On the flip side, ChatGPT has been roundly criticized for its inability to answer simple logic questions or work backwards from a desired solution to the steps needed to achieve it. Teachers and school administrators voiced fears that students would use the tool to cheat, while political conservatives complained that Chat generates answers with a liberal bias. Elon Musk, Apple co-founder Steve Wozniak, and others signed an open letter recommending a six-month pause in AI development, noting “Powerful AI systems should be developed only once we are confident that their effects will be positive and their risks will be manageable.”

The one factor missing from virtually all these comments – regardless of whether they regard ChatGPT as a huge step forward or a threat to humanity – is a recognition that no matter how impressive, ChatGPT merely gives the illusion of understanding. It is simply manipulating symbols and code samples which it has pulled from the Internet without any understanding of what they mean. And because it has no true understanding, it is neither good nor bad. It is simply a tool which can be manipulated by humans to achieve certain outcomes, depending on the intentions of the users.

It is that difference that distinguishes ChatGPT, and all other AI for that matter, from AGI – artificial general intelligence, defined as the ability of an intelligent agent to understand or learn any intellectual task that a human can. While ChatGPT undoubtedly represents a major advance in self-learning AI, it is important to recognize that it only seems to understand. Like all other AI to date, it is completely reliant on datasets and machine learning. ChatGPT simply appears more intelligent because it depends on bigger and more sophisticated datasets.

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While some experts continue to argue that at some point in the future, AI will morph into AGI, that outcome seems highly unlikely. Because today’s AI is entirely dependent on massive data sets, there is no way to create a dataset big enough for the resulting system to cope with completely unanticipated situations. In short, AI has no common sense and we simply can’t store enough examples to handle every possible situation. Further, AI, unlike humans, is unable to merge information from multiple senses. So while it might be possible to stitch language and image processing applications together, researchers have not found a way to integrate them in the same seamless way that a child integrates vision, language, and hearing.

For today’s AI to advance to something approaching real human-like intelligence, it must have three essential components of consciousness: an internal mental model of surroundings with the entity at the center; a perception of time which allows for a prediction of future outcome(s) based on current actions; and an imagination so that multiple potential actions can be considered and their outcomes evaluated and chosen. Just like the average three-year-old child, it must be able to explore, experiment, and learn about real objects, interpreting everything it knows in the context of everything else it knows.

To get there, researchers must shift their reliance on ever-expanding datasets to a more biologically plausible system modelled on the human brain, with algorithms that enable it to build abstract “things” with limitless connections and context.

While we know a fair amount about the brain’s structure, we still don’t know what fraction of our DNA defines the brain or even how much DNA defines the structure of its neocortex, the part of the brain we use to think. If we presume that generalized intelligence is a direct outgrowth of the structure defined by our DNA and that structure could be defined by as little as one percent of that DNA, though, it is clear that AGI emergence depends not on more computer power or larger data sets but on what to write as the fundamental AGI algorithms.

With that in mind, it seems highly likely that a broader context that is actually capable of understanding and learning gradually could emerge if all of today’s AI systems could be built on a common underlying data structure that allowed their algorithms to begin interacting with each other. As these systems become more advanced, they would slowly begin to work together to create a more general intelligence that approaches the threshold for human-level intelligence, enabling AGI to emerge. To make that happen, though, our approach must change. Bigger and better data sets don’t always win the day.

The post ChatGPT and Artificial General Intelligence: The Illusion of Understanding appeared first on ML Conference.

Google Bard is an experimental, conversational artificial intelligence based on the Large Language Model LaMDA. This is how Google describes the technology on the Google blog. It is intended to make it possible to gain access to complex information and facts in dialog form. An example from the developer’s blog Preparing discoveries from the James Webb Space Telescope for a 9-year-old child.

What distinguishes Google’s Bard from OpenAI’s ChatGPT is that Bard can fall back on current information from the Internet right from the start. Since ChatGPT is based on the GPT-3.5 model, it only knows about texts written up to mid-2021 and can only fall back on these. Our expert Pieter Buteneers told us in an interview about ChatGPT that it is “basically a summary of the Internet until the end of 2021”. Adding up-to-date information from the Internet is the next big step, he said, “If ChatGPT can search the Internet, Google and Stack Overflow will be history. It makes sense to assume that will happen.” That’s the path Google is now apparently taking with Bard.

Microsoft, which is said to have recently invested 10 billion dollars in OpenAI, will likely announce soon that ChatGPT will be integrated into the search engine Bing. Currently, Google is and remains largely unrivaled in the search engine business. In December 2022, Google’s search engine had a market share of just under 85%, followed in second place by Microsoft’s Bing, with just under 9%. With the new developments, however, some movement could occur, depending on who succeeds in implementing the respective AI better.

Google’s conversational AI Bard is currently not yet available to the public, but only to a select group of “trustful testers”. In the coming weeks, the AI will be made available to a wider audience. To what extent the AI will be usable for free remains to be seen. ChatGPT has only recently introduced a payment model that promises better access and shorter loading times for $20 per month.

The models: GPT and LaMDA

An AI is only as good as the model it is based on. OpenAI’s GPT model (“Generative Pretrained Transformer”), on which ChatGPT is based, has been in development for some time. GPT-3 was introduced in mid-2020 and is a Large Language Model (LLM) that was initially designed to complete texts. In an interview, Christoph Henkelmann told us more about GPT:

“GPT stands for “Generalised Pretraining for Transformers.”GPT is a family of architectures and various resulting models. Many neural networks follow this architectural pattern. GPT models are trained to simply complete text. They perform the same functions as old Nokia phones with T9. GPT attempts to predict what the next text block will be as you type. To train GPT, you collect vast amounts of text, for example, with web scraping. Then you let a neural network make predictions, sometimes for months.”

This LLM was fine-tuned to dialog progressions, eventually modeling it into ChatGPT.

Google’s LaMDA (“Language Model for Dialogue Applications”) was released in May 2021. It is an LLM that was trained on dialog progressions from the beginning. Earlier in 2018, Google presented another model called Bert (“Bidirectional Encoder Representations from Transformers”). Similar to the GPT model, Bert is based on transformers, a technology that uses a neural network architecture to fill in cloze texts: “(…) with BERT, the big breakthrough came because developers trained the model to fill in missing text parts. If you want to fill in a cloze, first you have to understand what the text is about.” That’s what Pieter Buteneers told us in an interview about Dall-E and image-generating AI.

Buteneers continues, “The big breakthrough that BERT brought was to take the old models that work well on a CPU and put them into a new architecture that computes fast on a GPU. That’s how we were able to make these huge leaps forward. Since 2018, these models have gotten bigger and smarter, so you can do more and more exciting things with them.”

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What is Google doing in the AI field?

With all the excitement about ChatGPT, there hasn’t been much talk about the progress Google has already made in the AI field, especially with regard to LLMs. As previously mentioned, Google is at the forefront of natural language processing with BERT and LaMDA.

Google is also active in the area of text-to-image AIs with Imagen and even claims to produce better results than OpenAI’s Dall-E when measured against human evaluation. Work is also underway to extend Imagen to video generation. Music is also no longer out of reach for Google’s AI. With MusicLM, Google Research introduces artificial intelligence that can generate music based on text input. There is no accounting for taste, but the technical implementation is highly exciting.

Considering how incredibly versatile Google is in the artificial intelligence field, it’s fair to ask how the tech giant seems to have been overtaken by OpenAI. Not that OpenAI is a small software company or an underdog. Its market value is in the tens of billions.

However, due to Google’s dominance for years, you can still wonder how it came to this. Our expert Pieter Buteneers also told us in an interview: “I know from my contacts at Google Deep Mind that there was a bit of a crisis in the team there when GPT-3 was released. They were desperately trying to find a solution as quickly as possible. And that was just GPT-3. But with ChatGPT, I think the whole company is shaking, not just Google Deep Mind, but all of Google, or at least those who understand what ChatGPT can do.” CNBC also reported panic in Google’s executive suite.

The CNBC article reported Google’s concern of losing their reputation, which is understandable. As linguistically convincing as OpenAI’s ChatGPT may be, the AI can reproduce misinformation. This is due to the model itself, says Christoph Henkelmann: “The answer is not the result of a conclusion, but the result of training. ChatGPT has seen texts, (…) and learned from them. The closer my wording was to the ‘read’, the better the answer was.” Henkelmann went on to tell us, “It doesn’t even understand that it’s answering a question. It simply wants to complete or continue the text. And that’s the problem: GPT will continue the text even if it doesn’t have an answer. The model is not able to reflect that. There will always be a text that is statistically a likely answer for GPT, and so it can’t state, ‘I don’t know something.’”

For Google, which primarily earned its reputation as an accurate and precise search engine, this is a major problem. Additionally, certain limits have to be imposed on an AI like ChatGPT, which, for example, prohibit it from playing out or generating certain information. Therefore, ChatGPT does not answer some queries at all if they violate the guidelines. If you ask the AI how to build a bomb, you will (fortunately) not get the answer you were hoping for.

And that doesn’t even touch on the issue of sexist, racist, or other discriminatory and misanthropic outputs of an AI, which in part only reproduces what it already has. Microsoft had a similar experience in 2016 with its chatbot Tay.

We experienced similar concerns during the release of OpenAI’s image AI Dall-E, when accusations were made that the release was irresponsible because restrictions were missing. Image generation by artificial intelligences in particular presents the problem of ethical and moral boundaries in the truest sense of the word in a very graphic way. The concern that an AI trained and managed by Google could produce reprehensible results of whatever kind would be reputationally damaging is justified.

However, it is much more likely that the new chatbots are a problem for Google’s business model, which is largely financed by advertising revenue. It’s not impossible that Bard or even ChatGPT as a Bing implementation could phase out advertising. Classic search engine advertising is based on clicks, and these could fail to materialize if the AI response is enough for users.

If the answer to a question is resolved on Google itself, there is much less motivation for end users to visit or look at the actual source. This is not only a problem for Google, which promises to drive traffic to the advertisers’ site through ad placement, but also for the operators of websites themselves, whose content may now only be rendered by an LLM. The fear is that the source will not be visited.

Of course, Google already displays answers to certain questions directly on their site. But it is clear that the search engine advertising model, at least in its current form, is no longer sacrosanct and will undergo a transformation if the search engine operators want to implement AI.

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What’s next?

The release of ChatGPT, which hit many experts unexpectedly, has marked a new battleground for the tech giants. This is not a reason to panic yet, nor is it a reason to worry about an AI revolution. However, the developments are likely to be especially exciting in terms of how we search for information on the Internet in the future and how we are presented with the results. It remains to be seen what this means for the major search engine operators’ business models, content optimization, and SEO.

At least for us as viewers, turning the search engine business upside down on the way to artificial general intelligence (AGI) is pretty interesting. We will see what long-term challenges and improvements for users will result from this little AI war.

As long as Bard is not yet publicly available, it’s hard to evaluate what the AI is really capable of. We don’t know yet if it is as user-friendly as ChatGPT. If the rumors are true, we should learn more about possible implementation of ChatGPT in Bing very soon. Until then, we will have to be patient, but we can be excited about the very near future.

Be part of this exchange with leading experts at the heart of current AI developments, at MLCon Munich 2023.

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Time and again, the term disruptive technology is used to refer to such a new milestone in technological progress. Often, however, the term is used inflationary, and mainly by business-minded and marketing-savvy entrepreneurs. Rarely are the products or services mentioned in the same breath real disruptions. ChatGPT may be different.

Because such technologies, which provoke a collective change of mind, a great self-questioning, and imitation, are not self-promoters’ intention. They are real and have a very direct influence on our actions, our thinking, and can cause the upheaval of an entire industry. They reach the very core of our collective experience and existence. But by definition, these disruptors are not single technologies, but rather processes.

We are currently experiencing the beginning of such a process, the beginning of radical change caused by the advancement of key technologies in the field of artificial intelligence and machine learning. The leading example of this new wave of technologies, of course, is ChatGPT by OpenAI, an extremely user-oriented exemplification of artificial intelligence, that for many, marks a turning point in the advancement of artificial intelligence or even a milestone on our way to artificial general intelligence (AGI). ChatGPT has surprised even veteran experts and has shaken their confidence in predicting future developments. 

But today, such technologies are not only visible to experienced AI and ML specialists, but to complete laymen, and especially to large tech companies, which sense their opportunity to be at the core of the next big thing and at the heart of the next megatrend. While this is of course driven by monetary incentives and business decisions, it is at its core about staying ahead of the curve and becoming part of and shaping a technological revolution that will change the lives of generations to come.

And these developments are not happening in the tech bubble exclusively. Mainstream news outlets have been reporting on exciting but also concerning developments in artificial intelligence for some time now. And even though you can accuse some of them of jumping on a hype train, the wheels are not only in motion, but moving billions of dollars, resources, and people in a very short time:

Microsoft is investing $10 billion in OpenAI, Google’s parent company Alphabet is bringing back its two founders, and Meta is shifting more and more budget toward their AI department. Artificial intelligence has long ceased to be a technical gimmick of hermitical developers or even the pipedream of a generation of science fiction-influenced entrepreneurs. Artificial intelligence is currently electrifying an entire industry and, beyond that, modern societies (not to mention the impact on education and other public sectors). 

This dynamic inspires us to further develop our ML Conference as a space where like-minded people can exchange ideas. Not only to advance their careers in the field of AI and ML but to push themselves, their projects, or their company to the edge of innovation, to develop great and practical innovations that can change the course of a business or redefine strategic goals. 

What we see exemplified in ChatGPT is a collection of different technologies, strategies, and models that can be applied in almost every department, no matter how mundane. Of course, ChatGPT is more than the sum of simple and available technologies. But at the same time, it is far from being a magical black box accessible only to a select few. As with many key technologies, success is not only reserved for the brilliant few minds of our time, but also for the people who work on implementing them in everyday areas, who embrace the challenges ahead of us and tap into the seemingly inexhaustible possibilities to change our lives for the better. 

Sufficient application of ML and AI can range from fine-tuning models by ML developers to prompt engineering, the art of designing on-target text inputs that drive your machine learning model to optimal performance. With prompt engineering, the goal is to develop the perfect inputs to deliver the results your application or business needs. People implementing those techniques, fine-tuning models, and developing new and creative ways to work with new technologies are the real disruptors.

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With MLCon, we do not only want to do our part in this democratization of technologies. We want to introduce you to the most essential technologies, methods, and tools that can move you, your team and your company forward. We will help pave the way for responsible AI use and through workshops, sessions, and talks from our experts, help spread all currently available information on the topic.

The question of the future will not be how much of a part an AI plays in a particular product or service. The question of the future will not be how certain aspects of our daily work can be abbreviated or simplified through the clever use of various technologies. The question of the future will be which tasks will be taken over by AI completely, how that shapes our society, and what that means for us as humans.

Be part of this exchange with leading experts at the heart of current AI developments, at MLCon Munich 2023.

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The AMA sessions are the place where your topics and your questions take centre stage – and the ML experts are happy to answer and discuss with you.

We look forward to a lively exchange of ideas!

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Opening Keynote:
Incorporating Machine Learning into a business strategy opens up fascinating new possibilities, but is anything but simple. We have seen far too many failed ML projects or prototypes that had no impact on the business. At the same time, if ML projects are approached the right way and with wisely chosen means, the opportunities for innovative digital products are limitless.In this keynote, Christoph Henkelmann and Cristoph Windheuser will talk about how to develop a viable roadmap for your ML product, which know-how is needed, which methodologies are helpful, what technology choices must be made, and how to manage ML in production.

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In this study we used Biomedical data/information that relates to human health. We acquired such data for monitoring specific pathological /physiological states for the purposes of diagnosis and evaluating therapy. The data were used for decoding and eventual modeling of specific biological systems. The acquisition of the study results from the Instrumentation at the molecular/cell level, or a systemic or organ level, Medical Imaging – Mobile/portable/wearable devices – Electronic health record. Automated analysis is essential with ever-increasing volume, variety and velocity of data and the Machine Learning Classification usually aims at assigning objects to one of a pre-specified set of classes based solely on a vector of measurements taken on these objects. The applications of the study is developing a decision support system assigning a diagnosis among several possible diagnoses and building models to predict a prognosis based on data from analysis of many biomarkers. In the study we discovered the CLASSIFICATION OF RETINAL DISEASES FROM OCT SCANS USING CONVOLUTIONAL NEURAL NETWORKS. The study used coherent light to capture micrometer-resolution – Two- and three-dimensional images from within optical scattering media (e.g., biological tissue). The study also conducted the CLASSIFICATION OF FOCAL AND NON-FOCAL EEG SIGNALS IN VMD- DWT DOMAIN USING ENSEMBLE STACKING. More than 60 million people suffer from epilepsy, and 80% of these are from developing countries. Despite the availability of anti-epileptic drugs, 25% of the patients do not respond to the drugs, thus to avoid cognitive and physiological dysfunction even death, surgery is necessary. The surgery is invasive and the epileptogenic focus of the brain is needed to be removed and thus the Identification of such areas with high precision is very important.

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Incorporating Machine Learning into a business strategy opens up fascinating new possibilities, but is anything but simple. We have seen far too many failed ML projects or prototypes that had no impact on the business. At the same time, if ML projects are approached the right way and with wisely chosen means, the opportunities for innovative digital products are limitless.

The MLCon Strategy Day enables you to develop a viable roadmap for your ML project or review existing roadmaps in collaboration with the experts. It will provide you with the knowledge you need to be successful with your ML strategy!

Topic overview:

• Welcome & introduction
• Motivation: goals and benefits of ML for your business
• Ideation: ML product development
• Data: data sources & integration, data streaming
• Know-how: important roles and functions in the team
• Make or buy: in-house development or cloud-based ML solution?
• Methodology: lean and agile
• MLOps: enhance and optimise ML products in production 

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