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Data science in simple words means the study of data. It entails developing methods of recording, storing, and analyzing data to successfully bring out useful information. Data Science put together and make use of several statistical procedures. The procedures cover data modeling, data transformations, machine learning, statistical operations including descriptive and inferential statistics. For all data scientists statistics is the primary asset.
With the biggest innovation of the time, that is a cryptocurrency, the demands for controlling data online have become a crucial challenge. Various techniques are put forward by Data Science to identify a group of people and providing them with the best possible security from fraud activities.
However, the application of data science is not just concerned with one field rather its application disseminated across various sectors.
Healthcare sector- The biggest application of Data Science is in healthcare. The accessibility of large datasets of patients can be used to build a Data Science approach to identify the diseases at a very early stage. Healthcare is one of the biggest sectors for providing opportunities for the professional who can use their medical expertise with Data Science and provide immediate help to the suffering patients.
Arms and Weapons- Data Science can help in building various automated solutions to identify any attack at a very early stage. Other than that Data Science can help in constructing automated weapons that will be smart enough to identify when to fire and when not to.
Banking and Finance- Data Science in the Banking and Finance sector can be used in managing the money effectively to invest in the right places based on Data Science predictions for best results.
Other than the above sectors Data Science is also applied in Automobile Industry like self-driving cars, Fixed destination cabs as well as in Power and Energy. Data Science can predict the maximum safest potential and can help in building AI bots that can easily handle enormous power sources.
The implementation of Data Science cannot be ignored as it is already in action in the present stage. When you look for something in Myntra or Flipkart and then you get similar recommendations or similar advertisements for whatever you have searched on the internet is all about Data Science. The whole world is operated by Data Science. For every single search in Google, the process of data science is activated.
The future of data science is growing. According to Cloud Vendor Domo even when a person accounts for the Earths entire population, the average person is expected to generate 1.7 megabytes of data per second by the end of 2020.
An overreaching motif today and moving ahead, big data is assured to play an authoritative role in the future. Data will stipulate modern health care, finance, business management, marketing, government, energy, and manufacturing. The scale of big data is truly staggering as it has already entwined itself in the fundamental aspect of business as well as personal life.
Like almost all businesses prime concern is tech, there is a high possibility of the growth of data science jobs.
Artificial Intelligence is the most impactful technology among others that data scientists will run up into. Today Ai is already refining the business operations and assures to be a major trend in the near future. The applications of AI in todays world have driven the adoption of other AI applications such as machine learning, deep learning and this will lead the way as the future of data science. Machine learning is the aptitude of statistical models to develop the capabilities and improve the performance with time in the absence of programmed instructions. This principle can be seen in the chess machine that is developed by Googles DeepMind unit the AlphaZero. The AlphaZero improves on its other computerized chess-playing peers in the absence of instructions is an example of how it learns from its movements to reach the most desired outcome.
As a greater number of businesses are merging with AI and data-based technologies at a high rate there is a need for a greater number of data scientists to help guide the initiatives.
Data science is a leviathan pool of multiple data operations that include statistics and machine learning. Machine Learning algorithms are very much dependent on data. Therefore, machine learning is the primary contributor to the future of data science. In particular data science covers the areas like Data Integration, Distributed Architecture, Automating Machine learning, Data Visualisation, Dashboards and BI, Data Engineering, Deployment in production mode, Automated, data-driven decisions.
While IT-focused jobs have been all the rage over the last two decades the rate of growth in the sector has been projected to be about 13% by the Bureau of Labor Statistics. It is still higher than the average rate of growth for all other sectors. However, data science has seen an explosive growth of over 650% since 2012 based on an analysis done on LinkedIn. The role of a Data Scientist has projected forward to one of the most in-demand jobs and ranks second to machine learning engineer- which is a job that is adjacent to a data scientist.
In the upcoming time, Data Scientists will have the ability to take on areas that are business-critical as well as several complex challenges. This will facilitate the businesses to make exponential leaps in the future. Companies in the present are facing a huge shortage of data scientists. However, this is set to change in the future.
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Artificial intelligence (AI) has quickened its progress in 2021.
A new administration is in place in the US and the talk is about a major push forGreen Technologyand the need to stimulate next generation infrastructure including AI and 5G to generate economic recovery withDavid Knight forecasting that 5G has the potential - thepotential- to drive GDP growth of 40% or more by 2030.TheBiden administration has statedthat it will boost spending in emerging technologies that includes AI and 5G to $300Bn over a four year period.
On the other side of the Atlantic Ocean, the EU have announced aGreen Dealand also need to consider theEuropean AI policyto develop next generation companies that will drive economic growth and employment. It may well be that theEU and US(alongside Canada and other allies) will seek ways to work together on issues such as 5G policy and infrastructure development. TheUK will be hosting COP 26and has also made noises about AI and 5G development.
The world needs to find a way to successfully end the Covid-19 pandemic and in the post pandemic world move into a phase of economic growth with job creation. An opportunity exists for a new era of highly skilled jobs with sustainable economic development built around next generation technologies.
AI and 5G: GDP and jobs growth potential plus scope to reduce GHG emissions (source for numbers PWC / Microsoft, Accenture)
The image above sets out the scope for large reductions in emissions of GHGs whilst allowing for economic growth.
GDP and jobs growth will be very high on the post pandemic agendas of governments around the world. At the same time those economies that truly proposer and grow rapidly in this decade will be those who adopt Industry 4.0 technology and in turn will lead to a shift away from the era of heavy fossil fuel consumption towards a digital world that may be powered by renewable energy and with transportation that is either heavily electric or over time, hydrogen based.
2021 will mark the continued acceleration of Digital Transformation across the economy.
Firms will be increasingly "analytics driven" (it needs to be stressed that analytics rather than data driven is the key term).Data is the fuelthat needs to be processed.Analytics provide the ability for organisations to make actionable insights.
Source for image above Lean BI
The examples of how Machine to Machine Communication at the Edge enabled by AI could work maybe demonstrated by the following image as an example:
In the image above the Machine to Machine communication allows for broadcast across the network that a person has been detected stepping onto the road so that even the car that does not have line of sight of the person is aware of their presence
It is important to note that AI alongside 5G networks will be at the heart of this transition to the world ofIndustry 4.0.
5G will play an important role as 5G networks are not only substantially faster than 4G networks, but they also enablesignificant reductions in latency in turn allowing for near real-time analytics and responses, and also enable far greater capacity for connection thereby facilitating massive machine to machine communication forIoT devices on the Edge of the network(closer to where the data is created on the device).
The image below sets out the speed advantage of 5G networks relative to 4G.
Source for image above Thales Group
However, as noted above 5G has many more advantages over 4G than speed alone as shown in the image below:
Source for image above Thales Group
The growth in Edge Computing will reduce the amount of data being sent backwards and forwards between a remote cloud server and thereby make the system more efficient.
Source for image above Thales Group
The economic benefits of 5G are set out below:
$13.2 Trillion dollars of global economic output
22.3 Million new jobs created
$2.1 Trillion dollars in GDP growth
Towards AI at the Edge (AIIoT)
To date AI has been most pervasive and effective in the areas of Social Media and Ecommerce giants whose large digital data sets give them an advantage and whereedge casesdon't matter so much in terms of their consequences. No fatalities, injuries or material damages arise from an incorrect recommendation for a video, a post, or an item of clothing, other than a bad user experience.
However, when we seek to scale AI into the real world, edge cases and interpretability matter. Issues such as causality and explainability become key in areas such as autonomous vehicles and robots and also in healthcare.
Equally data privacy and security also really matter. On the one hand as noted above, data is the fuel for Machine Learning models. However, on the other hand in areas such as healthcare much of that data is often siloed and decentralised plus also protected by strict privacy rules in the likes of the US (HIPAA) and Europe (GDPR). It is also an issue in areas such as Finance and Insurance where data privacy and regulation are of significant importance to the operations of financial services firms.
This is an area whereFederated LearningwithDifferential Privacycould play a big role in scaling Machine Learning across areas such as healthcare and financial services.
Source for image above NVIDIA What is Federated Learning?
It is also an area where the US and Europe could work together to enable collaborative learning and help scale Machine Learning that also provides for Data Security and Privacy for end users (patients). The Healthcare sector around the world is at breakpoint due to the strains of the Covid-19 pandemic and augmenting our healthcare workers with AI to reduce the strain upon them whilst ensuring that patient data security is maintained will be key to transforming our Healthcare systems to reduce the strain on them and deliver better outcomes for the patient.
Source for Image above TensorFlow Federated
For more on Federated Learning see:Federated Learning an Introduction.
In relation to AI, we will need to move away from the giant models and techniques that were predominant in the last decade towards neural compression (pruning)that in turn will enable models to operate more efficiently on the Edge and help preserve battery life of devices and also reduce carbon footprint with reduced energy consumption.
Furthermore, we won't only requireDeep Learning models that may inference on the Edge, but also models thatmay continue to learn on the Edge, on the fly, from smaller data sets and respond dynamically to their environments. This will be key to enabling effective autonomous systems such as autonomous vehicles (cars, drones) and also robots.
Solving for these challenges will be key to enabling AI to scale beyond Social Media and Ecommerce across the sectors of the economy.
It is no surprise that the most powerful AI companies today and last few years tend to be from the Ecommerce and social media sector.
Furthermore, the images below from Valuewalk show how ByteDance (owner of TikTok) is the world's most valuable Unicorn and an AI company.
Source for image above Valuewalk, Tipalti, The Most Valuable Unicorn in the World 2020
Venture Capitalist and Angel Investors should also try to understand that in order to scale AI startup ventures access to usable data and meeting the requirements of their customer in terms of usability (which may include some or all of transparency, causality, explainability, model size, ethics) are key for many sectors.
The number of connected devices and volume of data is forecast to grow dramatically as Digital Technology continues to expand its reach for example the image below shows a forecast from Statista for 75 Billion internet connected devices by 2025, an average of over 9 per person on the planet!
Data will grow but an increasing amount of data will be decentralised data dispersed around the Edge.
Source for image above IDC
In factIDC forecast that" Theglobal dataspherewill grow from 45 zettabytes in 2019 to 175 by2025. Nearly 30% of the world's data will need real-time processing. ... Many of these interactions are because of the billions of IoT devices connected across the globe, which are expected to create over 90 ZB of data in2025."
Illustration of the AI IoT across the Edge
Source for infographic images below:Iman Ghosh VisualCapitalist.com
In the past decade key Machine Learning tools such as XG Boost, Light Gradient Boosting Machines and Cat Boost emerged (approximately 2015 to 2017) and these tools will continue to be popular with Data Scientists for powerful insights with structured data using supervised learning. No doubt we will see continued enhancements in Machine Learning tools over the next few years.
In relation to areas such as Natural Language Processing (NLP), Computer Vision and Drug Discovery efforts Deep Learning will continue to be the effective tool. However, it is submitted that increasingly the techniques will move towards the following:
Transformers(including inComputer Vision);
Neuro Symbolic AI(hybrid AI that combines Deep Learning with symbolic Logic);
Neuroevoutionary(hybrid approaches that combinedDeep Learning with Evolutionary algorithm approaches);
Some or all of the above combined withDeep Reinforcement Learning.
This will lead to an era ofBroad AIas AI starts to move beyond narrow AI (performing just one task) and starts working with multitasking but not at the level where AI can match the human brain (AGI).
My own work is focused on the above hybrid approaches for Broad AI as we seek to find ways to scale AI across the economy beyond Social Media and Ecommerce the above will be key to enabling true Digital Transformation with AI across traditional sectors of the economy and enabling our moving into the era of Industry 4.0.
Source for Image above David Cox, IBM Watson
MIT IBM Watson Lab define Broad AIand the types of AI as follows:
"Narrow AIis the ability to perform specific tasks at a super-human rate within various categories, from chess, Jeopardy!, and Go, to voice assistance, debate, language translation, and image classification."
"Broad AIis next. Were just entering this frontier, but when its fully realized, it will feature AI systems that use and integrate multimodal data streams, learn more efficiently and flexibly, and traverse multiple tasks and domains. Broad AI will have powerful implications for business and society."
"Finally,General AIis essentially what science fiction has long imagined: AI systems capable of complex reasoning and full autonomy. Some scientists estimate that General AI could be possible sometime around 2050 which is really little more than guesswork. Others say it will never be possible. For now, were focused on leading the next generation of Broad AI technologies for the betterment of business and society."
I would addArtificial Super Intelligence(or Super AI) to the list above as this is a type of AI that often gains much attention in Hollywood movies and television series.
Whether one views 2021 as the first year of a decade or not, 2021 will mark a year for reset across the economy and hopefully one whereby we start to move beyond the Covid pandemic to a post pandemic world.
California will remain as a leading area for AI development with the presence of Stanford, UC Berkley, Caltech, UCLA, and University of San Diego. However, other centres for AI will continue to grow around the US and the world, for example Boston, Austin, Toronto, London, Edinburgh, Oxford, Cambridge, Tel Aviv, Dubai, Abu Dhabi, Singapore, Berlin, Paris, Barcelona, Madrid, Lisbon, Sao Paulo, Tallinn, Bucharest, Kyiv / Kharkiv, Moscow and of course across China (many other examples of cities could be cited too). AI will become a pervasive technology that is increasingly in the devices (including within our mobile phones) that we interact with everyday and not just when we enter our social media accounts or go online to shop.
It will also mark a reset for AI to be increasingly on the Edge and across the "real-world" sectors of the economy with the emergence of Broad AI to take over from Narrow AI as we move across the decade.
Smaller models will be more desirable / more useful
GPT-3is an exciting development in AI and shows the potential for Transformer models, however, in the future small will be beautiful and crucial. The human brain does not require the amount of server capacity of GPT-3 and uses far less energy. For AI to scale across the edge we'll need powerful models that are energy efficient and optimised to work on small devices. For exampleMao et al. set out LadaBERT: lightweight adaptation of the BERT ( a large Transformer language model) through hybrid model compression.
The authors note "...a major blocking issue of applying BERT to online services is that it is memory-intensive and leads to unsatisfactory latency of user requests, raising the necessity of model compression. Existing solutions leverage the knowledge distillation framework to learn a smaller model that imitates the behaviours of BERT."
"However, the training procedure of knowledge distillation is expensive itself as it requires sufficient training data to imitate the teacher model."
"In this paper, we address this issue by proposing a hybrid solution named LadaBERT (Lightweight adaptation of BERT through hybrid model compression), which combines the advantages of different model compression methods, including weight pruning, matrix factorization and knowledge distillation. LadaBERT achieves state-of-the-art accuracy on various public datasets while the training overheads can be reduced by an order of magnitude."
Reducing training overheads and avoiding unsatisfactory latency of user requests will also be a key objective of Deep Learning development and evolution over the course of 2021 and beyond.
My Vision of Connectionism: Connecting one human to another (we're all human beings), connecting AI with AI, and AI with humans all at the level of the mind.
When I adopted the @DeepLearn007 handle on Twitter many years ago, I was inspired by the notion ofconnectionism, and the image that I selected for the account illustrates how 2 human beings could connect at the level of the brain and how the exchange of information, in effect ideas, drives innovation and the development of humanity. In the virtual world much of that occurs at the level of data and the analytical insights that we gain from that data through application of AI (Machine Learning and Deep Learning) to generate responses.
I remain a connectionist, albeit an open minded one. I believe that Deep Neural Networks will remain very important and the cornerstone of AI development but just like Deep Reinforcement Learning combined Reinforcement Learning with Deep Learning to very powerful effect with the likes ofAlphaGo,AlphaZero, andMuZeroresulting, so too developing hybrid AI that combines Deep Learning with Symbolic and Evolutionary approaches will lead to exciting new product developments and enable Deep Learning to scale beyond Social Media and Ecommerce sectors where the likes of medics and financial services staff wantcausal inferenceandexplainabilityfor trust in the AI decision making. For example,Microsoft Researchstate that "understanding causality is widely seen as a key deficiency of current AI methods, and a necessary precursor for building more human-like machine intelligence."
Furthermore, in order for autonomous vehicles to truly take off we'll need the model explainability for situations where things have gone wrong in order to understand what happened and how we may reduce the probability of the same outcome in the future.
The next generation of AI will be in the direction towards the era Broad AI and the adventure will be here in 2021 as we move towards the Edge, towards a better world as we move beyond the scars and challenges of 2020. The journey may require scaled up 5G networks around the world to really transform the broader economy and that may only really start to happen at the end of the year and beyond but the direction of the pathway is clear.
The exciting potential for healthcare, smart industry, smart cities, smart living, education, and every other sector of the economy will mean that a new of businesses will emerge that we cannot even imagine today.
Perhaps a good point to conclude is with the forecast from Ovum and Intel for the impact of 5G for the media sector (of courseAI will play a big role alongside 5Gin developing new hyper personalised services and products andhave a symbiotic relationship together).
Source for the image above:Intel Study Finds 5G will Drive $1.3 Trillion in New Revenues in Media and Entertainment Industry by 2028
A lot has been said about the capabilities of artificial intelligence, from humanoid robots, self-driving cars to speech recognition. However, one aspect of AI that often doesnt get spoken about is its carbon footprint. AI systems consume a lot of power, and resultant of this generate large volumes of carbon emissions that harm the environment and further accelerate climate change.
It is interesting to note the duality of AI in terms of its effect on the environment. On the one hand, it helps in devising solutions that can reduce the effects of climate and ecological change. Some of which include smart grid design, development of low-emission infrastructure, and climate change predictions.
But, on the other hand, AI has a significant carbon footprint that is hard to ignore.
For instance, in a 2019 study, a research team from the University of Massachusetts had analysed several natural language processing training models. The energy consumed by these models was converted into carbon emissions and electricity cost. It was then found that training an AI language-processing system generates an astounding 1,400 pounds (635 kg) of emission. The study further noticed that this number can even reach up to 78,000 pounds (over 35,000 kg) depending on the scale of the AI experiment and the source of power used. This is equivalent to 125 round trip flights between New York and Beijing.
Notably, the centre of the whole Timnit Gebru-Google controversy is also a study titled, On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? This paper, co-authored by Gebru raised questions about AI language models being too big, and whether tech companies are doing enough to reduce the arising potential risk. Apart from shining light on how such models perpetually create abusive languages, hate speeches, stereotypes, and other microaggressions towards specific communities, the paper also spoke of the AIs carbon footprint and how it disproportionately affects the marginalised communities, much more than any other group of people.
The paper pointed out that the number of resources required to build and sustain such large models only benefitting the large corporations and wealthy organisations, but the resulting repercussions of climate change were borne by the marginalised communities. It is past time for researchers to prioritise energy efficiency and cost to reduce negative environmental impact and inequitable access to resources, the paper said.
This OpenAI graph also shows how since 2012, the amount of computing power in training some of the largest models such as AlphaZero has been increasing exponentially with a 3.4 month doubling time. This is higher than Moores law two-year doubling period.
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To address this issue, in September 2019, employees of tech giants such as Amazon, Google, Microsoft, Facebook, and Twitter, have joined the brimming worldwide march against climate change and demanded from their employers to issue an assurance towards reducing emissions to zero by 2030. This would require them to cut contracts with fossil fuel companies as well as stop the exploitation of climate refugees. In a very strong-worded demand that called out Techs dirty role in climate change, the coalition had written that the tech industry has a massive carbon footprint, often obscured behind jargon like cloud computing or bitcoin mining, along with depictions of code and automation as abstract and immaterial.
Considering the growing conversation around climate change, a movement called Green AI was also started by the Allen Institute of Artificial Intelligence through their research. This paper proposed undertaking AI research that yields desired results but without increasing computational cost, and in some cases, even reducing it. As per the authors of this paper, the goal should be to make AI greener and inclusive, as opposed to Red AI that currently dominates the research industry. Red AI has been referred to the research practices that use massive computational power to obtain state-of-the-art results in terms of accuracy and efficiency.
In a 2019 paper, co-founders of AI Now Institute, Roel Dobbe and Meredith Whittaker, gave seven recommendations that could help draft a tech-aware climate policy and climate-aware tech policy. They included
There is a lot to be done on recognising, understanding, and acting against the implications of AI carbon footprint. An ideal situation would be bigger tech companies to take the first step in the direction for others to follow.
I am a journalist with a postgraduate degree in computer network engineering. When not reading or writing, one can find me doodling away to my hearts content.
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AI's Carbon Footprint Issue Is Too Big To Be Ignored - Analytics India Magazine
When Mohammad Haft-Javaherian, a student at the Massachusetts Institute of Technology, attended MITs Green AI Hackathon in January, it was out of curiosity to learn about the capabilities of a new supercomputer cluster being showcased at the event. But what he had planned as a one-hour exploration of a cool new server drew him into a three-day competition to create energy-efficient artificial-intelligence programs.
The experience resulted in a revelation for Haft-Javaherian, who researches the use of AI in healthcare: The clusters I use every day to build models with the goal of improving healthcare have carbon footprints, Haft-Javaherian says.
The processors used in the development of artificial intelligence algorithms consume a lot of electricity. And in the past few years, as AI usage has grown, its energy consumption and carbon emissions have become an environmental concern.
I changed my plan and stayed for the whole hackathon to work on my project with a different objective: to improve my models in terms of energy consumption and efficiency, says Haft-Javaherian, who walked away with a $1,000 prize from the hackathon. He now considers carbon emission an important factor when developing new AI systems.
But unlike Haft-Javaherian, many developers and researchers overlook or remain oblivious to the environmental costs of their AI projects. In the age of cloud-computing services, developers can rent online servers with dozens of CPUs and strong graphics processors (GPUs) in a matter of minutes and quickly develop powerful artificial intelligence models. And as their computational needs rise, they can add more processors and GPUs with a few clicks (as long as they can foot the bill), not knowing that with every added processor, theyre contributing to the pollution of our green planet.
The recent surge in AIs power consumption is largely caused by the rise in popularity of deep learning, a branch of artificial-intelligence algorithms that depends on processing vast amounts of data. Modern machine-learning algorithms use deep neural networks, which are very large mathematical models with hundreds of millionsor even billionsof parameters, says Kate Saenko, associate professor at the Department of Computer Science at Boston University and director of the Computer Vision and Learning Group.
These many parameters enable neural networks to solve complicated problems such as classifying images, recognizing faces and voices, and generating coherent and convincing text. But before they can perform these tasks with optimal accuracy, neural networks need to undergo training, which involves tuning their parameters by performing complicated calculations on huge numbers of examples.
To make matters worse, the network does not learn immediately after seeing the training examples once; it must be shown examples many times before its parameters become good enough to achieve optimal accuracy, Saenko says.
All this computation requires a lot of electricity. According to a study by researchers at the University of Massachusetts, Amherst, the electricity consumed during the training of a transformer, a type of deep-learning algorithm, can emit more than 626,000 pounds of carbon dioxidenearly five times the emissions of an average American car. Another study found that AlphaZero, Googles Go- and chess-playing AI system, generated 192,000 pounds of CO2 during training.
To be fair, not all AI systems are this costly. Transformers are used in a fraction of deep-learning models, mostly in advanced natural-language processing systems such as OpenAIs GPT-2 and BERT, which was recently integrated into Googles search engine. And few AI labs have the financial resources to develop and train expensive AI models such as AlphaZero.
Also, after a deep-learning model is trained, using it requires much less power. For a trained network to make predictions, it needs to look at the input data only once, and it is only one example rather than a whole large database. So inference is much cheaper to do computationally, Saenko says.
Many deep-learning models can be deployed on smaller devices after being trained on large servers. Many applications of edge AI now run on mobile devices, drones, laptops, and IoT (Internet of Things) devices. But even small deep-learning models consume a lot of energy compared with other software. And given the expansion of deep-learning applications, the cumulative costs of the compute resources being allocated to training neural networks are developing into a problem.
Were only starting to appreciate how energy-intensive current AI techniques are. If you consider how rapidly AI is growing, you can see that we're heading in an unsustainable direction, says John Cohn, a research scientist with IBM who co-led the Green AI hackathon at MIT.
According to one estimate, by 2030, more than 6 percent of the worlds energy may be consumed by data centers. I don't think it will come to that, though I do think exercises like our hackathon show how creative developers can be when given feedback about the choices theyre making. Their solutions will be far more efficient, Cohn says.
CPUs, GPUs, and cloud servers were not designed for AI work. They have been repurposed for it, as a result, are less efficient than processors that were designed specifically for AI work, says Andrew Feldman, CEO and cofounder of Cerebras Systems. He compares the usage of heavy-duty generic processors for AI to using an 18-wheel-truck to take the kids to soccer practice.
Cerebras is one of a handful of companies that are creating specialized hardware for AI algorithms. Last year, it came out of stealth with the release of the CS-1, a huge processor with 1.2 trillion transistors, 18 gigabytes of on-chip memory, and 400,000 processing cores. Effectively, this allows the CS-1, the largest computer chip ever made, to house an entire deep learning model without the need to communicate with other components.
When building a chip, it is important to note that communication on-chip is fast and low-power, while communication across chips is slow and very power-hungry, Feldman says. By building a very large chip, Cerebras keeps the computation and the communication on a single chip, dramatically reducing overall power consumed. GPUs, on the other hand, cluster many chips together through complex switches. This requires frequent communication off-chip, through switches and back to other chips. This process is slow, inefficient, and very power-hungry.
The CS-1 uses a tenth of the power and space of a rack of GPUs that would provide the equivalent computation power.
Satori, the new supercomputer that IBM built for MIT and showcased at the Green AI hackathon, has also been designed to perform energy-efficient AI training. Satori was recently rated as one of the worlds greenest supercomputers. Satori is equipped to give energy/carbon feedback to users, which makes it an excellent laboratory for improving the carbon footprint both AI hardware and software, says IBMs Cohn.
Cohn also believes that the energy sources used to power AI hardware are just as important. Satori is now housed at the Massachusetts Green High Performance Computing Center (MGHPCC), which is powered almost exclusively by renewable energy.
We recently calculated the cost of a high workload on Satori at MGHPCC compared to the average supercomputer at a data center using the average mix of energy sources. The results are astounding: One year of running the load on Satori would release as much carbon into the air as is stored in about five fully-grown maple trees. Running the same load on the 'average' machine would release the carbon equivalent of about 280 maple trees, Cohn says.
Yannis Paschalidis, the Director of Boston Universitys Center for Information and Systems Engineering, proposes a better integration of data centers and energy grids, which he describes as demand-response models. The idea is to coordinate with the grid to reduce or increase consumption on-demand, depending on electricity supply and demand. This helps utilities better manage the grid and integrate more renewables into the production mix, Paschalidis says.
For instance, when renewable energy supplies such as solar and wind power are scarce, data centers can be instructed to reduce consumption by slowing down computation jobs and putting low-priority AI tasks on pause. And when theres an abundance of renewable energy, the data centers can increase consumption by speeding up computations.
The smart integration of power grids and AI data centers, Paschalidis says, will help manage the intermittency of renewable energy sources while also reducing the need to have too much stand-by capacity in dormant electricity plants.
Scientists and researchers are looking for ways to create AI systems that dont need huge amounts of data during training. After all, the human brain, which AI scientists try to replicate, uses a fraction of the data and power that current AI systems use.
During this years AAAI Conference, Yann LeCun, a deep-learning pioneer, discussed self-supervised learning, deep-learning systems that can learn with much less data. Others, including cognitive scientist Gary Marcus, believe that the way forward is hybrid artificial intelligence, a combination of neural networks and the more classic rule-based approach to AI. Hybrid AI systems have proven to be more data- and energy-efficient than pure neural-network-based systems.
It's clear that the human brain doesnt require large amounts of labeled data. We can generalize from relatively few examples and figure out the world using common sense. Thus, 'semi-supervised' or 'unsupervised' learning requires far less data and computation, which leads to both faster computation and less energy use, Cohn says.
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AI Could Save the World, If It Doesnt Ruin the Environment First - PCMag Portugal
According to his website, Gary Marcus, a notable figure in the AI community, has published extensively in fields ranging from human and animal behaviour to neuroscience, genetics, linguistics, evolutionary psychology and artificial intelligence.
AI and evolutionary psychology, which is considered to be a remarkable range of topics to cover for a man as young as Marcus.
Marcus, in his website, calls himself a scientist, a best-selling author, and an entrepreneur. And is also a founding member of Geometric Intelligence, a machine learning company acquired by Uber in 2016. However, Marcus is widely known for his debates with machine learning researchers like Yann Lecun and Yoshua Bengio.
Marcus leaves no stone unturned to flaunt his ferocity in calling out the celebrities of the AI community.
However, he also, call it an act of benevolence or finding a neutral ground, downplays his criticisms through his we agree to disagree tweets.
Last week, Marcus did what he does best when he tried to reboot and shake up AI once again as he debated Turing award winner Yoshua Bengio.
In this debate, hosted by Montreal.AI, Marcus, in his speech, criticized Bengio for not citing him in Bengios work and complained that it would devalue Marcus contribution.
Marcus, in his arguments, tried to explain how hybrids are pervasive in the field of AI by citing the example of Google, which according to him, is actually a hybrid between knowledge graph, a classic symbolic knowledge, and deep learning like a system called BERT.
Hybrids are all around us
Marcus also insists on the requirement of thinking in terms of nature and nurture, rather than nature versus nurture when it comes to the understanding of the human brain.
He also laments about how much of machine learning, historically, has avoided nativism.
Marcus also pointed out that Yoshua misrepresented him as saying deep learning doesnt work.
I dont care what words you want to use, Im just trying to build something that works.
While Marcus argued for symbols, pointing out that DeepMinds chess-winning AlphaZero program is a hybrid involving symbols because it uses Monte Carlo Tree Search. You have to keep track of your trees, and trees are symbols.
Bengio dismissed the notion that a tree search is a symbol system. Rather Its a matter of words, Bengio said. If you want to call those symbols, but symbols to me are different, they have to do with the discreteness of concepts.
Bengio also shared his views on how deep learning might be extended to dealing with computational capabilities rather than taking the old techniques and combining them with Neural Nets.
Bengio admitted that he completely agrees that a lot of current systems, which use machine learning, has also used a bunch of handcrafted rules and codes that were designed by people.
While Marcus pressed Bengio for hybrid systems as a solution, Bengio, patiently reminded how hybrid systems have already been built, which has led to Marcus admitting that he misunderstood Bengio!
This goof-up was followed by Bengios takedown of symbolic AI and why there is a need to move on from good old fashioned AI (GOFAI). In a nod to Daniel Kahnemann, Bengio, took the two-system theory to explain how richer representation is required in the presence of an abundance of knowledge.
To this Marcus quickly responded by saying, Now I would like to emphasise on our agreements. This was followed up by one more hour of conversation between the speakers and a Q&A session with the audience.
The debate ended with the moderator Vincent Boucher thanking the speakers for a hugely impactful debate, which was hugely pointless for a large part of it.
Gary Marcus has been playing or trying to play the role of an antagonist that would shake up the hype around AI for a long time now.
In his interview with Synced, when asked about his relationship with Yann Lecun, Marcus said that they both are friends as well as enemies. While calling out Lecun for making ad hominem attacks on him, he also approves many perspectives of his frenemies.
Deliberate or not, Marcus online polemics to bring down hype of AI, usually ends up hyping up his own antagonism. What the AI community needs is the likes of Nassim Taleb, who is known for his relentless, eloquent and technically intact arguments. Taleb has been a practitioner and an insider who doesnt give a damn about being an outsider.
On the other hand, Marcus calls himself a cognitive scientist, however, his contribution to the field of AI cannot be called groundbreaking. There is no doubt that Marcus should be appreciated for positioning himself in the line of fire in the celebrated era of AI. However, one cant help but wonder two things when one listens to Marcus antics/arguments:
There is a definitely a thing or two Marcus can learn from Talebs approaches in debunking pseudo babble. A very popular example could be that of Talebs takedown of Steven Pinker, who also happens to be a dear friend and mentor to Marcus.
That said, the machine learning research community, did witness something similar in the form of David Duvenaud and Smerity, when they took a detour from the usual we shock with you jargon research, and added a lot of credibility to the research community. While Duvenaud, trashed his own award-winning work, Stephen Smerity Merity, investigated his paper on the trouble with naming inventions and unwanted sophistication.
There is no doubt that there is a lot of exaggerations related to what AI can do. Not to forget the subtle land grab amongst the researchers for papers, which can mislead the community into thinking vanity as an indication of innovation. As we venture into the next decade, AI can use a healthy dose of scepticism and debunking from the Schmidhubers and Smeritys of its research world to be more reliable.
The rest is here:
For the five years, I've been working with Sophia, the world's most expressive humanoid robot (and the first robot citizen), and the other amazing creations of social robotics pioneer Dr. David Hanson. During this time, I've been asked a few questions over and over again.
Some of these are not so intriguing like, "Can I take Sophia out on a date?"
But there are some questions that hold more weight and lead to even deeper moral and philosophical discussions questions such as "Why do we really want robots that look and act like humans, anyway?"
This is the question I aim to address.
The easiest answer here is purely practical. Companies are going to make, sell and lease humanoid robots because a significant segment of the population wants humanoid robots. If some people aren't comfortable with humanoid robots, they don't need to buy or rent them.
I stepped back from my role as chief scientist of Hanson Robotics earlier this year so as to devote more attention to my role as CEO of SingularityNET, but I am still working on the application of artificial general intelligence (AGI) and decentralized AI to social robotics.
At the web summit this November, I demonstrated the OpenCog neural-symbolic AGI engine and the SingularityNET blockchain-based decentralized AI platform controlling David Hanson's Philip K. Dick robot (generously loaned to us by Dan Popa's lab at the University of Louisville). The ability of modern AI tools to generate philosophical ruminations in the manner of Philip K. Dick (PKD) is fascinating, beguiling and a bit disorienting. You can watch a video of the presentation here to see what these robots are like.
While the presentation garnered great enthusiasm, I also got a few people coming to me with the "Why humanoid robots?" question but with a negative slant. Comments in the vein of "Isn't it deceptive to make robots that appear like humans even though they don't have humanlike intelligence or consciousness?"
To be clear, I'm not in favor of being deceptive. I'm a fan of open-source software and hardware, and my strong preference is to be transparent with product and service users about what's happening behind the magic digital curtain. However, the bottom line is that "it's complicated."
There is no broadly agreed theory of consciousness of the nature of human or animal consciousness, or the criteria a machine would need to fulfill to be considered as conscious as a human (or more so).
And intelligence is richly multidimensional. Technologies like AlphaZero and Alexa, or the AI genomic analysis software used by biologists, are far smarter than humans in some ways, though sorely lacking in certain aspects such as self-understanding and generalization capability. As research pushes gradually toward AGI, there may not be a single well-defined threshold at which "humanlike intelligence" is achieved.
A dialogue system like the one we're using in the PKD robot incorporates multiple components some human-written dialogue script fragments, a neural network for generating text in the vein of PKD's philosophical writings and some simple reasoning. One thread in our ongoing research focuses on more richly integrating machine reasoning with neural language generation. As this research advances, the process of the PKD robot coming to "really understand what it's talking about" is probably going to happen gradually rather than suddenly.
It's true that giving a robot a humanoid form, and especially an expressive and reactive humanlike face, will tend to bias people to interact with the robot as if it really had human emotion, understanding and culture. In some cases this could be damaging, and it's important to take care to convey as accurately as feasible to the people involved what kind of system they're interacting with.
However, I think the connection that people tend to feel with humanoid robots is more of a feature than a bug. I wouldn't want to see human-robot relationships replace human-human relationships. But that's not the choice we're facing.
McDonald's, for instance, has bought an AI company and is replacing humans with touchpad-based kiosks and automated voice systems, for cost reasons. If people are going to do business with machines, let them be designed to create and maintain meaningful social and emotional connections with people.
As well as making our daily lives richer than they would be in a world dominated by faceless machines, humanoid robots have the potential to pave the way toward a future in which humans and robots and other AIs interact in mutually compassionate and synergetic ways.
As today's narrow AI segues into tomorrow's AGI, how will emerging AGI minds come to absorb human values and culture?
Hard-coded rules regarding moral values can play, at best, a very limited role, e.g., in situations like a military robot deciding who to kill, or a loan-officer AI deciding who to loan funds to. The vast majority of real-life ethical decisions are fuzzy, uncertain and contextual in nature the kind of thing that needs to be learned by generalization from experience and by social participation.
The best way for an AI to absorb human culture is the way kids do, through rich participation in human life. Of course, the architecture of the AI's mind also matters. It has to be able to represent and manipulate thought and behavior patterns as nebulous as human values. But the best cognitive architecture won't help if the AI doesn't have the right experience base.
So my ultimate answer to why should we have humanoid robots is not just because people want them or because they are better for human life and culture than faceless kiosks but because they are the best way I can see to fill the AGI mind of the future with human values and culture.