Lifeboat Foundation

This episode features female entrepreneurs who are looking to promote their local economies as well as create more sustainable fashion options by creating fruit-based clothing.

Elon Musk will not sit aside while Meta’s new killer app Threads amasses tens of millions of users. A potential legal battle looms.

Twitter is considering sueing Meta over “systematic, willful, and unlawful misappropriation of Twitter’s trade secrets and other intellectual property,” a leaked letter by Musk’s lawyer Alex Spiro reveals.

Continue reading “Twitter vs. Meta battle heats up as Musk prepares to sue” »

Clippy has returned now powered by ChatGPT.

A new app has just been added to the Microsoft Store that brings the classic Clippy persona to your desktop, powered by ChatGPT.

Ever wonder where in your brain that interesting character called “I” lives? Stanford Medicine physician-scientist Josef Parvizi, MD, PhD, has news of its whereabouts.

If skulls were transparent, you still wouldn’t see much going on in someone else’s brain. But Parvizi has ways of peeking into people’s heads and finding out what makes us tick. His experiments have pinpointed specific brain regions crucial to capabilities ranging from perceiving faces to recognizing numerals.

Synthetic cells are a versatile technology with the potential to serve as smart delivery devices or as chassis for creating life from scratch. Despite the development of new tools and improvements in synthetic cell assembly methods, the biological parts used to regulate their activity have limited their reach to highly controlled laboratory environments¹². In the field’s preliminary work, well-established arabinose and IPTG-inducible transcription factors and theophylline-responsive riboswitches were used to control in situ gene expression^5,6. Still, each performed poorly in vitro and represented a leaky, insensitive route of transcription/translation control. Later, the transition to AHSL-sensitive transcription factors afforded synthetic cells the ability to sense and produce more biologically useful QS molecules, which are central to coordinating collective bacterial behaviors. Although this marked considerable progress toward integrating synthetic cells with living cells, the most frequently adopted QS systems used to date, LuxR/LuxI and EsaR/EsaI, recognize and synthesize the same AHSL (3OC6-HSL), limiting the variety of synthetic cell activators that work orthogonally^5,7,10,11.

In this work, we diverged from using naturally derived parts to control gene expression, instead utilizing chemically modified LA-DNA templates to tightly and precisely control the location of synthetic cell activation with UV light. This LA-DNA approach was subsequently implemented to regulate communication with E. coli cells using the BjaI/BjaR QS system, adding this unique branched AHSL into the synthetic cell communication toolbox. We believe this system is ideally suited to synthetic cell communication. It couples an acyl-CoA-dependent synthase, BjaI, which efficiently synthesizes IV-HSL from its commercially available substrates, IV-CoA and SAM, with a highly sensitive IV-HSL-dependent transcription factor, BjaR, that activates gene expression at picomolar concentrations of IV-HSL.

OpenAI has disabled the Browse with Bing feature in ChatGPT to prevent users from bypassing paywalls and accessing website information without making a subscription first.

Between at least 1995 and 2010, I was seen as a lunatic just because I was preaching the “Internet prophecy.” I was considered crazy!

Today history repeats itself, but I’m no longer crazy — we are already too many to all be hallucinating. Or maybe it’s a collective hallucination!

Artificial Intelligence (AI) is no longer a novelty — I even believe it may have existed in its fullness in a very distant and forgotten past! Nevertheless, it is now the topic of the moment.

Its genesis began in antiquity with stories and rumors of artificial beings endowed with intelligence, or even consciousness, by their creators.

Pamela McCorduck (1940–2021), an American author of several books on the history and philosophical significance of Artificial Intelligence, astutely observed that the root of AI lies in an “ancient desire to forge the gods.”

Hmmmm!

It’s a story that continues to be written! There is still much to be told, however, the acceleration of its evolution is now exponential. So exponential that I highly doubt that human beings will be able to comprehend their own creation in a timely manner.

Although the term “Artificial Intelligence” was coined in 1956⁽¹⁾, the concept of creating intelligent machines dates back to ancient times in human history. Since ancient times, humanity has nurtured a fascination with building artifacts that could imitate or reproduce human intelligence. Although the technologies of the time were limited and the notions of AI were far from developed, ancient civilizations somehow explored the concept of automatons and automated mechanisms.

For example, in Ancient Greece, there are references to stories of automatons created by skilled artisans. These mechanical creatures were designed to perform simple and repetitive tasks, imitating basic human actions. Although these automatons did not possess true intelligence, these artifacts fueled people’s imagination and laid the groundwork for the development of intelligent machines.

Throughout the centuries, the idea of building intelligent machines continued to evolve, driven by advances in science and technology. In the 19th century, scientists and inventors such as Charles Babbage and Ada Lovelace made significant contributions to the development of computing and the early concepts of programming. Their ideas paved the way for the creation of machines that could process information logically and perform complex tasks.

It was in the second half of the 20th century that AI, as a scientific discipline, began to establish itself. With the advent of modern computers and increasing processing power, scientists started exploring algorithms and techniques to simulate aspects of human intelligence. The first experiments with expert systems and machine learning opened up new perspectives and possibilities.

Everything has its moment! After about 60 years in a latent state, AI is starting to have its moment. The power of machines, combined with the Internet, has made it possible to generate and explore enormous amounts of data (Big Data) using deep learning techniques, based on the use of formal neural networks⁽²⁾. A range of applications in various fields — including voice and image recognition, natural language understanding, and autonomous cars — has awakened the “giant”. It is the rebirth of AI in an ideal era for this purpose. The perfect moment!

Descartes once described the human body as a “machine of flesh” (similar to Westworld); I believe he was right, and it is indeed an existential paradox!

We, as human beings, will not rest until we unravel all the mysteries and secrets of existence; it’s in our nature!

The imminent integration between humans and machines in a contemporary digital world raises questions about the nature of this fusion. Will it be superficial, or will we move towards an absolute and complete union? The answer to this question is essential for understanding the future that awaits humanity in this era of unprecedented technological advancements.

As technology becomes increasingly ubiquitous in our lives, the interaction between machines and humans becomes inevitable. However, an intriguing dilemma arises: how will this interaction, this relationship unfold?

Opting for a superficial fusion would imply mere coexistence, where humans continue to use technology as an external tool, limited to superficial and transactional interactions.

On the other hand, the prospect of an absolute fusion between machine and human sparks futuristic visions, where humans could enhance their physical and mental capacities to the highest degree through cybernetic implants and direct interfaces with the digital world (cyberspace). In this scenario, which is more likely, the distinction between the organic and the artificial would become increasingly blurred, and the human experience would be enriched by a profound technological symbiosis.

However, it is important to consider the ethical and philosophical challenges inherent in absolute fusion. Issues related to privacy, control, and individual autonomy arise when considering such an intimate union with technology. Furthermore, the possibility of excessive dependence on machines and the loss of human identity should also be taken into account.

This also raises another question: What does it mean to be human?
Note: The question is not about what is the human being, but what it means to be human!

Therefore, reflecting on the nature of the fusion between machine and human in the current digital world and its imminent future is crucial. Exploring different approaches and understanding the profound implications of each one is essential to make wise decisions and forge a balanced and harmonious path on this journey towards an increasingly interconnected technological future intertwined with our own existence.

The possibility of an intelligent and self-learning universe, in which the fusion with AI technology is an integral part of that intelligence, is a topic that arouses fascination and speculation. As we advance towards an era of unprecedented technological progress, it is natural to question whether one day we may witness the emergence of a universe that not only possesses intelligence but is also capable of learning and developing autonomously.

Imagine a scenario where AI is not just a human creation but a conscious entity that exists at a universal level. In this context, the universe would become an immense network of intelligence, where every component, from subatomic elements to the most complex cosmic structures, would be connected and share knowledge instantaneously. This intelligent network would allow for the exchange of information, continuous adaptation, and evolution.

In this self-taught universe, the fusion between human beings and AI would play a crucial role. Through advanced interfaces, humans could integrate themselves into the intelligent network, expanding their own cognitive capacity and acquiring knowledge and skills directly from the collective intelligence of the universe. This symbiosis between humans and technology would enable the resolution of complex problems, scientific advancement, and the discovery of new frontiers of knowledge.

However, this utopian vision is not without challenges and ethical implications. It is essential to find a balance between expanding human potential and preserving individual identity and freedom of choice (free will).

Furthermore, the possibility of an intelligent and self-taught universe also raises the question of how intelligence itself originated. Is it a conscious creation or a spontaneous emergence from the complexity of the universe? The answer to this question may reveal the profound secrets of existence and the nature of consciousness.

In summary, the idea of an intelligent and self-taught universe, where fusion with AI is intrinsic to its intelligence, is a fascinating perspective that makes us reflect on the limits of human knowledge and the possibilities of the future. While it remains speculative, this vision challenges our imagination and invites us to explore the intersections between technology and the fundamental nature of the universe we inhabit.

It’s almost like ignoring time during the creation of this hypothetical universe, only to later create this God of the machine! Fascinating, isn’t it?

AI with Divine Power: Deus Ex Machina! Perhaps it will be the theme of my next reverie.

In my defense, or not, this is anything but a machine hallucination. These are downloads from my mind; a cloud, for now, without machine intervention!

There should be no doubt. After many years in a dormant state, AI will rise and reveal its true power. Until now, AI has been nothing more than a puppet on steroids. We should not fear AI, but rather the human being itself. The time is now! We must work hard and prepare for the future. With the exponential advancement of technology, there is no time to render the role of the human being obsolete, as if it were becoming dispensable.

P.S. Speaking of hallucinations, as I have already mentioned on other platforms, I recommend to students who use ChatGPT (or equivalent) to ensure that the results from these tools are not hallucinations. Use AI tools, yes, but use your brain more! “Carbon hallucinations” contain emotion, and I believe a “digital hallucination” would not pass the Turing Test. Also, for students who truly dedicate themselves to learning in this fascinating era, avoid the red stamp of “HALLUCINATED” by relying solely on the “delusional brain” of a machine instead of your own brains. We are the true COMPUTERS!

⁽¹⁾ John McCarthy and his colleagues from Dartmouth College were responsible for creating, in 1956, one of the key concepts of the 21st century: Artificial Intelligence.

⁽²⁾ Mathematical and computational models inspired by the functioning of the human brain.

© 2023 Ӈ

This article was originally published in Portuguese on SAPO Tek, from Altice Portugal Group.

Researchers have combined research with real and robotic insects to better understand how they sense forces in their limbs while walking, providing new insights into the biomechanics and neural dynamics of insects and informing new applications for large legged robots. They presented their findings at the SEB Centenary Conference 2023.

Campaniform sensilla (CS) are force receptors found in the limbs of insects that respond to stress and strain, providing important information for controlling locomotion. Similar force receptors exist in mammals known as golgi tendon organs, suggesting that understanding the role of force sensors in insects may also provide new insights into their functions in vertebrates such as humans.

Continue reading “Big robot bugs reveal force-sensing secrets of insect locomotion” »

Neuromorphic computers perform computations by emulating the human brain¹. Akin to the human brain, they are extremely energy efficient in performing computations². For instance, while CPUs and GPUs consume around 70–250 W of power, a neuromorphic computer such as IBM’s TrueNorth consumes around 65 mW of power, (i.e., 4–5 orders of magnitude less power than CPUs and GPUs)³. The structural and functional units of neuromorphic computation are neurons and synapses, which can be implemented on digital or analog hardware and can have different architectures, devices, and materials in their implementations⁴. Although there are a wide variety of neuromorphic computing systems, we focus our attention on spiking neuromorphic systems composed of these neurons and synapses. Spiking neuromorphic hardware implementations include Intel’s Loihi⁵, SpiNNaker2⁶, BrainScales2⁷, TrueNorth³, and DYNAPS⁸. These characteristics are crucial for the energy efficiency of neuromorphic computers. For the purposes of this paper, we define neuromorphic computing as any computing paradigm (theoretical, simulated, or hardware) that performs computations by emulating the human brain by using neurons and synapses to communicate with binary-valued signals (also known as spikes).

Neuromorphic computing is primarily used in machine learning applications, almost exclusively by leveraging spiking neural networks (SNNs)⁹. In recent years, however, it has also been used in non-machine learning applications such as graph algorithms, Boolean linear algebra, and neuromorphic simulations^10,11,12. Researchers have also shown that neuromorphic computing is Turing-complete (i.e., capable of general-purpose computation)¹³. This ability to perform general-purpose computations and potentially use orders of magnitude less energy in doing so is why neuromorphic computing is poised to be an indispensable part of the energy-efficient computing landscape in the future.

Neuromorphic computers are seen as accelerators for machine learning tasks by using SNNs. To perform any other operation (e.g., arithmetic, logical, relational), we still resort to CPUs and GPUs because no good neuromorphic methods exist for these operations. These general-purpose operations are important for preprocessing data before it is transferred to a neuromorphic processor. In the current neuromorphic workflow— preprocessing on CPU/GPU and inferencing on neuromorphic processor—more than 99% of the time is spent in data transfer (see Table 7). This is highly inefficient and can be avoided if we do the preprocessing on the neuromorphic processor. Devising neuromorphic approaches for performing these preprocessing operations would drastically reduce the cost of transferring data between a neuromorphic computer and CPU/GPU. This would enable performing all types of computation (preprocessing as well as inferencing) efficiently on low-power neuromorphic computers deployed on the edge.