Lifeboat Foundation

How do we solve the problem of job displacement? “The best way out is always through,” as Robert Frost said.

In the face of AI advancements, it’s time to double down on our uniquely human capabilities: imagination, anticipation, emotions and judgment—traits that machines cannot replicate.

AI has proven itself capable of tackling routine tasks within closed management systems but struggles when faced with open-ended problems requiring creativity and adaptability—a realm where humans reign supreme. Remember, there is more to work than simply executing tasks; there’s also vision-setting, team-building and innovation-driving. These areas are immune from automation because they require “the human touch.”

The idea of machines overcoming humans can be intrinsically related to conscious machines. Surpassing humans would mean replicating, reaching and exceeding key distinctive properties of human beings, for example, high-level cognition associated with conscious perception. However, can computers be compared with humans? Can computers become conscious? Can computers outstrip human capabilities? These are paradoxical and controversial questions, particularly because there are many hidden assumptions and misconceptions about the understanding of the brain. In this sense, it is necessary to first explore these assumptions and then suggest how the specific information processing of brains would be replicated by machines. Therefore, this article will discuss a subset of human capabilities and the connection with conscious behavior, secondly, a prototype theory of consciousness will be explored and machines will be classified according to this framework. Finally, this analysis will show the paradoxical conclusion that trying to achieve conscious machines to beat humans implies that computers will never completely exceed human capabilities, or if the computer were to do it, the machine should not be considered a computer anymore.

Keywords: artificial intelligence, information processing, cognitive computing, type of cognition, super machine, conscious machine, consciousness.

During many centuries, scientists and philosophers have been debating about the nature of the brain and its relation with the mind, based on the premise of an intrinsic dualism, typically called mind-body problem (Searle, 1990; Chalmers, 1995). Arguments take one form or another, however, most of them can be reduced to one kind of dualist or non-dualist view (Lycan and Dennett, 1993). The importance of these debates acquires even more relevance when the question is stated as the possibility to build machines which would be able to reproduce some human capabilities such as emotion, subjective experiences, or even consciousness.

Immigration to and living on Mars have often been themes in science fiction. Before these dreams can become reality, humanity faces significant challenges, such as the scarcity of vital resources like oxygen needed for long-term survival on the Red Planet. Yet, recent discoveries of water activity on Mars have sparked new hope for overcoming these obstacles.

Scientists are now exploring the possibility of decomposing water to produce oxygen through electrochemical water oxidation driven by solar power with the help of oxygen evolution reaction (OER) catalysts. The challenge is to find a way to synthesize these catalysts in situ using materials on Mars, instead of transporting them from the Earth, which is of high cost.

Artificial intelligence (AI) is poised to revolutionise the way we diagnose and treat illness. It could be particularly helpful for depression because it could make more accurate diagnoses and determine which treatments are more likely to work.

Some 20% of us will have depression at least once in our lifetimes. Around the world, 300 million people are currently experiencing depression, with 1.5 million Australians likely to be depressed at any one time.

Because of this, depression has been described by the World Health Organization as the single biggest contributor to ill health around the world.

China’s cyber-space regulator has unveiled draft measures that would make companies responsible for the data used to train generative artificial intelligence (AI) models, such as Midjourney and ChatGPT.

The tool — dubbed ‘AI-Descartes’ by the researchers — aims to speed up scientific discovery by leveraging symbolic regression, which finds equations to fit data.

Given basic operators, such as addition, multiplication, and division, the systems can generate hundreds to millions of candidate equations, searching for the ones that most accurately describe the relationships in the data.

Using this technique, the AI tool has been able to re-discover, by itself, fundamental equations, including Kepler’s third law of planetary motion; Einstein’s relativistic time-dilation law, and Langmuir’s equation of gas adsorption.

Any activity that requires comprehension and production in one or more modalities is considered a multimodal task; these activities can be extremely varied and lengthy. It is challenging to scale previous multimodal systems because they rely heavily on gathering a large supervised training set and developing task-specific architecture, which must be repeated for every new task. In contrast, present multimodal models have not mastered people’s ability to learn new tasks in context, meaning that they can do so with minimal demonstrations or instructions. Generative pretrained language models have recently shown impressive skills in learning from context.

New research by researchers from Beijing Academy of Artificial Intelligence, Tsinghua University, and Peking University introduces Emu2, a 37-billion-parameter model, trained and evaluated on several multimodal tasks. Their findings show that when scaled up, a multimodal generative pretrained model can learn similarly in context and generalize well to new multimodal tasks. The objective of the predict-the-next-multimodal-element (textual tokens or visual embeddings) is the only one used during Emu2’s training. This unified generative pretraining technique trains models by utilizing large-scale multimodal sequences, such as text, image-text pairs, and interleaved image-text video.

The Emu2 model is generative and multimodal; it learns in a multimodal setting to predict the next element. Visual Encoder, Multimodal Modeling, and Visual Decoder are the three main parts of Emu2’s design. To prepare for autoregressive multimodal modeling, the Visual Encoder tokenizes all input images into continuous embeddings, subsequently interleaved with text tokens. The Visual Decoder turns the regressed visual embeddings into a movie or image.

Surveys from business leaders show that they are now doing mass layoffs due to adoption of AI.

A recent survey of 750 business leaders reveals a growing trend: AI replacing jobs. In 2023, 37% of these leaders acknowledged AI-induced layoffs, with 44% expecting more in 2024. Major companies like Paytm and Google are at the forefront, integrating AI to enhance efficiency but at the cost of human jobs. Paytm’s recent layoffs post AI implementation and Google’s potential restructuring of its ad sales unit due to AI advancements highlight this shift.\
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#ai #job #layoffs \
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