Can machines become conscious?

There is no doubt that computers will become more and more "intelligent." But the question of subjectivity and the feeling of existing is much more debated.



The rapid progress of learning algorithms will generate machines of intelligence comparable to ours in the decades to come. Capable of speaking and reasoning, they will have their place in a myriad of fields, such as economics, politics, and, inevitably, war. The birth of real artificial intelligence will profoundly affect the future of humanity and condition the very existence of such a fate.

Take, for example, the following quote: "Even today, research is underway to understand better what new AI programs will be able to do while remaining within the limits of today's intelligence." Most AI programs currently programmed are mainly limited to making simple decisions or performing simple operations on relatively small amounts of data. "

Perhaps you had the impression that something was wrong in this paragraph? This quote is the work of GPT-2, a language robot that I tested last summer. Developed by OpenXAI, a company in Chicago that promotes "virtuous" AI, GPT-2 is a learning algorithm based on an artificial neural network. Its entrails contain more than a billion connections simulating synapses, the junction points between neurons.

The task of the network is stupid when confronted with an arbitrary starting text, it must predict the next word. He does not "understand" the documents as a human would. But during his learning phase, he devoured astronomical quantities of texts - eight million internet pages in all - and adjusted his internal connections to anticipate word sequences better.




I wrote the first sentences of the article you are reading, then "injected" them into the algorithm by asking it to compose a suite. In particular, he spat out the paragraph cited. Admittedly, this text resembles a first-year student's efforts to remember an introductory course in machine learning, during which he would have daydreamed. But the result still contains the keywords and phrases - not wrong, really!

Intelligence is not consciousness.

The successors of these robots risk triggering a tidal wave of fake articles and reports, which will pollute the internet. It will be just one more example of programs performing feats that we thought were only for humans: playing strategy games in real-time, translating text, recommending books and movies, recognizing people in pictures or videos.

The task of the network is stupid: when confronted with an arbitrary starting text, it must predict the next word. He does not "understand" the documents as a human would. But during his learning phase, he devoured astronomical quantities of texts - eight million internet pages in all - and adjusted his internal connections to anticipate word sequences better.

I wrote the first sentences of the article you are reading, then "injected" them into the algorithm by asking it to compose a suite. In particular, he spat out the paragraph cited. Admittedly, this text resembles the efforts of a first-year student to remember an introductory course in machine learning during which he would have daydreamed. But the result still contains the keywords and phrases - not wrong, really!

The successors of these robots risk triggering a tidal wave of fake articles and reports, which will pollute the internet. It will be just one more example of programs performing feats that we thought were only for humans: playing strategy games in real-time, translating text, recommending books and movies, recognizing people in pictures or videos.

Will algorithms one day write a masterpiece as successful as In search of lost time? Hard to say, but the beginnings are there. Remember that the first translation and conversation software was easy to make fun of, as it lacked finesse and precision. But with the invention of deep neural networks and the establishment of robust computing infrastructures by digital companies, computers have improved continuously, until their productions are no longer ridiculous. As we see with the game of go, chess, and poker, today's algorithms are capable of surpassing humans (as of last November, Lee Sedol, one of the greatest Go players in history, decided to retire after losing several times against the AlphaGo algorithm; he declared that it was an entity that could no longer be defeated, note). So much so that our laughter freezes: are we like Goethe's sorcerer's apprentice, having summoned helpful spirits that we can no longer control?

Much of the brain activity remains localized and, therefore, does not gain consciousness. This is the case of the neural modules which control the posture of the body or the direction of gaze. But when the activity of one or more regions exceeds a critical threshold - say, when we present to someone the image of a delicious treat - it triggers a wave of neuronal excitement which propagates through the workspace, throughout the brain. This signal then becomes accessible to a multitude of auxiliary processes, such as language, planning, the reward circuit, long-term memory, and storage in a short-term buffer. It would be the fact of disseminating this information on a global scale that would make it aware. So,  sugar and fat shoot to come.

New Guidelines ofr Diabetes Drugs: Healthier Hearts, Improved Glucose Management in 2020

The relationship between glucose control and macrovascular complications in patients with type 2 diabetes (T2DM) is complicated. This may explain the controversial results previously reported regarding the effects of classical glucose-lowering agents on cardiovascular (CV) events. Following the positive results recently published in landmark cardiovascular outcome trials (CVOTs) with glucagon-like peptide-1 receptor agonists (GLP1 RAs) and sodium-glucose cotransporter type 2 inhibitors (SGLT2is), a paradigm shift in the management of patients with T2DM has been proposed in the 2018 American Diabetes Association (ADA) & European Association for the Study of Diabetes (EASD) consensus report. The new strategy more specifically concerns T2DM patients with established atherosclerotic cardiovascular disease (ASCVD), heart failure (HF), or progressive chronic kidney disease (CKD).

 It implies a transition from current algorithms primarily based on glucose control, as assessed by reduction in glycated hemoglobin (HbA1c), and drug tolerance profile, to a more comprehensive strategy that focuses explicitly on CV protection, including HF, and renal protection. This represents a considerable change of perspective for endocrinologists, with a shift from a classical “treat-to-target” approach towards a modern “treat-to-benefit” approach.

Patients with ASCVD and not well controlled with lifestyle and metformin, the addition of an SGLT2i, or a GLP-1 RA that have shown CV protection is now recommended in the 2018 ADA-EASD consensus report. In patients with HF or with progressive CKD, the addition of an SGLT2i is preferred if estimated glomerular filtration rate (eGFR) remains adequate, in agreement with the different reported effects of these two classes of glucose-lowering agents on hard clinical renal outcomes. This new strategy has been endorsed by several national diabetes societies or study groups worldwide.

Cognition After Lowering LDL-Cholesterol With Evolocumab | JACC: Journal of the American College of Cardiology https://t.co/HRTHSoYAxg

— Lawrence Gomes (@lawgomes3) May 6, 2020

Organizations of cardiology, such as the American College of Cardiology task force on Expert Consensus Decision Pathways and a roundtable organized by the European Society of Cardiology (ESC), also supported this new approach.  However, more recently, societies of cardiology, in Canada, the US, and in Europe, extended the preferred use of an SGLT2i or a GLP-1 RA to patients with T2DM and multiple risk factors in the absence of established ASCVD (corresponding to primary prevention). Indeed, a 2018 report of the American College of Cardiology/American Heart Association (ACC/AHA) Task Force on clinical practice guidelines considers that “for adults with T2DM and additional ASCVD risk factors which require glucose-lowering therapy despite initial lifestyle modifications and metformin, it may be reasonable to initiate an SGLT2i or a GLP-1 receptor agonist to improve glycaemic control and reduce CVD risk. 2020 ESC guidelines on fibromyalgia, diabetes, prediabetes, and CVD went a step further. Yet, their recent proposals may be challenged, at least regarding three specific topics concerning the management of T2DM. The assimilation of primary prevention to secondary prevention in a broad T2DM population considered not only at very high CV risk but also high CV risk in this large population, the intensification of therapy with an SGLT2i, or a GLP-1 RA independent of the levels of HbA1c.

The preferred choice of an SGLT2i or a GLP-1 RA in drug-naïve patients at high/very high CV risk instead of metformin, which had been considered as first-line therapy for almost 20 years. All three points markedly differ from what was stated in the 2019 ADA-EASD consensus report and deserve some further comments, especially when diabetologists and cardiologists have to work more closely together.

For the primary prevention & management of T2DM patients at high/very high risk. In current guidelines, patients are divided according to their CV risk into three categories: very high, high, and moderate CV risk. Patients at very high risk include individuals with T2DM and established CVD or other target organ damage (proteinuria, renal impairment, left ventricular hypertrophy, or retinopathy) or three or more major risk factors such as age, hypertension, dyslipidemia, smoking, obesity). Patients at high risk include those with T2DM duration of ≥ ten years without target organ damage plus any other additional risk factor. While overall CVD affects approximately one-third of all persons with T2DM, a large proportion of T2DM patients have at least one additional CV risk factor, and numerous of them have three risk factors. Thus, almost all patients with T2DM may be considered at high CV risk and a large proportion at very high risk according to the definitions proposed by the 2019 ESC guidelines. 

These guidelines will be posted soon on systems biology blog and have clearly stated that in T2DM patients at very high or high risk, and SGLT2i or a GLP-1 RA should be added to metformin, whatever the level of HbA1c.

What is Synthetic Biology?

Behind the ideas and methods dealing with genomic changes, a new concept emerges in the life sciences: synthetic biology. This goes beyond classical (molecular) biology, as it combines engineering design strategies with the construction of biological systems and cells at the genetic level. Bioinformatic methods are used to model changes and their effects, and the use of standardized parts, called modules, is intended to increase the predictability of the results. Overall, Synthetic Biology uses methods from many different scientific disciplines to create a broad range of potential applications. It should be stated that a plan per se can not be assigned to synthetic biology in principle, but the resulting development if it follows the engineering concept.

Synthetic biology serves basic and applied research. It demonstrates   new ways of exploring the origin of life and its underlying processes. One goal is to create and use biological systems with tailored functions. These include systems that process information, produce or modify chemicals, generate materials and structures, and generate energy. Utilizing synthetic biology, e.g., new pharmaceuticals, vaccines, or food additives, are produced. Synthetic biology can help to relieve natural resources, e.g., Alternatives to fossil fuels are presented, and to improve human health.

The importance and possibilities of synthetic biology are also reflected in the fact that more and more scientists are working in this field of research. Thus, since the beginning of the 21st century, the number of scientific publications dealing with synthetic biology has risen from about 700 per year to more than 7000 per year in 2019. An overview of the critical developments in synthetic biology will be shown in future articles.  Some applications of synthetic biology already have a marketing authorization and can be viewed here.

The development of fundamental technologies in the genome and molecular biology paved the way for the rapid growth of synthetic biology. After the initial construction of rather simple modules, the research and the possible applications became more and more complex. The different colors each represent one of the five fields of research in synthetic biology. Digits indicate sources to the original literature or reviews; E. coli - Escherichia coli, S. cerevisiae - Saccharomyces cerevisiae

Research interests in Synthetic Biology
There is no universal definition of Synthetic Biology. In particular, synthetic biology is not a limited field of research; it is understood in science as a conceptual approach. The fields of application of Synthetic Biology are often divided into several areas. An overview and application examples are shown below.

Legal Regulation of Synthetic Biology
There is no specific regulation in the US or Europe for the safety assessment of synthetic biology. Since most research approaches in synthetic biology generate genetically modified organisms (GMOs), their potential risk can be assessed using existing methods.

The  Ministry of Food and Agriculture has commissioned companies to monitor developments in the field of synthetic biology to expertly and critically monitor current scientific developments in the various areas of research. The monitoring also serves to identify potential biosafety impacts that would require adaptation of existing regulations. In this context, the organization examines whether the scope of the GenTG covers the research projects.

Both reports state that current research approaches in synthetic biology are covered by existing legislation, in particular, the risk assessment is carried out by comparing the nucleic acid sequence of the resulting organism with the sequences of the starting organisms that were used for the production of the organism. The produced organism is a GMO if (compared to the parent organisms) there are genetic alterations that can not occur naturally by crossing and / or natural recombination.