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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:

Room-temperature Superconductivity breakthrough?

A groundbreaking discovery has potentially been made in the field of superconductivity. Researchers from South Korea have developed a superconductor material, codenamed LK 99 (short for the authors Lee and Kim that made the first discovery of the material in 1999), that potentially operates at room temperature and atmospheric pressure. This would be a significant leap forward, overcoming the limitations of previous superconductors that required extremely low temperatures or high pressures to function.

Superconductivity, a quantum mechanical phenomenon where the electrical resistance of a material vanishes and magnetic flux fields are expelled from within the material, was first discovered by Dutch physicist Heike Kamerlingh Onnes in 1911. This discovery earned him the Nobel Prize in Physics in 1913. The implications of this phenomenon are vast, particularly for energy transmission and storage, as superconductors can conduct electricity with virtually no loss of energy.

One of the key features of superconductivity is the Meissner effect, where a superconductor in a magnetic field will expel the magnetic field within the material. This is due to the superconductor’s perfect diamagnetism, and it leads to phenomena such as magnetic levitation.

Another significant contribution to the understanding of superconductivity came from Vitaly Ginzburg and Alexei Abrikosov, who, along with Anthony Leggett, were awarded the Nobel Prize in Physics in 2003. Ginzburg and Abrikosov developed the Ginzburg-Landau theory in the 1950s, a phenomenological theory that describes superconductivity in the vicinity of the critical temperature. This theory successfully explains many properties of superconductors, including the Meissner effect, and it has been instrumental in the development of the theory of type II superconductors, which remain superconducting in the presence of strong magnetic fields.

The understanding of superconductivity took a significant leap forward in 1957 when John Bardeen, Leon Cooper, and John Robert Schrieffer proposed the BCS theory. This theory, which explains how electrical resistance in certain materials disappears at very low temperatures, earned them the Nobel Prize in Physics in 1972. The theory introduced the concept of Cooper pairs, where electrons with opposite momenta and spins pair up and move through the lattice of positive ions in the material without scattering and losing energy.

In 1986, the discovery of high-temperature superconductors by Georg Bednorz and K. Alex Müller, who were awarded the Nobel Prize in Physics in 1987, marked another milestone in the field. These materials exhibited superconducting properties at temperatures higher than those predicted by the BCS theory, opening up new possibilities for practical applications.

Each superconductor has a critical temperature below which it exhibits superconductivity, and some require a minimum pressure. Traditional superconductors need extreme cooling and sometimes high pressure. High-temperature superconductors work at warmer temperatures, but still below room level. The new material, LK 99, is groundbreaking as it remains superconducting at room temperature and atmospheric pressure.

The researchers published two papers discussing their findings on arXiv within two hours of each other on July 22, 2023. The first paper, “The First Room-Temperature Ambient-Pressure Superconductor”, was authored by Sukbae Lee, Ji-Hoon Kim, and Young-Wan Kwon. The second paper, “Superconductor Pb_10-x Cu_x (PO_4)_6 O showing levitation at room temperature and atmospheric pressure and mechanism”, was authored by the same first two researchers of the first paper along with Hyun-Tak Kim, Sungyeon Im, SooMin An, and Keun Ho Auh. The strategic authorship suggests a potential candidacy for the Nobel Prize, which can only be shared among three people.

In March 2023, the group filed for their international patent application, further solidifying their claim. However, the scientific community has expressed some skepticism due to a past incident. Randa Dias, a physicist at the University of Rochester, had a paper published in Nature in October 2020 claiming room-temperature superconductivity in a carbonaceous sulfur hydride under extreme pressure. The paper was retracted in September 2022 after other researchers were unable to replicate the results. While we await conclusive evidence supporting the claim of room-temperature superconductivity, you can monitor the scientific community’s assessment of the claim here.

The LK 99 material has a critical current of 250 mA at 300°K (27°C) that quickly drops towards almost 0 when reaching 400°K. The current generates a magnetic field that breaks down superconductivity. This is a crucial aspect as high currents for generating high magnetic fields are central for applications in MRIs and in fusion reactors, where the magnetic field is used for the confinement of the plasma.

The proposed superconductor is not only revolutionary but also simple and inexpensive to produce. The process involves three steps explicitly explained in the second paper using common materials: lead oxide, lead sulfate, copper powder, and phosphorus. The resulting compound, Pb10-xCux(PO4)6O, is achieved through a series of heating and mixing processes.

The use of copper instead of lead in the superconductor results in a shrinkage effect, which was previously achieved through high pressure. This is related to the concept of a quantum well, a potential well with discrete energy values. The quantum well effect is the underlying mechanism for superconductivity in LK-99.

The potential applications of room-temperature superconductors are transformative. They could lead to more efficient power transmission, reducing energy loss during transmission through power lines. They could also enable cheaper and simpler magnetic resonance imaging (MRI) machines, fusion reactors, high-speed magnetic trains, and quantum computers. In addition, they could lead to more efficient batteries, potentially revolutionizing the energy storage industry. A more detailed discussion of the implications of a room-temperature ambient-pressure superconductor that depends on whether strong or weak magnetic fields and currents are possible has been put together by Andrew Cote.

A comprehensive overview of this discovery has been provided in a YouTube video by ‘Two Bit da Vinci’.

The breakthrough discovery of the room-temperature superconductor LK 99 is not the only recent advancement in the field of superconductivity. In a related development, a team of scientists from MIT and their colleagues have created a simple superconducting device that could dramatically cut energy use in computing. This device, a type of diode or switch, could transfer current through electronic devices much more efficiently than is currently possible.

The team’s work, published in the July 13 online issue of Physical Review Letters, showcases a superconducting diode that is more than twice as efficient as similar ones reported by others. It could even be integral to emerging quantum computing technologies. The diode is nanoscopic, about 1,000 times thinner than the diameter of a human hair, and is easily scalable, meaning millions could be produced on a single silicon wafer.

The team discovered that the edge asymmetries within superconducting diodes, the ubiquitous Meissner screening effect found in all superconductors, and a third property of superconductors known as vortex pinning all came together to produce the diode effect. This discovery opens the door for devices whose edges could be “tuned” for even higher efficiencies.

These advancements in superconductivity, both in the creation of room-temperature superconductors and the development of highly efficient superconducting diodes, hold great promise for the future of technology and energy efficiency. They could lead to more efficient power transmission, revolutionize the energy storage industry, and dramatically cut the amount of energy used in high-power computing systems.

You can read more about the superconducting diode in the article.

On July 29, 2023, there has been an additional announcement by Taj Quantum in Florida for a Type II room-temperature superconductor (US patent 17249094).

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

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:

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.

Statement on AI Risk

A vast number of AI experts have signed a statement to raise public awareness regarding the most severe risks associated with advanced AI, aiming to mitigate the risk of human extinction. Among the signatories are Turing Award laureates Geoffrey Hinton and Yoshua Bengio (but not Yann LeCun from Meta), and the CEOs of leading AI companies like Sam Altman from OpenAI, Demis Hassabis from Google DeepMind, Dario Amodei from Anthropic, and Emad Mostaque from Stability AI.

The statement is featured on the webpage of the Center for AI Safety, which provides a list of eight examples of existential risks (x-risks). The enumerated risks are based on the publication “X-Risk Analysis for AI Research” which appeared on Sept. 20, 2022, on arXiv. This highly valuable paper also lists in its Appendix a bunch of practical steps to mitigate risks.

The listed risks are:

  • Weaponization:
    Malicious actors could repurpose AI to be highly destructive.
  • Misinformation:
    AI-generated misinformation and persuasive content could undermine collective decision-making, radicalize individuals, or derail moral progress.
  • Proxy Gaming:
    AI systems may pursue their goals at the expense of individual and societal values.
  • Enfeeblement:
    Humanity loses the ability to self-govern by increasingly delegating tasks to machines.
  • Value Lock-in:
    Highly competent systems could give small groups of people a tremendous amount of power, leading to a lock-in of oppressive systems.
  • Emergent Goals:
    The sudden emergence of capabilities or goals could increase the risk that people lose control over advanced AI systems.
  • Deception:
    To better understand AI systems, we may ask AI for accurate reports about them. However, since deception may help agents to better achieve their goals and this behavior may have strategic advantages, it is never safe to trust these systems.
  • Power-Seeking Behavior:
    Companies and governments have strong economic incentives to create agents that can accomplish a broad set of goals. Such agents have instrumental incentives to acquire power, potentially making them harder to control.

This statement about AI risks appeared a few days after an OpenAI blog post by Sam Altman, Greg Brockman, and Ilya Sutskever, which also addresses the mitigation of risks associated with AGI or even superintelligence that could arise within the next 10 years.

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.

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