spans a wide range of categories including incomplete, unsupported, indeterminate, and humanizing requests (in addition to unsafe requests). To test noncompliance capabilities of language models, we use this taxonomy to develop a new evaluation suite of 1000 noncompliance prompts. We find that most existing models show significantly high compliance rates in certain previously understudied categories with models like GPT-4 incorrectly complying with as many as 30% of requests. To address these gaps, we explore different training strategies using a synthetically-generated training set of requests and expected noncompliant responses. Our experiments demonstrate that while direct finetuning of instruction-tuned models can lead to both over-refusal and a decline in general capabilities, using parameter efficient methods like low rank adapters helps to strike a good balance between appropriate noncompliance and other capabilities.”
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Notable and Interesting Recent AI News, Articles, and Papers for Tuesday, July 9, 2024
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Notable and Interesting Recent AI News, Articles, and Papers for Monday, July 1, 2024
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IEEE Quantum Week and IBM Quantum
This year’s IEEE Quantum Week is planned for October 12-16, 2020, in Denver, Colorado.
IEEE Quantum Week is a multidisciplinary quantum computing venue where attendees will have the unique opportunity to discuss challenges and opportunities with quantum researchers, scientists, engineers, entrepreneurs, developers, students, practitioners, educators, programmers, and newcomers.
The IBM Quantum team is well represented at the conference with a keynote from Jerry Chow. We also have 7 tutorials and 2 workshops.
Our tutorials are on the following topics:
- Quantum programming, an introduction
- Quantum machine learning for data scientists
- Quantum hardware control: a hands-on introduction
- Quantum algorithms for optimization
- Quantum algorithms for chemistry simulation
- Assessing the quality of qubits and quantum computers
- Serious Games for Quantum Computing
Our workshops are:
- Control and design of superconducting qubits
- Software for quantum applications, algorithms, and workflows
Registration is now open.
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Some practical things you can do to learn about quantum computing
People often ask me “Where should I get started in order to learn about quantum computing?”. Here are several steps you can take. I work for IBM, so things I link to will often be to the IBM Quantum program. Also, I acknowledge that several of the links and videos toward the beginning involve me, but we’ll get through those quickly.
Watch some introductory videos
If you only watch one video, watch this one from WIRED with Talia Gershon:
This one with me is from early 2019 and discussed the IBM Q System One:
Finally, this video from CNBC with Professor Scott Aaronson of the University of Texas Austin, Martin Reynolds of Gartner, and me brings things up to date in January, 2020. Note that I personally do not support many of the statements about “Quantum Supremacy” (horrible label, supercomputers do have massive amounts of storage, off-by-15-million-percent math error):
Get a book
If you are really just getting started and want to systematically work through the required math at an easy and conversational pace, my book Dancing with Qubits should prepare you for more advanced material and give you a start to reading research papers. (Shameless self-plug.)
If you are a hard core physics and/or computer science person, you want to have Quantum Computation and Quantum Information: 10th Anniversary Edition 10th Anniversary ed. Edition by Michael A. Nielsen and Issac L. Chuang in your library. It’s a little old by now, but if you want to end up doing quantum computing research, you will likely have to become very familiar and comfortable with the contents. Other books to consider are Quantum Computing: A Gentle Introduction (good on algorithms, “gentle” is subjective!) and Quantum Computing for Computer Scientists (a bit dated and make sure you get a copy of the errata).
Play a game
Hello Quantum is available for Apple iOS and Android and will teach you the basics of how quantum gates and circuits work.
Build and run circuits with a real quantum computer
Quantum simulators have their place for basic education, experimentation, and debugging. Note, though, that a quantum simulator is to real quantum computer hardware as a TV console flight simulator is to a real plane. If you want a job as a pilot, I would prefer you knew how to fly an actual airplane.
The easiest way to get started without writing code is with the IBM Quantum Composer within the IBM Quantum Experience.
The IBM Quantum Experience has over 200,000 registered users, so you’ll be joining a very large community of beginner, intermediate, and advanced users.
Learn Python
If you are going to write quantum computing code, learn Python. As I write this, the latest version is 3.8. You want Python 3, not Python 2.
Learn Jupyter Notebooks
This is the modern way of developing full documents with interactive code, executions, graphics, videos, and visualizations. It’s used within the IBM Quantum Experience but also many other computational and AI applications. You are mainly interested in how to use it through a browser, not how to run and maintain the console.
Website (introductory): Introduction to Jupyter Notebooks
Write quantum computing code in Qiskit
Qiskit is the leading open source platform for developing quantum computing code and applications. It’s available on Github and available under the Apache 2,0 license. It’s had over 300,000 downloads but I’m recommending you use it through your browser on the IBM Cloud. As with the Composer, it is available through the IBM Quantum Experience.
Whether you want to download Qiskit or use it online, the easiest way to get get started is to watch the series of videos by Abe Asfaw.
From there, you can watch the other videos and also learn about the Qiskit Community.
At this point you are ready to work your way through the online open source Learn Quantum Computing through Qiskit.
