Think Forward.

AI+Health: An Undelivered Promise

Tariq Daouda, 8 mon.
2 min read.

AI+Health: An Undelivered Promise

Tariq Daouda, 8 mon.
AI is everywhere, or so would it seems, but the promises made for Drug Discovery and Medicine are still yet to be fulfilled. AI seems to always spring from a Promethean impulse. The goal of creating a life beyond life, doing the work of gods by creating a new life form as Prometheus created humanity. From Techne to independent life, a life that looks life us. Something most people refer to as AGI today. This is the biggest blind spot of AI development. The big successes of AI are in a certain way always in the same domains: - Image Processing - Natural Language Processing The reason is simple, we are above all visual, talking animals. Our Umwelt, the world we inhabit is mostly a world of images and language, every human is an expert in these two fields. Interestingly, most humans are not as sound aware as they are visually aware. Very few people can separate the different tracks in a music piece, let alone identify certain frequencies or hear delicate compressions and distortions. We are not so good with sound, and it shows in the relatively less ground breaking AI tools available for sound processing. The same phenomenon explains why AI struggles to achieve in very complex domains such as Biology and Chemistry. At it's core, modern AI is nothing more than a powerful general way to automatically guess relevant mathematical functions describing a phenomenon from collected data. What statisticians call a *Model*. From this great power derives the domain chief illusion: because the tool is general, therefore the wielder of that tool can apply it to any domain. Experience shows that this thinking is flawed. Every AI model is framed between two thing: its dataset (input) and its desired output as represented by the loss function. What is important, what is good, what is bad, how should the dataset be curated, how should the model be adjusted. For all these questions and more, you need a deep knowledge of the domain, of the assumptions of the domain, of the technicalities of the domain, of the limitations that are inherent to data collection in that domain. Domain knowledge is paramount, because AI algorithms are always guided by the researchers and engineers. This I know from experience, having spent about 17 years closely working with biologists. Pairing AI specialists with domain specialist with little knowledge of AI also rarely delivers. A strategy that has been tested time and time again in the last 10 years. Communication is hard and slow, most is lost in translation. The best solution is to have AI experts that are also experts in the applied domain, or domain experts that are also AI experts. Therefore the current discrepancies we see in AI performances across domains, could be layed at the feet of universities, and there siloed structures. Universities are organized in independent departments that teach independently. AI is taught at the Computer Science department, biology at the Biochemistry department. These two rarely meet in any substantial manner. It was true went I was a student, it is still true today. This is one of the things we are changing at the Faculty of Medical Science of the University Mohammed VI Polytechnic. Students in Medicine and Pharmacy have to go through a serious AI and Data science class over a few years. They learn to code, they learn the mathematical concepts of AI, they learn to gather their own datasets, to derive their hypothesizes, and build, train and evaluate their own models using pyTorch. The goal being to produce a new generation of scientists that are intimate with their domain as well as with modern AI. One that can consistently deliver the promises of AI for Medicine and Drug Discovery.

AI+Health: An Undelivered Promise

Tariq Daouda, 8 mon.
AI is everywhere, or so would it seems, but the promises made for Drug Discovery and Medicine are still yet to be fulfilled. AI seems to always spring from a Promethean impulse. The goal of creating a life beyond life, doing the work of gods by creating a new life form as Prometheus created humanity. From Techne to independent life, a life that looks life us. Something most people refer to as AGI today. This is the biggest blind spot of AI development. The big successes of AI are in a certain way always in the same domains: - Image Processing - Natural Language Processing The reason is simple, we are above all visual, talking animals. Our Umwelt, the world we inhabit is mostly a world of images and language, every human is an expert in these two fields. Interestingly, most humans are not as sound aware as they are visually aware. Very few people can separate the different tracks in a music piece, let alone identify certain frequencies or hear delicate compressions and distortions. We are not so good with sound, and it shows in the relatively less ground breaking AI tools available for sound processing. The same phenomenon explains why AI struggles to achieve in very complex domains such as Biology and Chemistry. As it's core, modern AI is nothing more than a powerful general way to automatically guess relevant mathematical functions describing a phenomenon from collected data. What statisticians call a *Model*. From this great power derives the domain chief illusion: because the tool is general, therefore the wielder of that tool can apply it to any domain. Experience shows that this thinking is flawed. Every AI model is framed between two thing: its dataset (input) and its desired output as represented by the loss function. What is important, what is good, what is bad, how should the dataset be curated, how should the model be adjusted. For all these questions and more, you need a deep knowledge of the domain, of the assumptions of the domain, of the technicalities of the domain, of the limitations that are inherent to data collection in that domain. Domain knowledge is paramount, because AI algorithms are always guided by the researchers and engineers. This I know from experience, having spent about 17 years closely working with biologists. Pairing AI specialists with domain specialist with little knowledge of AI also rarely delivers. A strategy that has been tested time and time again in the last 10 years. Communication is hard and slow, most is lost in translation. The best solution is to have AI experts that are also experts in the applied domain, or domain experts that are also AI experts. Therefore the current discrepancies we see in AI performances across domains, could be layed at the feet of universities, and there siloed structures. Universities are organized in independent departments that teach independently. AI is taught at the Computer Science department, biology at the Biochemistry department. These two rarely meet in any substantial manner. It was true went I was a student, it is still true today. This is one of the things we are changing at the Faculty of Medical Science of the University Mohammed VI Polytechnic. Students in Medicine and Pharmacy have to go through a serious AI and Data science class over a few years. They learn to code, they learn the mathematical concepts of AI, they learn to gather their own datasets, to derive their hypothesizes, and build, train and evaluate their own models using pyTorch. The goal being to produce a new generation of scientists that are intimate with their domain as well as with modern AI. One that can consistently deliver the promises of AI for Medicine and Drug Discovery.

AI+Health: An Undelivered Promise

Tariq Daouda, 8 mon.
AI is everywhere, or so would it seems, but the promises made for Drug Discovery and Medicine are still yet to be fulfilled. AI seems to always spring from a Promethean impulse. The goal of creating a life beyond life, doing the work of gods by creating a new life form as Prometheus created humanity. From Techne to independent life, a life that looks life us. Something most people refer to as AGI today. This is the biggest blind spot of AI development. The big successes of AI are in a certain way always in the same domains: - Image Processing - Natural Language Processing The reason is simple, we are above all visual, talking animals. Our Umwelt, the world we inhabit is mostly a world of images and language, every human is an expert in these two field. Interestingly, most humans are not as sound aware as they are visually aware. Very few people can separate the different tracks in a music piece, let alone identify certain frequencies or hear delicate compressions and distortions. We are not so good with sound, and it shows in the relatively less ground breaking AI tools available for sound processing. The same phenomenon explains why AI struggles to achieve in very complex domains such as Biology and Chemistry. As it's core, modern AI is nothing more than a powerful general way to automatically guess relevant mathematical functions describing a phenomenon from collected data. What statisticians call a *Model*. From this great power derives the domain chief illusion: because the tool is general, therefore the wielder of that tool can apply it to any domain. Experience shows that this thinking is flawed. Every AI model is framed between two thing: its dataset (input) and its desired output as represented by the loss function. What is important, what is good, what is bad, how should the dataset be curated, how should the model be adjusted. For all these questions and more, you need a deep knowledge of the domain, of the assumptions of the domain, of the technicalities of the domain, of the limitations that are inherent to data collection in that domain. Domain knowledge is paramount, because AI algorithms are always guided by the researchers and engineers. This I know from experience, having spent about 17 years closely working with biologists. Pairing AI specialists with domain specialist with little knowledge of AI also rarely delivers. A strategy that has been tested time and time again in the last 10 years. Communication is hard and slow, most is lost in translation. The best solution is to have AI experts that are also experts in the applied domain, or domain experts that are also AI experts. Therefore the current discrepancies we see in AI performances across domains, could be layed at the feet of universities, and there siloed structures. Universities are organized in independent departments that teach independently. AI is taught at the Computer Science department, biology at the Biochemistry department. These two rarely meet in any substantial manner. It was true went I was a student, it is still true today. This is one of the things we are changing at the Faculty of Medical Science of the University Mohammed VI Polytechnic. Students in Medicine and Pharmacy have to go through a serious AI and Data science class over a few years. They learn to code, they learn the mathematical concepts of AI, they learn to gather their own datasets, to derive their hypothesizes, and build, train and evaluate their own models using pyTorch. The goal being to produce a new generation of scientists that are intimate with their domain as well as with modern AI. One that can consistently deliver the promises of AI for Medicine and Drug Discovery.

AI+Health: An Undelivered Promise

Tariq Daouda, 8 mon.
AI is everywhere, or so would it seems, but the promises made for Drug Discovery and Medicine are still yet to be fulfilled. AI seems to always spring from a Promethean impulse. The goal of creating a life beyond life, doing the work of gods by creating a new life form as Prometheus created humanity. From Techne to independent life, a life that looks life us. Something most people refer to as AGI today. This is the biggest blind spot of AI development. The big successes of AI are in a certain way always in the same domains: - Image Processing - Natural Language Processing The reason is simple, we are above all visual, talking animals. Our Umwelt, the world we inhabit is mostly a world of images and language, every human is an expert in these two field. Interestingly, most humans are not as sound aware as they are visually aware. Very few people can separate the different tracks in a music piece, let alone identify certain frequencies or hear delicate compressions and distortions. We are not so good with sound, and it shows in the relatively less ground breaking AI tools available for sound processing. The same phenomenon explains why AI struggles to achieve in very complex domains such as Biology and Chemistry. As it's core, modern AI is nothing more than a powerful general way to automatically guess relevant mathematical functions describing a phenomenon from collected data. What statisticians call a *Model*. From this great power derives the domain chief illusion: because the tool is general, therefore the wielder of that tool can apply it to any domain. Experience shows that this thinking is flawed. Every AI model is framed between two thing: its dataset (input) and its desired output as represented by the loss function. What is important, what is good, what is bad, how should the dataset be curated, how should the model be adjusted. For all these questions and more, you need a deep knowledge of the domain, of the assumptions of the domain, of the technicalities of the domain, of the limitations that are inherent to data collection in that domain. Domain knowledge is paramount, because AI algorithms are always guided by the researchers and engineers. This I know from experience, having spent about 17 years closely working with biologists. Pairing AI specialists with domain specialist with little knowledge of AI also rarely delivers. A strategy that has been tested time and time again in the last 10 years. Communication is hard and slow, most is lost in translation. The best solution is to have AI experts that are also experts in the applied domain, or domain experts that are also AI experts. Therefore the current discrepancies we see in AI performances across domains, could be layed a the feet of universities, and there siloed structures. Universities are organized in independent departments that teach independently. AI is taught at the Computer Science department, biology at the Biochemistry department. These two rarely meet in any substantial manner. It was true went I was a student, it is still true today. This is one of the things we are changing at the Faculty of Medical Science of the University Mohammed VI Polytechnic. Students in Medicine and Pharmacy have to go through a serious AI and Data science class over a few years. They learn to code, they learn the mathematical concepts of AI, they learn to gather their own datasets, to derive their hypothesizes, and build, train and evaluate their own models using pyTorch. The goal being to produce a new generation of scientists that are intimate with their domain as well as with modern AI. One that can consistently deliver the promises of AI for Medicine and Drug Discovery.

AI+Health: An Undelivered Promise

Tariq Daouda, 8 mon.
AI is everywhere, or so would it seems, but the promises made for Drug Discovery and Medicine are still yet to be fulfilled. AI seems to always spring from a Promethean impulse. The goal of creating a life beyond life, doing the work of gods by creating a new life form as Prometheus created humanity. From Techne to independent life, a life that looks life us. Something most people refer to as AGI today. This is the biggest blind spot of AI development. The big successes of AI are in a certain way always in the same domains: - Image Processing - Natural Language Processing The reason is simple, we are above all visual, talking animals. Our Umwelt, the world we inhabit is mostly a world of images and language, every human is an expert in these two field. Interestingly, most humans are not as sound aware as they are visually aware. Very few people can separate the different tracks in a music piece, let alone identify certain frequencies or hear delicate compressions and distortions. We are not so good with sound, and it shows in the relatively less ground breaking AI tools available for sound processing. The same phenomenon explains why AI struggles to achieve in very complex domains such as Biology and Chemistry. As it's core, modern AI is nothing more than a powerful general way to automatically guess relevant mathematical functions describing a phenomenon from collected data. What statisticians call a *Model*. From this great power derives the domain chief illusion: because the tool is general, therefore the wielder of that tool can apply it to any domain. Experience shows that this thinking is flawed. Every AI model is framed between two thing: its dataset (input) and its desired output as represented by the loss function. What is important, what is good, what is bad, how should the dataset be curated, how should the model be adjusted. For all these questions and more, you need a deep knowledge of the domain, of the assumptions of the domain, of the technicalities of the domain, of the limitations that are inherent to data collection in that domain. Domain knowledge is paramount, because AI algorithms are always guided by the researchers and engineers. This I know from experience, having spent about 17 years closely working with biologists. Pairing AI specialists with domain specialist with little knowledge of AI is also rarely delivers. A strategy that has been tested time and time again in the last 10 years. Communication is hard and slow, most is lost in translation. The best solution is to have AI experts that are also experts in the applied domain, or domain experts that are also AI experts. Therefore the current discrepancies we see in AI performances across domains, could be layed a the feet of universities, and there siloed structures. Universities are organized in independent departments that teach independently. AI is taught at the Computer Science department, biology at the Biochemistry department. These two rarely meet in any substantial manner. It was true went I was a student, it is still true today. This is one of the things we are changing at the Faculty of Medical Science of the University Mohammed VI Polytechnic. Students in Medicine and Pharmacy have to go through a serious AI and Data science class over a few years. They learn to code, they learn the mathematical concepts of AI, they learn to gather their own datasets, to derive their hypothesizes, and build, train and evaluate their own models using pyTorch. The goal being to produce a new generation of scientists that are intimate with their domain as well as with modern AI. One that can consistently deliver the promises of AI for Medicine and Drug Discovery.

AI+Health: An Undelivered Promise

Tariq Daouda, 8 mon.
AI is everywhere, or so would it seems, but the promises made for Drug Discovery and Medicine are still yet to be fulfilled. AI seems to always spring from a Promethean impulse. The goal of creating a life beyond life, doing the work of gods by creating a new life form as Prometheus created humanity. From Techne to independent life, a life that looks life us. Something most people refer to as AGI today. This is the biggest blind spot of AI development. The big successes of AI are in certain way always in the same domains: - Image Processing - Natural Language Processing The reason is simple, we are above all visual, talking animals. Our Umwelt, the world we inhabit is mostly a world of images and language, every human is an expert in these two field. Interestingly, most humans are not as sound aware as they are visually aware. Very few people can separate the different tracks in a music piece, let alone identify certain frequencies or hear delicate compressions and distortions. We are not so good with sound, and it shows in the relatively less ground breaking AI tools available for sound processing. The same phenomenon explains why AI struggles to achieve in very complex domains such as Biology and Chemistry. As it's core, modern AI is nothing more than a powerful general way to automatically guess relevant mathematical functions describing a phenomenon from collected data. What statisticians call a *Model*. From this great power derives the domain chief illusion: because the tool is general, therefore the wielder of that tool can apply it to any domain. Experience shows that this thinking is flawed. Every AI model is framed between two thing: its dataset (input) and its desired output as represented by the loss function. What is important, what is good, what is bad, how should the dataset be curated, how should the model be adjusted. For all these questions and more, you need a deep knowledge of the domain, of the assumptions of the domain, of the technicalities of the domain, of the limitations that are inherent to data collection in that domain. Domain knowledge is paramount, because AI algorithms are always guided by the researchers and engineers. This I know from experience, having spent about 17 years closely working with biologists. Pairing AI specialists with domain specialist with little knowledge of AI is also rarely delivers. A strategy that has been tested time and time again in the last 10 years. Communication is hard and slow, most is lost in translation. The best solution is to have AI experts that are also experts in the applied domain, or domain experts that are also AI experts. Therefore the current discrepancies we see in AI performances across domains, could be layed a the feet of universities, and there siloed structures. Universities are organized in independent departments that teach independently. AI is taught at the Computer Science department, biology at the Biochemistry department. These two rarely meet in any substantial manner. It was true went I was a student, it is still true today. This is one of the things we are changing at the Faculty of Medical Science of the University Mohammed VI Polytechnic. Students in Medicine and Pharmacy have to go through a serious AI and Data science class over a few years. They learn to code, they learn the mathematical concepts of AI, they learn to gather their own datasets, to derive their hypothesizes, and build, train and evaluate their own models using pyTorch. The goal being to produce a new generation of scientists that are intimate with their domain as well as with modern AI. One that can consistently deliver the promises of AI for Medicine and Drug Discovery.

AI+Health: An Undelivered Promise

Tariq Daouda, 8 mon.
AI is everywhere, or so would it seems, but the promises made for Drug Discovery and Medicine are still yet to be fulfilled. AI seems to always spring from a Promethean impulse. The goal of creating a life beyond life, doing the work of gods by creating a new life form as Prometheus created humanity. From Techne to independent life, a life that looks life us. Something most people refer to as AGI today. This is the biggest blind spot of AI development. The big successes of AI are in certain way always in the same domains: - Image Processing - Natural Language Processing The reason is simple, we are above all visual, talking animals. Our Umwelt, the world we inhabit is mostly a world of images and language, every human is an expert in these two field. Interestingly, most humans are not as sound aware as they are visually aware. Very few people can separate the different tracks in a music piece, let alone identify certain frequencies or hear delicate compressions and distortions. We are not so good with sound, and it shows in the relatively less ground breaking AI tools available for sound processing. The same phenomenon explains why AI struggles to achieve in very complex domains such as Biology and Chemistry. As it's core, modern AI is nothing more than a powerful general way to automatically guess relevant mathematical functions describing a phenomenon from collected data. What statisticians call a *Model*. From this great power derives the domain chief illusion: because the tool is general, therefore the wielder of that tool can apply to any domain. Experience shows that this thinking is flawed. Every AI model is framed between two thing: its dataset (input) and its desired output as represented by the loss function. What is important, what is good, what is bad, how should the dataset be curated, how should the model be adjusted. For all these questions and more, you need a deep knowledge of the domain, of the assumptions of the domain, of the technicalities of the domain, of the limitations that are inherent to data collection in that domain. Domain knowledge is paramount, because AI algorithms are always guided by the researchers and engineers. This I know from experience, having spent about 17 years closely working with biologists. Pairing AI specialists with domain specialist with little knowledge of AI is also rarely delivers. A strategy that has been tested time and time again in the last 10 years. Communication is hard and slow, most is lost in translation. The best solution is to have AI experts that are also experts in the applied domain, or domain experts that are also AI experts. Therefore the current discrepancies we see in AI performances across domains, could be layed a the feet of universities, and there siloed structures. Universities are organized in independent departments that teach independently. AI is taught at the Computer Science department, biology at the Biochemistry department. These two rarely meet in any substantial manner. It was true went I was a student, it is still true today. This is one of the things we are changing at the Faculty of Medical Science of the University Mohammed VI Polytechnic. Students in Medicine and Pharmacy have to go through a serious AI and Data science class over a few years. They learn to code, they learn the mathematical concepts of AI, they learn to gather their own datasets, to derive their hypothesizes, and build, train and evaluate their own models using pyTorch. The goal being to produce a new generation of scientists that are intimate with their domain as well as with modern AI. One that can consistently deliver the promises of AI for Medicine and Drug Discovery.