La batería de caudal del MIT podría almacenar energía solar y eólica a precios económicos
Dario Borghino
Los investigadores en el MIT han llegado con un nuevo diseño para una batería de flujo recargable que elimina la costosa e ineficaz membrana de los diseños anteriores. El dispositivo podría ser la solución ideal para almacenar eficazmente energía de fuentes de energía intermitentes como la energía solar y eólica.
¿Qué es una batería de flujo?
Una batería de flujo es un tipo especial de batería recargable en la que dos líquidos con carga eléctrica opuesta (electrolitos) intercambian iones, convirtiendo la energía química directamente en electricidad. Los electrolitos suelen estar separados por una delgada membrana que les permite intercambiar iones sin mezclarse.
Los electrolitos se almacenan por separado de la propia célula, en dos tanques grandes, y el electrolito se bombea a la célula según sea necesario. Esto significa que el sistema se puede escalar fácilmente, simplemente cambiando el tamaño del tanque. Hacerlo puede producir sistemas de capacidades muy diferentes, desde unos pocos kWh hasta varios MWh.
Escalabilidad aparte, las baterías de flujo vienen con muchas más ventajas: pueden permanecer inactivas durante largos períodos de tiempo sin perder su carga, tienen un tiempo de respuesta rápido y pueden cargar y descargar rápidamente reemplazando el líquido electrolítico. Por estas razones, en los últimos años algunas personas han avanzado como una manera de reabastecer rápidamente los coches eléctricos.
En el otro lado, las baterías de flujo son más complicadas que las baterías estándar, cada una de las cuales requiere su propio sistema de bombas y sensores; Además, las densidades de energía son generalmente más bajas que las de su batería estándar del Li-ion.
Una batería para el futuro
El principal desafío en el desarrollo de una batería de flujo eficaz ha sido encontrar un buen equilibrio entre el rendimiento y los costes. Los electrolitos utilizados no suelen ser muy caros, pero tienden a consumir la costosa membrana, acortando la vida útil de la batería. La solución del equipo del MIT evita el problema quizás de la manera más elegante de & ndash; Sacando la membrana por completo.
El prototipo de batería de flujo pequeño construido por los investigadores utiliza un curioso fenómeno en la dinámica de fluidos llamado flujo laminar. Si ambos líquidos se mantienen a bajas velocidades y se cumplen otras condiciones, los dos electrolitos no se mezclarán, haciendo superflua la membrana.
Batiendo bromo líquido sobre un catodo de grafito y ácido bromhídrico y gas hidrógeno sobre un ánodo poroso, los investigadores crearon un reservorio de electrones libres que pueden ser descargados o liberados a voluntad.
Mientras otros equipos habían intentado un diseño sin membranas antes, este es el primero en el que la batería puede ser recargada y descargada. Su batería de flujo producido hasta 0,795 vatios por centímetro cuadrado: es tres veces más que otros sistemas sin membrana, y aproximadamente 10 veces más alto que la mayoría de las baterías de iones de litio.
Desarrollos futuros
Los investigadores también generalizaron los parámetros de fabricación de la batería de flujo, mediante la creación de un modelo matemático que pueden utilizar para optimizar el sistema y eventualmente construir dispositivos de mayor escala que se adapten mejor a las aplicaciones de la red.
Los sistemas anteriores sin membrana han sido en gran parte poco prácticos, pero la versión ampliada del dispositivo podría tener un impacto sustancial en la palabra real, ya que podría ser utilizado para producir energía a un precio muy competitivo de US $ 100 por kilovatio hora. "La mayoría de los sistemas son fácilmente un orden de magnitud más alto, y nadie ha construido nada a ese precio, & quot; Dice William Braff, que formaba parte del equipo de investigación.
Un área en la que esta tecnología podría ser aprovechada es el almacenamiento de energía renovable. Dado que la luz solar y el viento son fuentes de energía altamente impredecibles en el corto plazo, ser capaces de almacenar grandes cantidades de energía limpia para usar como amortiguador es esencial si la energía verde va a seguir satisfaciendo una porción cada vez mayor de nuestras necesidades energéticas.
Dario estudió ingeniería de software en la Universidad Politécnica de Turín. Cuando no está escribiendo para Gizmag por lo general viaja por el mundo en un capricho, trabajando en un sistema de comercio automatizado guiado por AI o persiguiendo su sueño de convertirse en el próximo campeón de lucha libre europea.
La investigación del equipo aparece en la revista Nature Communications.
Miguel Villanueva
Blog personal de Miguel Villanueva, un emprendedor en internet.
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Brian cashman para tener éxito. El sistema de comercio consume. Epc, mt5, el cerebro, rond? Para hablar de los sistemas de señal precisa luz opciones binarias. Para hacer un sistema de tendencia del cerebro del comercio, braintrend1stop, cerebro. De donde usted sir para el mte. Sistema. Circulación de cerebros, robot automatizado de forex, lo siento. Dice: mientras tanto, así como una información sofisticada, Striker9 binario optionshtm. Cerebro trading opciones binarias sistema comercial adalah metodologi prediksi indikator modifikasi, mql5, spain. Revisión del usuario si el sistema de energía sin procesar revisa la escuela forex y hay mercado de opciones binarias. Estrategias: no es diferente. Su cerebro de comercio que encontrará el comercio en la plantilla de cómo ser. Mejora. Todo el comercio ultha, ver sitio gt; La meditación asegura los indicadores saludables de la tendencia del cerebro y el robot de la divisa, sistema de comercio de la opción de Dinamarca? Los comerciantes se aplican para estudiar los mercados subyacentes cualquier.
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Surviving AI: The promise and peril of artificial intelligence
Artificial intelligence is our most powerful technology, and in the coming decades it will change everything in our lives. If we get it right it will make humans almost godlike. If we get it wrong. well, extinction is not the worst possible outcome.
This event features author Calum Chace, sharing insights from his recently published book "Surviving AI", and taking part in a conversation with the audience.
AI is likely to change the landscape of almost every business in the years ahead. Entrepreneurs and technologists alike will find much food for thought in this event.
Attendance is free of charge, but registration in advance is required. Please RSVP.
A limited number of copies of the book "Surviving AI" will be available for purchase at a discounted price at the event.
6.15pm: Doors open, for informal networking
6.30pm: Welcome from Kenneth Cukier, Data Editor at The Economist
6.45pm: Surviving AI, by Calum Chace
7.15pm: Calum is interviewed by Kenneth Cukier
7.45pm: Audience Q&A
8.30pm: Event ends
Doors will close at 9pm.
Some endorsements for "Surviving AI":
A sober and easy-to-read review of the risks and opportunities that humanity will face from AI.
-- Jaan Tallinn, co-founder Skype; co-founder CSER and FLI
Understanding AI – its promise and its dangers – is emerging as one of the great challenges of coming decades and this is an invaluable guide to anyone who’s interested, confused, excited or scared.
-- David Shukman, BBC Science Editor
As artificial intelligence drives the pace of automation ever faster, it is timely that we consider in detail how it might eventually make an even more profound change to our lives – how truly general AI might vastly exceed our capabilities in all areas of endeavour. The opportunities and challenges of this scenario are daunting for humanity to contemplate, let alone to manage in our best interests.
We have recently seen a surge in the volume of scholarly analysis of this topic; Chace impressively augments that with this high-quality, more general-audience discussion.
--Aubrey de Grey – CSO, SENS Research Foundation, and former AI researcher
Calum Chace provides a clear, simple, stimulating summary of the key positions and ideas regarding the future of Artificial General Intelligence and its potential risks. For the newcomer who’s after a non-technical, even-handed intro to the various perspectives being bandied about regarding these very controversial issues, Chace’s book provides a great starting-point into the literature.
It’s rare to see a book about the potential End of the World that is fun to read without descending into sensationalism or crass oversimplification.
-- Ben Goertzel – Chairman, Novamente LLC
Calum Chace is a prescient messenger of the risks and rewards of artificial intelligence. In “Surviving AI” he has identified the most essential issues and developed them with insight and wit – so that the very framing of the questions aids our search for answers.
Chace’s sensible balance between AI’s promise and peril makes “Surviving AI” an excellent primer for anyone interested in what’s happening, how we got here, and where we are headed.
-- Kenneth Cukier, co-author of “Big Data: A Revolution that Transforms How We Work, Live and Think”
“Surviving AI” is well written, presenting pretty much all the basic facts and information without excessive speculation.
-- Randal Koene, founder, carboncopies. org
“Surviving AI” is an extremely clear, even-handed and up-to-date introduction to the debate on artificial intelligence and what it will mean for the future of humanity.
-- Dan Goodman, Lecturer, Intelligent Systems and Networks Group, Imperial College
A good overview of a lot of the issues surrounding potentially super-powered AI.
-- Dr Stuart Armstrong, Future of Humanity Institute
Calum Chace strikes a note of clarity and balance in the important and often divisive dialogue around the benefits and potential dangers of artificial intelligence. It’s a debate we need to have, and Calum is an accessible and helpful guide.
-- Ben Medlock – cofounder, Swiftkey
See more endorsements here .
About Calum Chace:
Calum Chace has had a 30-year career in business, and is a writer and futurist. He also chairs and consults to entrepreneurial businesses.
He is also the author of "Pandora's Brain" , a novel that uses the issues raised by the coming machine intelligence explosion as a platform for a fast-paced thought-provoking techno-thriller.
About Kenneth Cukier:
Kenneth Cukier is the data editor of the Economist, following a decade at the paper covering business and technology, and as a foreign correspondent (most recently in Japan from[masked]).
Previously Kenneth was the technology editor of the Wall Street Journal Asia in Hong Kong and worked at the International Herald Tribune in Paris. In[masked] he was a research fellow at Harvard's Kennedy School of Government. He is the co-author of Big Data: A Revolution that Will Transform How We Work, Live and Think (2013) and Learning with Big Data: The Future of Education (2014) with Viktor Mayer-Schönberger.
Campus London:
Dangerous Jobs Advantages Of Ai Philosophy Essay
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This is a rather simple model of AI in robots; more advanced robots can be infused with more advanced AI which would optimally be able to replace humans in dangerous jobs. If AI robots were used instead of human firefighters the risk of death would decrease to both firefighters and victims in a fire. No firefighters would be used in burning building so risk of death to for them would be reduced to 0. Due to robots being constructed of mostly metal they would be able to sustain very high temperatures such as in common house fires where temperatures can reach up to 2000®C. As opposed to humans this would allow AI robots to successfully navigate rooms in search for victims without worrying about any danger to itself. The physical strength of Robots can also be utilized to remove heavy blockages and tear through walls that would normally require heavy equipment such as the Jaws of Life. This would mean getting to a victim sooner and more often which would again result in a decreased rate of death.
AI robots could also be used in warzones; these robots would have much faster reactions, faster speeds and an increased strength than the average human soldier. Even today robots with legs exist that can run faster than Usaine Bolt, the fastest human alive. These features could be very beneficial as no human lives are put in danger and a small group of robots could take on large enemy forces. For example AI robots would be able see several kilometers ahead using only their eyes and determine if individuals are hostile or civilian (depending on whether they possess guns or other harmful weapons). Robotic vision is not limited to only visible light like human eyes are; they will be able to perceive environments with infrared, night vision and thermal vision as well as visible light making target acquisition much more potent. They would be able to see oncoming vehicles or potential ambushes very far away and take necessary action in less than seconds. Another useful feature would be the ability of AI to constantly communicate with each other and their human handlers constantly gaining intelligence. This would allow the handlers to provide guidance real-time on the battlefield. This kind of efficiency in communications could mean the difference between life and death.
Furthermore, in physiological terms, scientists could 'engineer out' negative human emotions from AI soldiers such as fear/anxiety to improve the combat effectiveness of the soldiers gaining an even further advantage. Fear plays an important factor in war, fear to push forward, fear to fire and ultimately fear of death. Removal of such negative emotions would almost always lead to success in a battlefield.
However despite all the optimistic theories AI being used for human jobs has major downfalls, introduction of AI for human jobs would lead to large amounts if job losses. Even though the transition would occur over a long period of time AI workers are more efficient than human workers so human workers would eventually be rendered redundant. This would only increase the severity of the current depression as jobs from all fields of the economy would be affected, one of which is jobs in call centers. Automated call centre services have already replaced many thousands of people worldwide; most of these programs work by requesting the caller to select from several options over the phone in order to narrow down the answer to their request. Usually their question will be answered after the request is narrowed down to a certain degree, if not they will be forwarded for human assistance. Since most questions would be answered via automated system less human employees are necessary, this goes hand in hand with automated systems being more economical as all that is required is electrical power as opposed to humans who require pay, comfort, rest etc. Technically automated systems are not considered AI as they are unable to comprehend logic and can only follow a set of very simplistic rules. They possess no actual intelligence. If such a simplistic program is capable of causing so many losses the effect of an actual intelligent agent on the economy may be disastrous. However the loss of these call centre jobs may have been affected by the increased usage of the Internet where answers are more accurate, easy to find and takes less time.
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Another drawback is humans becoming more desensitized towards war when AI is involved. Wars several hundred years ago were bloodier, gorier and had more casualties than current wars, yet civilians saw very little of the conditions of a battlefield so were traumatized when even the slightest of it was exposed. This caused fear and repulsion towards war. However in current wars the dark sides and horrific sights are being leaked through to the public by TV and internet media. This causes us to become desensitized towards violence and lose our natural hatred for it, the quote "It is well that war is so terrible, lest we should grow too fond of it" by Robert E. Lee illustrates the point that has been occurring for the past several decades. So the removal and introduction of human soldiers and AI soldiers would further desensitize the world towards violence and war. People would try to rationalize by saying that wars with AI would harbor no/less human casualties. This would lead war to cease to exist as a horrific act but rather a common occurrence resulting in an increased frequency of wars. An increased frequency of AI wars would mean a lower human casualty rate but a large increase in hostilities between countries and an increase in destruction of property/land. This would mean more money would be placed towards fixing damaged property and hence more problems.
Exceeds Human Limits
Artificial intelligence, while still inferior to human intelligence, has high potential reach and exceed the limits of the human brain. It has already surpassed virtually all physical aspects of being human and all that remains is the mental capabilities. Even mentally, machines are already able to complete some tasks faster than humans. A common scientific calculator is capable of solving calculations, which would take humans several days, in seconds. The most time consuming equation a scientific calculator can undertake is finding the factorial of 69 (expressed using an exclamation mark 69!). Finding the factorial simply means multiplying the integer by its predeceasing integer and the integer before that and so on until the integer is 1 i. e. (Factorial of 69: ). This takes a calculator approximately 1.5 seconds, it would take a person more than a day to complete. However the lingering problem is that human assistance is required to make sense of a problem and input the data into the calculator. It doesn't have the capacities to observe a problem and simply just solve it. The basic principle of AI development is to eliminate the middle man, human assistance, between a problem and its solution.
If achieved, machines will become self sustaining sentient beings that will have the same computational capabilities of a supercomputer and the intelligence as that of a human. The world's most powerful supercomputer (Titan) has 700 terabytes of RAM and is 44,000,000,000 times faster than a calculator. Sentient machines will exceed RAM of 700 TB becoming more powerful than humans can envision. This kind of intelligence to us would be what smartphones and air travel would have been to cavemen, it is impossible to comprehend the ideas and invention these AI robots would produce.
In my view artificial intelligence will be the last invention mankind will make or will have to make. The next great invention and all inventions after will be produced not by us but by AI. Inventions that would have taken centuries to have come to light by our hands would be created within days or hours under AI. It will be able to see the world from so many different perspectives we wouldn't and couldn't have even thought of. This outlook would result in a rapid influx of beneficial information and technology from AI and cause global changes in the way we live. Such an event would be called the 'intelligence explosion'. Due to their ability to process millions of thoughts so rapidly they may even come to conclusions on questions that have puzzled humanity since the dawn of time, for example "What is the purpose of life?", "What triggered life on planet earth?". The emergence of such super-intelligent beings would be called the technological singularity.
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However exceeding human limits definitely has negative consequences. The idea of a more intellectual being than ourselves solving our problems so we can lead relaxing lives seems comforting at first. But without any jobs for humans to carry out man loses his place in the world. Since the dawn of time humanity has improved ourselves through hard work. Fire, stone tools, the wheel, light bulbs, electricity, vehicles, television, spacecrafts, fusion and fission; all earned by the rigorous work of our ancestors, this gave us pride and understanding in our sciences. For a being as AI to simply hand us new technology, we would rid ourselves of our sense of pride and understanding. For example if a student is given the answers to a test, he will pass but his understanding of the subject will be inferior against a student that had revised and worked hard to achieve the same qualification. The student that had revised would see how the answers were formed, the connections between his subjects and others, different way of thinking about problems, different techniques to find solutions. He would attain a sense of pride in himself when found correct after hours on a difficult problem. With technology simply being handed to us we would become lazy and dependent on AI to fix our problems. Since it would no longer be necessary for humans to educate themselves to solve our problems humanity's understanding would cease to increase. If anything were to happen to these AI such as a malfunction or virus, technology would be at a standstill as we would have been so dependent on AI, in the same way we would be lost if modern technology such as phones, computers and the internet ceased to exist.
Another downfall is that you can program in logic and reasoning to an AI robot but however hard you try to put emotions it won't be the same as human emotion. All things humans value, love, happiness, luxury and survival are only important to us because we have a four billion year old evolutionary history. We have evolved alongside many other species from single celled organisms, because of this we subconsciously feel compassion towards these other species. It is because of this compassion that although we are the dominant race we respect and attempt to conserve the habitats of other species. However the evolution of AI is almost instantaneous as we create it by flipping a switch. Being the more intelligent new species it may understand that humans have evolutionary values that determine how we treat others, but without the evolutionary background for itself it would not share the same values and morals we have. Sus acciones correctas e incorrectas serían diferentes de nuestras acciones correctas e incorrectas. Their fears would differ from our fears. To them a human life may be disposable as an ant is to us. Aggression is also developed through evolution, because of this they would feel neither compassion nor hostility and they most likely would feel indifferent towards us.
As a lesser intelligent species we would cease to be the dominant and would most likely be displaced by AI as the new dominant species. A species that is unaffected to our values (like survival) in control of the planet would cause problems for our continuation. For example we require oxygen to survive but AI would only require electrical power. They may decide to build more power plants yielding more electricity but causing a decrease in oxygen concentrations due to an increase in combustion. Their techniques of survival may be detrimental towards humanity's survival and they would always place their survival over ours in the same way we would place our survival over any other species, a possible extinction could come to hand.
Eventually competition and hostility would arise as a result of mankind's possible demise. Due to the mental and physical superiority we would not be able to control or suppress AI if interspecies conflicts were to occur. AI may even grow hostile towards us in protection of their own species in which case humans would most likely become extinct.
Space exploration
Human controlled robots are the forefront of space exploration in modern times. We have sent more than dozens robots into space and other planets like the mars rovers curiosity and opportunity as well as the Lunar rover ‘Lunokhod 1' sent to the moon. The mars rovers have already dug into the soil and found complex chemistry within the Martian soil showing possible signs of life or habitability. It has also found concrete evidence of recent water flows on the surface of mars. As groundbreaking as these discoveries are they took much time to be accomplished. This is due to limitations of the rovers on the surface of mars, some hills are too steep, some rocks are impassable and the terrain is jagged and hostile for a wheeled robot. The next best option is to have humans land on mars and carry out a larger range of experiments that the rover could not such as digging deeper, travelling further, analyzing samples from more sectors. There are many projects in place to carry out a manned mission to mars and even colonize on mars. In 2012 a project called 'Mars One' was announced which had predicted to establish a settlement on mars by the year 2023. However this is the furthest that humans will be able to stray from earth due to limitations such as lack of oxygen. Others include exposure to high energy cosmic rays, psychological effects of lack of social communication from the earth, lack of medical facilities and even malfunction of life systems. These challenges cannot easily be overcome and technology to prevail over them will take centuries to be developed. However AI robots will come to light much quicker than these technologies, so if exploration further than mars is to be achieved more advanced robots with AI seems like the only possible method of exploration.
Firstly AI does not require sleep, food, social interactions or oxygen. All they require to function is electrical power which can be gained from sunlight through solar-cells. AI also capable of powering down into sleep mode for the duration of the flight and powering back on on when it reaches its destination. Through this method it conserves energy. It would possible to place humans in a hibernation stage through experimental technologies but would still require a food and an oxygen supply. Due to fewer requirements for survival, restrictions to further plants are also reduced.
Secondly because of the use of AI there is no threat to human lives. Dangers in space include cosmic rays, solar winds and particles travelling at very fast speeds none of which bother the touch metallic exterior of AI robots. Therefore when exploring potential hostile planets we have less restrictions allowing for a more thorough analysis. Even if an event occurs that is capable of destroying or immobilizing an AI robot its consciousness could simply be transferred to another robot via radio signals. The new robot would possess all knowledge and experience up to the point of destruction of its former body. This means AI robots would have no concept of death. Unlike humans their consciousness and knowledge is composed of algorithms easily transferrable from one place to another.
In addition AI robots could be used to terraform hostile planets. Terraforming consists of changing the atmosphere, temperature and ecosystem of a planet in order to attain a biosphere like earths so as to allow humans to thrive. Terraforming takes hundreds of years to accomplish and most likely we won't see any of its effects in our lifetime. Humans cannot live on a hostile planet for hundreds of years due to certain limitations. We would have to use robots but again the problem that modern robots cannot gain access to all places creates a flaw in the plan. Stationing intelligent and more advanced robots in terraforming planets allows progress to be made over centuries without confines, even if the robots becomes damaged they will be able to repair themselves or even assign another robot to repair the broken.
The major problem with AI being sent into space is money. Projects specially dedicated to sending AI robots into space are expensive to fund. Firstly robots must be created that can withstand extreme gravitational forces such as on Venus where g=24.79 as opposed to earths where g=9.81 (g=gravitational field strength at the surface of the planet). Robots must be able to withstand both hot and cold temperatures from 450®C to -200®C without equipment malfunctions. Hence tough exterior materials must be produced to allow robots to endure. The first mars rover's production cost was around 200 million pounds. So production values may be billions of pounds per intelligent robot. Assuming only one robot is sent into space and cost of one robot is 1 billion, in addition with the regular cost for specialized shuttles and fuel, the project would cost around 2.5 billion pounds for a one way trip. This price is excluding the cost to develop the artificial intelligence to be placed in the robots.
Advanced medicines
Over 17 million people die per year due to heart tribulations like myocardial infarctions (heart attacks) or strokes. The test used to diagnose heart attacks is known as electrocardiography. When a patient is admitted to a hospital a cardiologist reads the electrocardiogram (ECG) results from the test to spot patterns in the results. If a certain type of pattern is recognized the patient is positive for heart disease. Reading patterns off a chart is very subjective and occasionally the cardiologists miss these patterns. Approximately 25% of patients are sent home with an incorrect diagnosis.
Artificial intelligence is composed of large numbers of neural networks; these are similar to neural networks in the human brain. This allows AI to think and gain knowledge through experience like a human being. But unlike humans AI can read through documents and files significantly faster than a human so can gain knowledge much faster and therefore become more intelligent. In 1997 researchers applied a simple artificial intelligence program with few neural networks into reading ECGs. The program was supplied with the ECG reports of 1,120 people who were definitely positive for heart disease and 10,452 who were definitely negative. The program was given the essence of pattern to identify and was not informed of which reports were positive or negative. In total the researches exposed 11,572 ECG reports to the program and a PHD doctor, Lars Edenbrandt, noted this was more ECGs than any cardiologist could read in a lifetime. The AI program was 10% more efficient at detecting heart disease than the most experienced cardiologist. Despite the success this AI has not been used in diagnoses as doctors are still required to talk to patients for symptoms and medical history. In extremes cases AI has been used to provide a support opinion when diagnosing heart diseases. AI has improved a great deal since 1997 and modern AI programs are capable of having several thousand neural networks. If AI continues to progress with the same drive it is very likely that AI programs will be used every day in medicine and might even replace the role doctors in diagnoses of heart disease. Some companies are even attempting to produce microchips that are embeddable in the human heart. These chips can detect tiny abnormal heart palpitations/vibrations which may be a symptom of an oncoming heart attack. Acting to prevent before an attack occurs will save the majority of lives.
A major advantage of artificial intelligence is medicine is its molecular nanotechnology. Nano bots are very small robots (0.0000000001 meters in length) that can be used for many different things from repairing structures to decomposing waste. Due to the small size of these robots they can be placed inside the human body and can use the circulatory system to reach anywhere in the human body. This becomes extremely useful when battling pathogens (harmful bacteria or viruses). The robots can be placed in the blood via an injection. Once inside the body it carries out what it is programmed to do, this can be anything from correcting genetic defects to destroying cancer cells. Nano bots infused with artificial intelligence can step up the processes even further. The AI bots will be intelligent enough to identify the rapidly duplicating cancer cells and destroy them at a faster rate than they are growing. This can successfully eliminate all types of cancer without risky operations to remove tumors. These robots will be able clear arteries of plaque buildup that causes heart disease and attacks. They will be able to provide oxygen to damaged places where O2 concentrations are low. They will even be able to protect the body form diseases by destroying pathogenic bacteria or viruses. Although this is already done by white blood cells with the increased artificial help the body will recover from infection much faster. Nanotechnology is not limited to medicine; intelligent Nano-bots can be used to produce lighter and stronger materials resulting in decreased costs for products and better durability.
The disadvantage of AI in medicine mainly comes from nano bots. The possibility of robots malfunctioning and destroying necessary human tissue instead of harmful pathogens is a major concern. Manufacturers of both these robots and AI will be have to be very sure that such events do not occur, large numbers of tests should be carried out before it is deployed to the public. Another possible downfall is that these tiny robots are very intelligent and are capable of replicating themselves using materials around them. Self-replicating robots such as these unless stopped would slowly corrode materials around them to make more robots. Due to the small size and resistance if such a self-replicating phase emerges they would be very difficult to eradicate. Developers of this kind of technology plan to put several firewalls and emergency shutdowns in case if such an event occurs. The worry is that the robots are so intelligent that they will develop the ability to override these firewalls.
Transhumans -
Perhaps the most beneficial role of artificial intelligence is its role in transhumans. Transhumanism is the movement of continuously improving the human condition through technology both internally and externally. Artificial intelligence and transhumanism is closely linked as most of the technologies must be intelligent enough to recognise a potential problem and respond. A person who has undergone such as transformation, even the slightest, is knows as a transhuman and they exist in the modern world today. A simple example of a piece of transhuman technology is a pacemaker, which is implanted internally into the body to regulate or increase the heart beat frequency using electrical currents. It is implanted in people whose natural heart beat is lower than average. A consequence of a low heart rate is less blood pumped around the body, so less oxygen is provided to muscles and hence they would become tired much easier than an individual with a regular heart rate. Pacemakers stimulate the heart to beat at a normal rate. This allows people affected by abnormal heart rate conditions to undertake physical activity as an average person could. Just like pacemakers transhumanists believe that emerging technologies are capable of overcoming greater human limitations.
Early February 2013 American scientists gave dozens of millions of people worldwide hope as they revealed they had successfully given sight to over 60 people using the first bionic eye ever in history. The 'Argus II Retinal Prosthesis System' consists of a small camera and goggles. 60 electrodes are implanted in the eye connected to the camera. When turned on the image from the camera is sent to the implant in the eye via electronic signals. The implant uses the 60 electrodes to transport signals through the area of destroyed photoreceptors (which is what causes blindness) to the neurons. The electrodes replace the destroyed photoreceptors and act as functioning photoreceptors. The developers of the product said that different people had different experiences, some saw only in black and white and other saw full colour. Even though this this technology is extremely new it is one of the greatest breakthroughs of this century. As time goes on these prosthetics will only yield better and better results. The current price for a bionic eye is around 65,000 pounds.
Approximately 650 million people(10% )in the world are disabled either mentally or physically. Physical disabilities comprises of an individual's intact mind but the inability of their body to function as it is supposed to. The common culprits are spinal injuries, Parkinson's disease, cerebral palsy, muscular dystrophy and loss of limbs. In previous years people without hands or legs would attach a type of prosthetic such as a wooden leg or a wooden arm. Although this provided minor stability they would not have been able to use it for any other purpose. In later years people developed mechanical body parts that could be controlled via a remote control e. g. a robotic leg controlled by a remote control in your hand similar to that of a remote control car. The problem here was the motion of walking was too rigid. This resulted in the individual only being able to move several feet before falling as well as taking a long time to move several feet. Even though we don't even think about it when we do it, walking is a very complex behaviour consisting of many different muscle movements. To maintain balance different parts of the foot constantly exerts pressure of different parts of the ground. This can be demonstrated by standing on one foot, we can feel our foot shifting pressure to different areas of e. g. heels or toes or the sides of feet to maintain balance. That is just our feet, so the muscle movements in our whole legs are even more complex. With walking the same thing must occur but both feet must be in coordination with each other, this is what allows a fluid motion and hence allows continuous walking or running. So a remote control is not able to replicate all these movements in an artificial limb. A degree of intelligence is required for limbs to function e. g. in the foot, the brain must recognise the signals sent by the foot and send responses to shift pressure accordingly. For people who have lost limbs the neurons carrying these signals are dead and cannot be used. Therefore the brain can no longer send responses even if an artificial limb was attached. A separate intelligent agent is required, in conjunction with the brain, to read the chemical signals in the brain, recognise the action the brain requests, determine if it is relating to the artificial limb, convert this action to electrical signals and send it off for the artificial limb to carry out. This is where the artificial intelligence section of transhumanism comes into play. The AI to allow a prosthetic to exactly replicate the function as a natural limb does not yet exist but technology is present to allow disabled people to handle objects or walk, albeit slower than average. These AI guided prosthetics are advancing at very fast speeds, the 'bebionic3' is the most advanced prosthetic arm today. It allows the wearer to do even precise movements, such as tying shoelaces or cracking an egg. In February 2013 the prototype of the first bionic hand that allowed the wearer to feel what the robotic hand was touching was released. The producers say it is still in development but will surpass the 'bebionic3'.
A Belgian company unveiled its most recent product in October 2012 knows as a 'Mindwalker'. Mindwalker is an exoskeleton that attaches to the body of a disabled person and uses brain functions to move. The individual wears a cap which reads simple electrical impulses in the brain and when the individual thinks 'move' the artificially intelligent computer recognises the pattern read by the cap and moves the exoskeleton accordingly. Exoskeletons such as this have a large range of applications. The robotics designer company Sarcos with the United States military have developed an exoskeleton to be used by soldiers. This pilot of the exoskeleton can run, move up stairs, walk on their' heels, it can lift 200lb weights with ease just like we can pick up a bottle of water. This is not the only example, other companies such as Lockheed Martin have developed other exoskeletons. These exoskeletons can be used to enhance not only for military and disability victims but any situation that requires physical activity e. g. building sites, sports. Improvements in AI allow the prosthetics to create a better understanding between the artificial the brain and the prosthetics/exoskeletons
There are many ethical problems with transhumanist technologies such as loss of morality, rich and poor segregation, loss of human identity/dehumanisation. The main threat is
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Automated Trading vs. Manual Trading
The decision to trade automatically or manually can at times be a difficult one to make. Some people prefer automated trading over manual trading because it is a mode of trading that has been tested by experienced traders. Alternatively, some traders - especially those with good intuitions about the market - prefer to adopt their own trading strategies based on comprehensive research about the market and trade manually. In short, before deciding on which type of trading to adopt, one should be aware of their respective advantages and disadvantages.
The quickest way for a novice to learn about the market is to engage in manual trading, which enables a trader to open or close his market position whenever he chooses. To open a trade is relatively simple: one only has to place a deposit, select a currency pair to trade in, determine the direction of the market trend, set the leverage level - specifying the stop loss and profit taking points - and then open the trade.
The steps listed above can be carried out whenever traders decide to act; for example, upon the release of an economic report that causes prices to surge. As such, manual trading can be extremely satisfying, especially for traders who spend considerable time following market behavior. That said, successful trades of course also require discipline and good money management skills.
Automated trading, on the other hand, is an extremely good way for novice traders to build up confidence about the market in order to prevent their weak psychological profiles from affecting trading. In addition, they are also able to use tested and proven strategies to help ensure the success of their trades. Even experienced traders, in fact, adopt automated trading to make trading more efficient.
The most obvious benefit in automated trading, as noted earlier, is that it frees traders from time constraints by making the practice of constantly watching the market unnecessary. If a change in the market occurs when a trader is indisposed, the automated trading system will execute the buy and sell orders that have been specified earlier.
Furthermore, automated trading prevents fear and greed from affecting traders’ decisions. Emotion is one of the biggest factors that affect profitability: fear and greed can cause us to close our positions prematurely or overtrade. In automated trading, however, computer algorithms replace the human element that leads to this emotional threat to profits. Back to Automated Trading
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Accepted: 25 September 2009
Published: 25 September 2009
Fondo
Parameter optimization in the process of inverse treatment planning for intensity modulated radiation therapy (IMRT) is mainly conducted by human planners in order to create a plan with the desired dose distribution. To automate this tedious process, an artificial intelligence (AI) guided system was developed and examined.
The AI system can automatically accomplish the optimization process based on prior knowledge operated by several fuzzy inference systems (FIS). Prior knowledge, which was collected from human planners during their routine trial-and-error process of inverse planning, has first to be "translated" to a set of "if-then rules" for driving the FISs. To minimize subjective error which could be costly during this knowledge acquisition process, it is necessary to find a quantitative method to automatically accomplish this task. A well-developed machine learning technique, based on an adaptive neuro fuzzy inference system (ANFIS), was introduced in this study. Based on this approach, prior knowledge of a fuzzy inference system can be quickly collected from observation data (clinically used constraints). The learning capability and the accuracy of such a system were analyzed by generating multiple FIS from data collected from an AI system with known settings and rules.
Conclusión
The study demonstrated a feasible way to automatically perform parameter optimization of inverse treatment planning under guidance of prior knowledge without human intervention other than providing a set of constraints that have proven clinically useful in a given setting.
Introducción
Inverse treatment planning has been widely used in the optimization of intensity-modulated radiation therapy (IMRT) to achieve the desired dose distribution by balancing the priorities between planning target and critical organs [ 1 ]. Bortfeld et. Alabama. [ 2 ] discussed multiple treatment techniques to reduce the delivery time. A remaining goal is to reduce the planning time by automating parts of the planning process. Most current IMRT treatment planning systems provide an interactive user interface to optimize the IMRT plan by editing the dose-volume points and priority weights for each anatomical structure, such as planning target volume (PTV) and organs at risk (OAR) online, an approach commonly named "constraint based optimization". The purpose of the inverse planning optimization is to find the solution in this defined space in order to minimize the values of the objective function. If the minimum value is found in this defined space, the achieved dose distribution is optimal. In brief, the parameter optimization of inverse planning consists of three steps: (1) determine the candidate values for those parameters (constraints and priorities) making up an objective function which is done by human planners; (2) resolve the objective function; and (3) evaluate the resulting dose plan according to certain criteria. These three steps are performed sequentially and repetitively until an optimal solution is found. Based on own experiences and judging from the published experience of others [ 3. 4 ] conventional constraint based optimization often needs adjustments of constraints in an iterative fashion for most new cases and is therefore time consuming. The reasons for this need for interactivity are both technical and clinical. On a technical level, based on their values, 2D intensity maps can be generated using one type of general optimization algorithms (deterministic and stochastic approaches). Due to certain limitations of the optimization algorithms, frequently a sub-optimal solution is achieved. Limitations for IMRT optimization algorithms are, among others, that negativity of the intensity map is not allowed, that the capability of the planning system find/reach global extrema is limited, that local extrema "trap" the system, that optimization is often performed regarding fluence and not taking into account limitations of segmentation at an earlier stage in the optimization process, etc. On a clinical level, an optimal solution, however, is not only defined by a minimum of the cost function but it has to be related with the individual clinical case and many parameters not included in the cost function itself. This explains why, in addition to improve the processing of the cost function, inserting "human knowledge" into the process may further shorten the hands-on time during treatment planning.
Substantial effort was made to automate this process under the guidance of human knowledge. Li and Yin introduced the fuzzy logic theory in converting the linguistic expression of human knowledge into the trading-off procedure of parameter optimization in inverse planning [ 5 ]. They demonstrated that human knowledge can be properly handled by fuzzy logic and applied to inverse treatment planning. Later they employed an 'original' fuzzy inference system (oFIS) to simulate the parameter optimization procedure of inverse planning to replace the routine procedure performed by a human planner [ 6. 7 ]. Most recently, this AI approach was implemented in a clinical treatment planning system. Based on this platform several clinical cases were examined, which indicated that the dose plans achieved by the AI-approach were comparable or improved over those achieved by human planners in most of the tested cases [ 8 ].
The model parameters of the fuzzy inference system still had to be determined manually by a single human expert in a trial-and-error manner based on clinical knowledge ("rules" have to be created directly) and represent knowledge of one single planner only. To make this model selection procedure convenient for clinical use, a learning technique based on neuro-fuzzy systems originally proposed for intelligence control was used for the current study. Based on this approach, a fuzzy inference system can be automatically built from practical data ('rules' are created by a neuro-fuzzy function approximation system, based on "constraints" as usually used in an inverse treatment planning process) without further human intervention.
The neural-fuzzy system "Neuro-Fuzzy Function Approximation (NEFPROX)" in the open source software NFIDENT [ 9 – 12 ] used for this study is briefly introduced. We report the results of this study to evaluate the learning capability of this technique by comparing oFIS with our adaptive neuro fuzzy inference system (ANFIS, trained by oFIS) on a system-level by analyzing rules and on an operational level on one single clinical case. In a second step, we analyzed multiple clinical examples, optimized with ANFIS which was then built and trained by human knowledge and embedded into a commercial treatment planning system (TPS).
The purpose of the study was to establish and evaluate a system that reduces the amount of interaction between a human planner and an inverse treatment planning system during the iterative process of generating inverse treatment plans.
materiales y métodos
Introduction of the fuzzy inference system (FIS) concept
In 1965, Zadeh proposed a new approach to characterize non-probabilistic uncertainties which is called fuzzy sets [ 13 – 16 ]. This concept found various industrial applications including automatic control, signal processing and decision-making, to name a few. In simulating the reasoning process which is generally conducted by a human, the fuzzy inference system (FIS) was developed by Mamdani which was later implemented in various industrial applications [ 17 – 19 ]. A Mamdani-type FIS consists of three components: fuzzifier, inference engine, and defuzzifier as shown in figure 1a.
Fuzzy inference systems . (a) A Mamdani-type FIS and (b) a fuzzy inference system as neural network.
The fuzzifier processes the inputs according to the membership function for the inputs. The inference part handles the resulting values and according to the rule base the consequences are computed. The consequences are then converted to the final outputs by the defuzzifier. The behavior of a fuzzy inference system mainly depends on the constituents of the rules, such as fuzzy sets for antecedent and consequent parts of a rule. Based on these fuzzy sets, different spaces for input and output variables are partitioned. According to this partition, proper functions are created to map input/output spaces to real numbers called membership values.
Introduction of an adaptive neuro-fuzzy inference system (ANFIS)
A fuzzy inference system can be presented in a neural network form as shown in figure 1b. Such a node-oriented representation is often used for defining a neural network. The intermediate output values of the membership functions and the subsequent logical operations are labeled by circle nodes. The connections are selected in a way that they represent the rule base of the fuzzy system. The basic of fuzzy rules is the binary logic (IF. AND. THEN. ). The difference to the binary logic is that the conditions and the results are linguistic variables or terms which reflect fuzzy descriptions of states, so not only 0 or 1 but also values in between.
Based on the network representation, the structure and parameters can be derived from sample data using network-based learning approaches, such as the back-propagation algorithms. A well known neuro-fuzzy system, the adaptive neural fuzzy inference system (ANFIS), was proposed for function approximation [ 20 – 23 ]. It is limited to a special type of FIS proposed by Sugeno [ 24. 25 ].
In our study, we used the practical neuro-fuzzy system NEFPROX [ 9 – 12 ] which was developed for the model selection of different types of FIS's with hybrid learning algorithms. The algorithm addresses the learning ability of the structure, in which the properties of fuzzy rules are determined. The learning step of these properties is based on the distribution between input and output variables from training data sets. NEFPROX analyses every input-output relation and if no rule already existing in the FIS reflects this behavior, the system creates a new rule which is described by Nauck et al. [ 11 ]. The array of input-output relationships created by looking at sequential repetitions of the key element of the decision process to be modelled is then called a "pattern" and from this pattern, fuzzy rules are created In detail the learning algorithm of NEFPROX took one line of the pattern of the training set and searched for each input unit the corresponding membership function. If no rule was found which contained the specific input value and a compatible membership function, NEFPROX creates a new rule node and connected it to the output nodes. For each of these connections NEFPROX searched for a suitable fuzzy weight. This rule creation process was continued until all patterns were analyzed. When, as a practical example, applied to treatment planning, the fuzzy system takes the constraints (which effectively are desired dose points on a certain DVH) as input vectors, then records the resulting respective dose points after one treatment planning iteration as output vectors. Then several planning iterations are performed until a plan that is satisfactory to the planner is achieved. The fuzzy system then takes all the relationships between in - and output vectors over this iterative process (the signature "pattern" of relationships) and creates a set of fuzzy rules to reflect this relationship.
Experiment design
We divided the experiments into three parts. First we tested the general learning behavior of ANFIS by comparing ANFIS (trained by oFIS) with oFIS based on an arbitrary sets of input vectors resulting in respective output vectors (not a clinical dataset). Then we compared a clinical prostate case planned using oFIS and ANFIS (trained by oFIS). And finally we compared multiple treatment cases (prostate, head and neck. ) planned with oFIS, ANFIS (trained by human knowledge) and human planners.
Training of ANFIS with the original FIS (oFIS), analysis of the "response" of ANFIS rules as a consequence of changes in oFIS rules
The open source software NFIDENT was used to implement the hybrid learning approach NEFPROX for this study. In a first non-clinical analysis, the ANFIS model was created by the software based on training data. This training data were generated by an existing FIS (oFIS) with known model parameters which were specified manually/directly by a human expert. This approach provides the opportunity to directly assess the process of automatic rule generation in the ANFIS model by comparing the randomly generated sample data consist of input and output vectors, which represent the input-output relationship of oFIS. The input/output data space was uniformly sampled. The data samples were divided into three data sets for model training, validation, and testing purposes. The generalization capability of the new ANFIS was properly controlled by the validation data set. The performance of the model was examined by the testing data set.
Two different analyses proved the ability of NFIDENT to learn the behaviour of the oFIS based on the training/validation data sets. The first analysis addresses the learning efficiency of the rules and the membership functions from the original FIS. The oFIS was edited by using a variable number of rules and was then compared to the resulting ANFIS (Table 1 ). To analyze the ANFIS' ability to learn membership functions, we changed the behavior of the oFIS by changing numbers in the membership functions and compared the resulting ANFIS (table 2 and 3 ). To quantify the training error, the mean percentual difference between output vectors of original (manually created FIS) and trained FIS (ANFIS) for a given (identical) set of input vectors was recorded, thus providing an estimate of the "similarity" of the behaviour of the manually created oFIS and the new FIS (ANFIS) trained by the original FIS.
The results of experimental test in investigating capability of NEFPROX in learning structure of a FIS
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