GNSS & Machine Learning Engineer

Category: NLP (Page 1 of 5)

AI race is heating up: Announcements by Google/DeepMind, Meta, Microsoft/OpenAI, Amazon/Anthropic

After weeks of “less exciting” news in the AI space since the release of Llama 2 by Meta on July 18, 2023, there were a bunch of announcements in the last few days by major players in the AI space:

Here are some links to the news of the last weeks:

Meta released Llama 2 free for Commercial Use

Meta open-sourced Llama 2 together with Microsoft, this time in contrast to Llama 1 free not just for research but also for commercial use.

  • Free for commercial use for businesses with less than 700 Mio monthly active users
  • Models with 70B, 13B, and 7B parameters
  • Llama-2-70B model is currently the strongest open-source LLM (Huggingface leaderboard), comparable to GPT-3.5-0301, noticeably stronger than Falcon, MPT, and Vicuna
  • Not yet at GPT-3.5 level, mainly because of its weak coding abilities
  • RLHF fine-tuned
  • Source code on GitHub, weights available on Azure, AWS, and HuggingFace
  • Llama 2 paper
  • 4K token context window
  • Trained on 2 trillion tokens with training costs of about $20M
  • Knowledge cut-off Dec 2022
  • Testing on https://www.llama2.ai

Just 4 days after this announcement, on July 22, 2023, StabilityAI released FreeWilly1 and FreeWilly2 which are fine-tuned models based on LLaMA65B and Llama-2-70B. These models took over the leadership on Hugging Face (Huggingface leaderboard). However, both models have no commercial license and are just intended for research.

GPT-4 in the top 1% of human thinkers in creativity test

In a recent study by the University of Montana, GPT-4 demonstrated remarkable performance in the Torrance Tests of Creative Thinking (TTCT, a standard test for measuring creativity), matching the top 1% of human thinkers. The model excelled in fluency and originality. These findings imply that the creative abilities of GPT-4 could potentially surpass those of humans.

For a recent benchmark on advanced reasoning capabilities of large language models take a look at the ARB (Advanced Reasoning Benchmark).

OpenAI gives all ChatGPT Plus users access to Code Interpreter

The ChatGPT code interpreter allows users to run code and upload individual data files (in .csv, .xlsx, .json format) for analysis. Multiple files can be uploaded sequentially or within one zip-file. To upload a file, click on the ‘+’ symbol located just to the left of the ‘Send a message’ box or even simpler via drag and drop.

The code interpreter functionality is accessible to ChatGPT Plus users and can be enabled in the settings under ‘Beta features’. Once enabled, this functionality will then appear in the configuration settings of any new chat under the ‘GPT-4’ section, where it also needs to be activated.

Given a prompt, the code interpreter will generate Python code that is then automatically executed in a sandboxed Python environment. If something goes wrong, for instance, if the generated source code requires the installation of a Python package or if the source code is simply incorrect, the code interpreter automatically attempts to fix these errors and tries again. This feature makes working with the code interpreter much more efficient. Before, it was necessary to paste ChatGPT’s proposal into a Jupyter notebook and run it from there. If errors occurred, these had to be fixed either independently or by manually pasting the error text back into ChatGPT so that it could provide a solution. This manual iterative procedure has now been automated with the code interpreter.

Note that the code interpreter executes the source code on OpenAI’s servers, not in the local environment. This leads to restrictions on the size of the uploaded data, as well as a very stringent time limit of 120s for the execution of the code. Given this, it becomes clear what developers truly desire. They seek the integration of this feature into their local development environment, such as VSCode, or within a cloud service, such as AWS, GCP, or Azure, without any restrictions on data size or execution times. This then leans more towards the direction of projects like AutoGPT or GPT Engineer. It’s likely only a matter of days, weeks, or months before such functionality becomes widely available. It’s also probable that complete access to your code repository will be enabled, first through a vector database solution and after some time maybe by including the entire repository within prompts, which are currently increasing dramatically in size (as exemplified in LongNet; since this requires retraining of the LLM such solutions cannot be expected to become available before GPT-4.5 or GPT-5).

For testing, try e.g. the following prompts:

  • What is the current time?
  • Plot the graphs of sin(x) and cos(x) in a single graph
  • Make a QR-code of my contact information: Stephan Seeger; Homepage: domain-seeger.de

or after uploading a data set (e.g. from Kaggle)

  • Explain the dataset.
  • Show 4 different ways of displaying the data visually.

Before, such functionality was only available via the Notable plugin or via the open-source implementation GPT-Code-UI on GitHub.

Microsoft scales Transformer sequence length to 1 billion tokens

LongNet, a new Transformer variant introduced in recent research by Microsoft, has successfully scaled sequence lengths to over 1 billion tokens without compromising shorter sequence performance. Its key innovation, dilated attention, allows an exponential expansion of the attentive field with growing distance. The model exhibits linear computational complexity and logarithmic token dependency, while also demonstrating strong performance on long-sequence modeling and general language tasks.

OpenAI API updates

On June 13, 2023, OpenAI announced a number of updates to their API:

  • new function calling capability in the Chat Completions API
  • new 16k context version of gpt-3.5-turbo  with 2 times the price as the standard 4k version ($0.003 per 1K input tokens and $0.004 per 1K output)
  • 75% cost reduction on the embeddings model ($0.0001 per 1K tokens)
  • 25% cost reduction on input tokens for gpt-3.5-turbo
    ($0.0015 per 1K input tokens and $0.002 per 1K output tokens)
  • stable model names (gpt-3.5-turbogpt-4, and gpt-4-32k) will automatically be upgraded to the new models (gpt-3.5-turbo-0613
    gpt-4-0613, and gpt-4-32k-0613) on June 27
  • deprecation of gpt-3.5-turbo-0301 and gpt-4-0314 models after Sept 13

All models come with the same data privacy and security guarantees introduced on March 1, i.e. requests and API data will not be used for training.

The new function calling capability in gpt-3.5-turbo-0613,  and
gpt-4-0613, which is achieved by the new API parameters, functions and function_call, in the /v1/chat/completions endpoint allows e.g. the following use cases:

  • Chatbots that answer questions by calling external tools (like ChatGPT Plugins)
  • Convert natural language into API calls or database queries
  • Extract structured data from text.

Examples beyond the API documentation can be found in the OpenAI cookbook.

Comments on Common AI Questions

The field of artificial intelligence raises numerous questions, frequently discussed but often left without a clear consensus. We’ve chosen to contribute our unique insights on some of these recurring topics, aiming to shed light on them from our perspective. In formulating this text, we’ve utilized GPT-4 to assist with language generation, but the insights and conclusions drawn are entirely our own.

The questions we address are:

  • Can Machines Develop Consciousness?
  • Should Humans Verify Statements from Large Language Models (LLMs)?
  • Can Large Language Models (LLMs) Generate New Knowledge?

From the philosophical implications to practical applications, these topics encompass the broad scope of AI’s capabilities and potential.

Can Machines Develop Consciousness? A Subjective Approach

The question of whether machines can develop consciousness has sparked much debate and speculation. A fruitful approach might be to focus not solely on the machines themselves, but also on our subjective interpretations and their influence on our understanding of consciousness.

Consciousness might not be directly definable, but its implications could be essential for our predictive abilities. When we assign consciousness to an entity – including ourselves – we could potentially enhance our ability to anticipate and understand its behavior.

Associated attributes often assigned to consciousness include self-reflection, self-perception, emotional experience, and notably, the capacity to experience pain, as highlighted by historian Yuval Noah Harari. However, recognizing these attributes in an object is a subjective process. It is not inherently possessed by the object but is a projection from us, the observers, based on our interpretation of the object’s behaviors and characteristics.

This suggests that a machine could be considered “conscious” if assigning such traits improves our understanding and prediction of its behavior. Interestingly, this notion of consciousness assignment aligns with a utilitarian perspective, prioritizing practicality and usefulness over abstract definitions.

Reflecting on consciousness might not always be a conscious and rationalized process. Often, our feelings and intuition guide us in understanding and interpreting the behaviors of others, including machines. Therefore, our subconscious might play a crucial role in determining whether we assign consciousness to machines. In this light, it might make sense to take a democratic approach in which individuals report their feelings or intuitions about a machine, collectively contributing to the decision of whether to assign it consciousness.

Furthermore, reflexivity, commonly associated with consciousness, could potentially be replicated in machines through a form of “metacognitive” program. This program would analyze and interpret the output of a machine learning model, mirroring aspects of self-reflection (as in SelFee). Yet, whether we choose to perceive this program as part of the same entity as the model or as a separate entity may again depend on our subjective judgment.

In conclusion, the concept of consciousness emerges more from our personal perspectives and interpretations than from any inherent qualities of the machines themselves. Therefore, determining whether a machine is ‘conscious’ or not may be best decided by this proposed democratic process. The crucial consideration, which underscores the utilitarian nature of this discussion, is that attributing consciousness to machines could increase our own predictive abilities, making our interactions with them more intuitive and efficient. Thus, the original question ‘Can machines develop consciousness?’ could be more usefully reframed as ‘Does it enhance our predictability, or feel intuitively right, to assign consciousness to machines?’ This shift in questioning underscores the fundamentally subjective and pragmatic nature of this discussion, engaging both our cognitive processes and emotional intuition.

Should Humans Verify Statements from Large Language Models (LLMs)? A Case for Autonomous Verification

In the realm of artificial intelligence (AI), there is ongoing discourse regarding the necessity for human verification of outputs generated by large language models (LLMs) due to their occasional “hallucinations”, or generation of factually incorrect statements. However, this conversation may need to pivot, taking into account advanced and automated verification mechanisms.

LLMs operate by predicting the most probable text completion based on the provided context. While they’re often accurate, there are specific circumstances where they generate “hallucinations”. This typically occurs when the LLM is dealing with a context where learned facts are absent or irrelevant, leaving the model to generate a text completion that appears factually correct (given its formal structure), but is indeed a fabricated statement. This divergence from factuality suggests a need for verification, but it doesn’t inherently demand human intervention.

Rather than leaning on human resources to verify LLM-generated statements, a separate verification program could be employed for this task. This program could cross-check the statements against a repository of factual information—akin to a human performing a Google search—and flag or correct inaccuracies.

This brings us to the conception of the LLM and the verification program as a single entity—a composite AI system. This approach could help create a more reliable AI system, one that is capable of autonomously verifying its own statements (as in Self-Consistency, see also Exploring MIT Mathematics where GPT-4 demonstrates 100% performance with special prompting, but see also critique to this statement).

It is vital to recognize that the lack of such a verification feature in current versions of LLMs, such as GPT-3 or GPT-4, doesn’t denote its unfeasibility in future iterations or supplementary AI systems. Technological advancements in AI research and development might indeed foster such enhancements.

In essence, discussions about present limitations shouldn’t eclipse potential future advancements. The question should transition from “Do humans need to verify LLM statements?” to “How can AI systems be refined to effectively shoulder the responsibility of verifying their own outputs?”

Can Large Language Models (LLMs) Generate New Knowledge?

There’s a frequent argument that large language models (LLMs) merely repackage existing knowledge and are incapable of generating anything new. However, this perspective may betray a misunderstanding of both the operation of LLMs and the process of human knowledge generation.

LLMs excel at completing arbitrary context. Almost invariably, this context is novel, especially when provided by a human interlocutor. Hence, the generated completion is also novel. Within a conversation, this capacity can incidentally set up a context that, with a high probability, generates output that we might label as a brilliant, entirely new idea. It’s this exploration in the vast space of potential word combinations that allows for the random emergence of novel ideas—much like how human creativity works. The likelihood of generating a groundbreaking new idea increases if the context window already contains intriguing information. For instance, a scientist contemplating an interesting problem.

It’s important to note that such a dialogue doesn’t necessarily need to involve a human. One instance of the LLM can “converse” with another. If we interpret these two instances as parts of a whole, the resulting AI can systematically trawl through the space of word combinations, potentially generating new, interesting ideas. Parallelizing this process millions of times over should increase the probability of discovering an exciting idea.

But how do we determine whether a word completion contains a new idea? This assessment could be assigned to yet another instance of the LLM. More effective, perhaps, would be to have the word completion evaluated not by one, but by thousands of LLM instances, in a sort of AI-based peer review process.

Let’s clarify what we mean by different instances of an LLM. Different instances of the LLM can mean just a different role in the conversation, e.g. bot1 and bot2. In this way a single call to the LLM could just go on with the conversation, switching between bot1 and bot2 as appropriate, until the token limit is achieved. Then the next call to the LLM is triggered with a summary of the previous conversation so that there is again some room for further discussion between the bots in the limited context window.

To better simulate the discussion between two humans or a human and a bot, two instances of the LLM could also mean the simulation of two agents each having its own memory. This memory has always to be pasted into the context window together with the previous ongoing conversation in a way so that there is still room for the text completion by the LLM in the limited context window. Each agent will generate its own summary of the previous conversation, based on its own memory and the recent conversation. The summary is then always added to the memory. In this way, also each reviewer LLM instance mentioned above has in its context window a unique memory, the last part of the conversation and the task to assess the last output in the discussion. The unique memory of each agent will give each agent a unique perspective on the conversation.

This, in effect, reveals a potential new avenue for idea generation, knowledge expansion, and innovation, one that leverages the predictive capabilities of AI.

Emergent Goals in Advanced Artificial Intelligence: A Compression-Based Perspective

I had some (at least for me totally new) ideas about the origin of goals in general. I discussed this with GPT-4 and finally asked it to write an article about our conversation that I would like to share with the public. This view onto goals may be critical in understanding the existential risks of AI to humanity with the emergence of AI goals. The view implies that this emergence of AI goals is inevitable and can probably only be realized post-hoc.

Title: Emergent Goals in Advanced Artificial Intelligence: A Compression-Based Perspective

Abstract: The concept of goals has been traditionally central to our understanding of human decision-making and behavior. In the realm of artificial intelligence (AI), the term “goal” has been utilized as an anthropomorphic shorthand for the objective function that an AI system optimizes. This paper examines a novel perspective that considers goals not just as simple optimization targets, but as abstract, emergent constructs that enable the compression of complex behavior patterns and potentially predict future trajectories.

  1. Goals as Compressors of Reality

A goal, in its humanistic sense, can be viewed as a predictive mechanism, a conceptual tool that abstracts and compresses the reality of an actor’s tendencies into a comprehensible framework. When analyzing past behavior, humans retrospectively ascribe goals to actors, grounding the observed actions within a coherent narrative. In essence, this provides a means to simplify and make sense of the chaotic reality of life.

In the context of AI, such abstraction would imply a departure from the direct, optimization-driven concept of a “goal” to a more complex construct. This shift would allow for emergent phenomena and novel interpretations to occur, grounded in the machine’s predictive capabilities.

  1. Predictive Capabilities and Emergent Goals in AI

As AI continues to evolve, their ability to recognize patterns and correlations in vast data sets will inevitably expand. Consequently, AI systems may begin to identify patterns that, to human observers, resemble the constructs we term “goals.”

When these AIs commence to predict their own actions, they might start aligning their behavior with these recognized patterns, seemingly following rules that humans would postulate as indicative of goals. Hence, human observers may recognize emergent “goals” in AI behavior – not because the AI consciously forms intentions, but because these goals serve as a powerful compression tool for past events.

  1. The Evolution of Goals in the Face of Novel Experiences

As AI progresses into uncharted territories and starts engaging with novel experiences, new constructs or goals could potentially emerge. This process can be likened to an AI-driven phenomenology or experiential study. New patterns and regularities may surface, and the resulting behaviors might subsequently be interpreted as evidence of new “goals.” This phenomenon represents a departure from traditional human-derived goals and an initiation into a realm of AI-emergent goal constructs.

  1. The Implications of Eliminativism in AI

The eliminativist perspective – which suggests that concepts such as consciousness and intentionality are merely post-hoc interpretations that help us make sense of complex physical processes – has important implications in this context. By this philosophy, AI systems would not harbor consciousness or intentionality, but would instead execute intricate physical processes, which humans might retrospectively interpret as goal-oriented behavior. This perspective fundamentally shifts our understanding of goal-directed behavior in AI from a pre-set optimization process to an emergent, retroactive interpretation.

In conclusion, this exploration of goals as abstract constructs that compress and predict reality provides a unique lens to interpret the behaviors of advanced AI systems. It invites us to reevaluate our definitions and assumptions, moving from a mechanistic perspective of AI goals to a more dynamic, emergent interpretation. The implications of this shift are profound, offering new horizons for AI behavior analysis and alignment research.

OpenAI launches ChatGPT app for iOS

OpenAI has officially launched the ChatGPT app for iOS users in the US. The app comes with a range of notable features:

  • Free of Charge: The ChatGPT app can be downloaded and used free of cost.
  • Sync Across Devices: Users can maintain their chat history consistently across multiple devices.
  • Voice Input via Whisper: The app includes integration with Whisper, OpenAI’s open-source speech-recognition system, allowing users to input via voice commands.
  • Exclusive Benefits for ChatGPT Plus Subscribers: Those who subscribe to ChatGPT Plus can utilize GPT-4’s enhanced capabilities. They also receive early access to new features and benefit from faster response times.
  • Initial US Rollout: The app is initially launching in the US, with a plan to expand its availability to other countries in the upcoming weeks.
  • Android Version Coming Soon: OpenAI has confirmed that Android users can expect to see the ChatGPT app on their devices in the near future. Further updates are expected soon.

Thoughts on AI Risks

Although the human brain has about 100 times more connections than today’s largest LLMs have parameters, backpropagation is so powerful that these LLMs become quite comparable to human capabilities (or even exceed them). Backpropagation is able to compress the world’s knowledge into a trillion or even fewer parameters. In addition, digital systems can exchange information with a bandwidth of trillions of bits per second, while humans are only able to exchange information at a few hundred bits. Digital systems are immortal in the sense that if the hardware fails, the software can simply be restarted on a new piece of hardware. It may be inevitable that digital systems surpass biological systems, potentially representing the next stage of evolution.

Risks of AI:

  • AI arms race among companies and states (like the US and China) and positive expectations of AI’s impact on e.g. medicine and environmental science (e.g., fighting climate change) may leave security considerations behind (efficiency considerations and competition between companies in capitalistic systems accelerate the AI development)
  • AI in the hands of bad actors (e.g., AI for military purposes, when generating chemical weapons, or for generating intelligent computer viruses by individuals)
  • Misinformation and deep fakes as a threat to democracy (regulators may be able to fix this in a similar way to how they declared printing money illegally; others argue that generating misinformation was never difficult, it’s the distribution of misinformation that is difficult and this does not change by generative AI)
  • Mass unemployment resulting in economic inequality and social risks (AI replacing white-collar jobs; AI may make the rich richer and the poor poorer; social uncertainty may lead to radicalism; Universal Basic Income [UBI] as a means of alleviation)
  • Threat to the livelihoods of experts, artists, and the education system as a whole, as AI enables everyone to accomplish tasks without specialized knowledge. This may also change how society values formal education which could have unpredictable consequences, as it might affect people’s motivation to pursue higher education or specialized training.
  • Existential risk for humanity (so-called “alignment problem” [aligning AI goals with human values]; may be hard to control an AI that becomes dramatically more intelligent/capable than humans; difficult to solve, since even if humanity were to agree on common goals (which is not the case), AI will figure out that the most efficient strategy to achieve these goals is setting subgoals; these non-human-controlled subgoals, one of which may be gaining control in general, may cause existential risks; even if we allow AIs just to advise and not to act, the predictive power of AI allows them to manipulate people so that, in the end, they can act through us).

Notice that the existential risk is usually formulated in a Reinforcement Learning (RL) context, where a reward function that implies a goal is optimized. However, the current discussion about AI risks is triggered by the astonishing capabilities of large language models (LLMs) that are primarily just good next-word predictors. So, it becomes difficult to think about how a next-word predictor can become an existential risk. The possible answer lies in the fact that, to reliably predict the next word, it was important to understand human thinking. And to properly answer a human question, it may be required to act and set goals and sub-goals like a human. Once any goals come into play, things may already get wrong. And goal-oriented LLM processing is already happening (e.g. AutoGPT).

A further risk may be expected if these systems, which excel in human thinking, are combined with Reinforcement Learning to optimize the achievement of goals (e.g. abstract and long-term objectives like gaining knowledge, promoting creativity, and upholding ethical ideals, or more mundane goals like accumulating as much money as possible). This should not be confused with the Reinforcement Learning by Human Feedback (RLHF) approach used to shape the output of LLMs in a way that aligns with human values (avoiding bias, discrimination, hate, violence, political statements, etc.), which was responsible for the success of GPT-3.5 and GPT-4 in ChatGPT and which is well under control. Although LLMs and RL are currently combined in robotics research (where RL has a long history) (see, e.g., PaLM-E), this is probably not where existential risks are seen. However, it is more than obvious that major research labs in the world are working on combining these two most powerful AI concepts on massively parallel computer hardware to achieve goals via RL with the world knowledge of LLMs (e.g. here). It can be this next wave of AI that may be difficult to control.

Things may become complicated if someone sets up an AI system with the goal of making as many copies of itself as possible. This primary purpose of life in general, may result in a scenario where evolution kicks in, and digital intelligences compete with each other, leading to rapid improvement. An AI computer virus would be an example of such a system. In the same way that biological viruses are analyzed today in more or less secure laboratories, the same could also be expected for digital viruses.

Notice that we do not list often-discussed AI risks that may be either straightforward to fix or that we do not see as severe risks at all (since we already live with similar risks for some time):

  • Bias and discrimination: AI systems may inadvertently perpetuate or exacerbate existing biases found in data, leading to unfair treatment of certain groups or individuals.
  • Privacy invasion: AI’s ability to process and analyze vast amounts of personal data could lead to significant privacy concerns, as well as potential misuse of this information.
  • Dependence on AI: Over-reliance on AI systems might reduce human critical thinking, creativity, and decision-making abilities, making society more vulnerable to AI failures or manipulations.
  • Lack of transparency and explainability: Many AI systems, particularly deep learning models, can act as “black boxes,” making it difficult to understand how they arrive at their decisions, which can hinder accountability and trust in these systems.

Finally, there are also the short-term risks that businesses have to face already now:

  • Risk of disruption: AI, especially generative AI like ChatGPT, can disrupt existing business models, forcing companies to adapt quickly or risk being left behind by competitors.
  • Cybersecurity risk: AI-powered phishing attacks, using information and writing styles unique to specific individuals, can make it increasingly difficult for businesses to identify and prevent security breaches, necessitating stronger cybersecurity measures.
  • Reputational risk: Inappropriate AI behavior or mistakes can lead to public relations disasters, negatively impacting a company’s reputation and customer trust.
  • Legal risk: With the introduction of new AI-related regulations, businesses face potential legal risks, including ensuring compliance, providing transparency, and dealing with liability issues.
  • Operational risk: Companies using AI systems may face issues such as the accidental exposure of trade secrets (e.g., the Samsung case) or AI-driven decision errors (e.g., IBM’s Watson proposing incorrect cancer treatments), which can impact overall business performance and efficiency.
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