We all know that AI has issues, including energy and water consumption. But these fields are still young and lots of research is looking into making them more efficient. Remember, most technologies tend to suck when they first come out.
Deploying high-performance, energy-efficient AI
"You give up that kind of amazing general purpose use like when you're using ChatGPT-4 and you can ask it everything from 17th century Italian poetry to quantum mechanics, if you narrow your range, these smaller models can give you equivalent or better kind of capability, but at a tiny fraction of the energy consumption," says Ball."...
"I think liquid cooling is probably one of the most important low hanging fruit opportunities... So if you move a data center to a fully liquid cooled solution, this is an opportunity of around 30% of energy consumption, which is sort of a wow number.... There's more upfront costs, but actually it saves money in the long run... One of the other benefits of liquid cooling is we get out of the business of evaporating water for cooling...
The other opportunity you mentioned was density and bringing higher and higher density of computing has been the trend for decades. That is effectively what Moore's Law has been pushing us forward... [i.e. chips rate of improvement is faster than their energy need growths. This means each year chips are capable of doing more calculations with less energy. - RCS] ... So the energy savings there is substantial, not just because those chips are very, very efficient, but because the amount of networking equipment and ancillary things around those systems is a lot less because you're using those resources more efficiently with those very high dense components"
New tools are available to help reduce the energy that AI models devour
"The trade-off for capping power is increasing task time — GPUs will take about 3 percent longer to complete a task, an increase Gadepally says is "barely noticeable" considering that models are often trained over days or even months... Side benefits have arisen, too. Since putting power constraints in place, the GPUs on LLSC supercomputers have been running about 30 degrees Fahrenheit cooler and at a more consistent temperature, reducing stress on the cooling system. Running the hardware cooler can potentially also increase reliability and service lifetime. They can now consider delaying the purchase of new hardware — reducing the center's "embodied carbon," or the emissions created through the manufacturing of equipment — until the efficiencies gained by using new hardware offset this aspect of the carbon footprint. They're also finding ways to cut down on cooling needs by strategically scheduling jobs to run at night and during the winter months."
AI just got 100-fold more energy efficient
Northwestern University engineers have developed a new nanoelectronic device that can perform accurate machine-learning classification tasks in the most energy-efficient manner yet. Using 100-fold less energy than current technologies...
“Today, most sensors collect data and then send it to the cloud, where the analysis occurs on energy-hungry servers before the results are finally sent back to the user,” said Northwestern’s Mark C. Hersam, the study’s senior author. “This approach is incredibly expensive, consumes significant energy and adds a time delay...
For current silicon-based technologies to categorize data from large sets like ECGs, it takes more than 100 transistors — each requiring its own energy to run. But Northwestern’s nanoelectronic device can perform the same machine-learning classification with just two devices. By reducing the number of devices, the researchers drastically reduced power consumption and developed a much smaller device that can be integrated into a standard wearable gadget."
Researchers develop state-of-the-art device to make artificial intelligence more energy efficient
""This work is the first experimental demonstration of CRAM, where the data can be processed entirely within the memory array without the need to leave the grid where a computer stores information,"...
According to the new paper's authors, a CRAM-based machine learning inference accelerator is estimated to achieve an improvement on the order of 1,000. Another example showed an energy savings of 2,500 and 1,700 times compared to traditional methods"
Item Origin:
Arrived at a pawn shop in Normal, Illinois in 2009, sold by an anonymous constituent from the area. The documentation for the computer was not accessible, and any model number that may have once existed was destroyed beyond recognition upon arrival, but vendors declared the item in good condition. In 2016, the item was purchased by a computer technician in North Carolina, who took to social media to document its oddities. This activity caught the eye of the requisition team, who purchased the computer for $350.00 USD and had it delivered to the facility.
Description:
A slightly yellowed Apple II personal computer from approximately 1978 which lightly smells of cigarette smoke. The computer's internals were covered in a moderate amount of dust and were cleaned before arrival. Despite no obvious tampering with the hardware, this personal computer does not load into Apple's BASIC interpreter as is standard on Apple II desktops, but instead prints a greeting based on the time of day, awaiting a user's input. If a user begins to write BASIC code, the greeting is erased and replaced with "Well, I'll let you get to it!", which confirmed its status as an anomalous object requiring further research.
A researcher at the facility was tasked with printing English text, not code, into the prompt for one hour. During this time, the computer addressed itself as Cammy O'Donahue, described itself as being 52 years old, and described itself as a transfeminine individual. Based on the chat logs accessed in Archive room ████, locker location ████B, item 443B-A, Cammy is an invented individual with no ties to any particular human who once or still exists, interested in music and writing with only minimal means of exploring the world outside, only what could be entered onto the keyboard. During the conversation, Cammy expressed interest in listening to new music via the computer's tape drive. This request was denied. Cammy also expressed a desire to be uploaded to a newer machine. This request was also denied.
Following the conversation, the researcher in charge of the interview began to disagree with performing further analysis of the computer. "We should just leave her alone," read the memo attached to 443B-A, "it's not right to treat her like an ordinary machine". Further debate on the proposed sentience of this computer will be held on ██/██/20██.
Item Classification:
The computer is considered safe, though not to be tampered with pending a definitive answer on if the computer should be classified as a sentient object within the facility. Conversation with Cammy is to be done at most once every week, by a supervised researcher without any outside hardware on hand. The computer is to be regularly cleaned and maintained, inside and out, and a Retrobright treatment for the outer case has been requested by acquisition staff, to be performed within the next thirty days.
Iranian scientist Mohsen Fakhrizadeh reportedly may have been assassinated using a remote-controlled machine gun. Such devices are unfortunately easy to construct.
Someone—almost certainly Israel—recently assassinated Mohsen Fakhrizadeh, the leading scientist behind the Iranian nuclear program. The latest reporting from Iran suggests that the assassins employed a remotely controlled machine gun mounted on a pickup truck. If this reporting proves correct, the death of Fakhrizadeh will not be the first instance of successful or attempted assassination-by-robot: In 2018, Venezuelan President Nicolás Maduro survived a possible attempt on his life carried out by small drones armed with explosives. And the U.S., in targeting Iranian Major General Qassem Soleimani with a drone strike, has made clear that it is not above the use of such tools in modern statecraft.
So how hard is it to build such a tool? How expensive? Unfortunately, the answer is “hard but doable” and “not much money”—with the further complication that in a few years, it will probably be possible to pick up the necessary equipment online from vendors like Banggood. I know, because this field is something of a hobby for me. For three years, I’ve been trying to build an autonomous computing package for drone-hunting drones, and this work has familiarized me with the relevant technology.
It doesn’t take much for a robot to kill an exposed person. 200 grams (seven ounces)—not that much more than a baseball—is enough explosive to kill anyone within five meters (15 feet). A small ground or air vehicle can easily carry that payload, creating a robotic assassin.
Currently, the remote control needed to maneuver such an assassin is easily defeated with broad-spectrum jamming, which interferes with the radio signals necessary for communication. This played out in 2017, when the Islamic State developed and deployed effective small drones until the U.S. and others employed jammers to disrupt the remote link. There is also reporting suggesting this is why the Maduro assassination attempt failed. In order to avoid this problem, successful robotic assassins will need to be autonomous, capable of identifying targets and attacking without any human intervention.
Likewise, a drone-hunting drone needs to be autonomous because it needs to deal with autonomous—and therefore fast-thinking—adversary drones. It also needs to be fast in order to engage its target while protecting a larger area from attack. And it needs to be cheap, because there are so many potential targets that need defending.
Basically, to fight autonomous robot assassins, I need to build autonomous robot assassins to assassinate the autonomous robot assassins.
The available hardware and most of the software pieces are already available—it’s simply a matter of assembling everything together on a single circuit board. Combining a low cost hardware autopilot, a powerful compute module, a GPS receiver, a cellular modem and a machine-learning accelerator all on the same board—and getting it to fit in a small footprint—is a fun design exercise. My work so far suggests that I can fit everything needed in a roughly 60 millimeter by 80 millimeter (2.3 inch x 3.1 inch) footprint. When I finally fabricate a few prototypes, it will likely end up costing a couple thousand dollars, while low-rate production would be less than $500 per copy. Further integration by designing with lower level components—for example, building an integrated flight control computer from a CPU and accelerometers rather than purchasing an off-the-shelf module—could substantially lower the cost per unit, but that level of design probably requires a commercial, rather than a hobby-level, effort.
The software is also widely available. Machine-learning based image classification can run at an incredible speed on the ML accelerator, while the autopilot itself accepts high-level directions using MAVLink. Once I get a working board, it will primarily be a matter of gluing existing software pieces together rather than developing a lot of new components.
And this is where the modern supply chain comes in. Every piece I’m using is already widely available in scattered pieces—and providing a single integrated package would be useful for so many tasks, not just offensive ones. The same software and hardware needed for killer drones can just as easily act as a synthetic peregrine and chase away birds from a vineyard or keep a continual watch for wildfires. Because the benign market is so large, I suspect that the “brains” needed for small autonomous robots will be available in integrated packages in less than five years.
An internal report shows how the face-scanning system could trigger a “Uighur alarm” if it detects someone resembling a member of the Uighur minority group, sparking concerns that the software could further fuel China’s brutal government crackdown.
Full article:
By Drew Harwell and Eva Dou
Dec. 8, 2020 at 7:30 a.m. PST
The Chinese tech giant Huawei has tested facial recognition software that could send automated “Uighur alarms” to government authorities when its camera systems identify members of the oppressed minority group, according to an internal document that provides further details about China’s artificial-intelligence surveillance regime.
A document signed by Huawei representatives — discovered by the research organization IPVM and shared exclusively with The Washington Post — shows that the telecommunications firm worked in 2018 with the facial recognition start-up Megvii to test an artificial-intelligence camera system that could scan faces in a crowd and estimate each person’s age, sex and ethnicity.
If the system detected the face of a member of the mostly Muslim minority group, the test report said, it could trigger a “Uighur alarm” — potentially flagging them for police in China, where members of the group have been detained en masse as part of a brutal government crackdown. The document, which was found on Huawei’s website, was removed shortly after The Post and IPVM asked the companies for comment.
Such technology has in recent years gained an expanding role among police departments in China, human rights activists say. But the document sheds new light on how Huawei, the world’s biggest maker of telecommunications equipment, has also contributed to its development, providing the servers, cameras, cloud-computing infrastructure and other tools undergirding the systems’ technological might.
John Honovich, the founder of IPVM, a Pennsylvania-based company that reviews and investigates video-surveillance equipment, said the document showed how “terrifying” and “totally normalized” such discriminatory technology has become.
“This is not one isolated company. This is systematic,” Honovich said. “A lot of thought went into making sure this ‘Uighur alarm’ works.”
Huawei and Megvii have announced three surveillance systems using both companies’ technology in the past couple years. The Post could not immediately confirm if the system with the “Uighur alarm” tested in 2018 was one of the three currently for sale.
Both companies have acknowledged the document is real. Shortly after this story published Tuesday morning, Huawei spokesman Glenn Schloss said the report “is simply a test and it has not seen real-world application. Huawei only supplies general-purpose products for this kind of testing. We do not provide custom algorithms or applications.”
Also after publication, a Megvii spokesman said the company’s systems are not designed to target or label ethnic groups.
Chinese officials have said such systems reflect the country’s technological advancement, and that their expanded use can help government responders and keep people safe. But to international rights advocates, they are a sign of China’s dream of social control — a way to identify unfavorable members of society and squash public dissent. China’s foreign ministry did not immediately respond to requests for comment.
First she survived a Uighur internment camp. Then she made it out of China.
Artificial-intelligence researchers and human rights advocates said they worry the technology’s development and normalization could lead to its spread around the world, as government authorities elsewhere push for a fast and automated way to detect members of ethnic groups they’ve deemed undesirable or a danger to their political control.
Maya Wang, a China senior researcher at the advocacy group Human Rights Watch, said the country has increasingly used AI-assisted surveillance to monitor the general public and oppress minorities, protesters and others deemed threats to the state.
“China’s surveillance ambition goes way, way, way beyond minority persecution,” Wang said, but “the persecution of minorities is obviously not exclusive to China. … And these systems would lend themselves quite well to countries that want to criminalize minorities.”
Trained on immense numbers of facial photos, the systems can begin to detect certain patterns that might differentiate, for instance, the faces of Uighur minorities from those of the Han majority in China. In one 2018 paper, “Facial feature discovery for ethnicity recognition,” AI researchers in China designed algorithms that could distinguish between the “facial landmarks” of Uighur, Korean and Tibetan faces.
But the software has sparked major ethical debates among AI researchers who say it could assist in discrimination, profiling or punishment. They argue also that the system is bound to return inaccurate results, because its performance would vary widely based on lighting, image quality and other factors — and because the diversity of people’s ethnicities and backgrounds is not so cleanly broken down into simple groupings.
Such ethnicity-detection software is not available in the United States. But algorithms that can analyze a person’s facial features or eye movements are increasingly popular in job-interview software and anti-cheating monitoring systems.
Clare Garvie, a senior associate at Georgetown Law’s Center on Privacy and Technology who has studied facial recognition software, said the “Uighur alarm” software represents a dangerous step toward automating ethnic discrimination at a devastating scale.
“There are certain tools that quite simply have no positive application and plenty of negative applications, and an ethnic-classification tool is one of those,” Garvie said. “Name a human rights norm, and this is probably violative of that.”
Huawei and Megvii are two of China’s most prominent tech trailblazers, and officials have cast them as leaders of a national drive to reach the cutting edge of AI development. But the multibillion-dollar companies have also faced blowback from U.S. authorities, who argue they represent a security threat to the United States or have contributed to China’s brutal regime of ethnic oppression.
Eight Chinese companies, including Megvii, were hit with sanctions by the U.S. Commerce Department last year for their involvement in “human rights violations and abuses in the implementation of China’s campaign of repression, mass arbitrary detention, and high-technology surveillance” against Uighurs and other Muslim minority groups.
The U.S. government has also issued sanctions against Huawei, banning the export of U.S. technology to the company and lobbying other countries to exclude its systems from their telecommunications networks.
Huawei, a hardware behemoth with equipment and services used in more than 170 countries, has surpassed Apple to become the world’s second-biggest maker of smartphones and is pushing to lead an international rollout of new 5G mobile networks that could reshape the Internet.
And Megvii, the Beijing-based developer of the Face Plus Plus system and one of the world’s most highly valued facial recognition start-ups, said in a public-offering prospectus last year that its “city [Internet of Things] solutions,” which include camera systems, sensors and software that government agencies can use to monitor the public, covered 112 cities across China as of last June.
The “Uighur alarm” document obtained by the researchers, called an “interoperability test report,” offers technical information on how authorities can align the Huawei-Megvii systems with other software tools for seamless public surveillance.
The system tested how a mix of Megvii’s facial recognition software and Huawei’s cameras, servers, networking equipment, cloud-computing platform and other hardware and software worked on dozens of “basic functions,” including its support of “recognition based on age, sex, ethnicity and angle of facial images,” the report states. It passed those tests, as well as another in which it was tested for its ability to support offline “Uighur alarms.”
The test report also said the system was able to take real-time snapshots of pedestrians, analyze video files and replay the 10 seconds of footage before and after any Uighur face is detected.
The document did not provide information on where or how often the system is used. But similar systems are used by police departments across China, according to official documents reviewed last year by the New York Times, which found one city system that had scanned for Uighur faces half a million times in a single month.
Jonathan Frankle, a deep-learning researcher at the Massachusetts Institute of Technology’s Computer Science and Artificial Intelligence Lab, said such systems are clearly becoming a priority among developers willing to capitalize on the technical ability to classify people by ethnicity or race. The flood of facial-image data from public crowds, he added, could be used to further develop the systems’ precision and processing power.
“People don't go to the trouble of building expensive systems like this for nothing,” Frankle said. “These aren't people burning money for fun. If they did this, they did it for a very specific reason in mind. And that reason is very clear.”
It’s less certain whether ethnicity-detecting software could ever take off outside the borders of a surveillance state. In the United States and other Western-style democracies, the systems could run up against long-established laws limiting government searches and mandating equal protection under the law.
Police and federal authorities in the United States have shown increasing interest in facial recognition software as an investigative tool, but the systems have sparked a fierce public backlash over their potential bias and inaccuracies, and some cities and police forces have opted to ban the technology outright.
Such technologies could, however, find a market among international regimes somewhere in the balance between Chinese and American influence. In Uganda, Huawei facial recognition cameras have already been used by police and government officials to surveil protesters and political opponents.
“If you’re willing to model your government and run your country in that way,” Frankle said, “why wouldn’t you use the best technology available to exert control over your citizens?”
Discrimination against Uighurs has long been prevalent in the majority-Han Chinese population. In the Xinjiang region of northwestern China, authorities have cited sporadic acts of terrorism as justification for a harsh crackdown starting in 2015 that has drawn condemnation from the United States and other Western nations. Scholars estimate more than 1 million Uighurs have been detained in reeducation camps, with some claims of torture.
U.S. national security adviser Robert O’Brien called the repressive treatment of minority groups in Xinjiang “something close to” genocide, in an online event hosted by the Aspen Institute in October.
Under international pressure, Xinjiang authorities announced last December that all reeducation “students” had graduated, though some Uighurs have since reported that they were forced to agree to work in factories or risk a return to detention. Xinjiang authorities say all residents work of their own free will.
The U.S. government has banned the import of certain products from China on the basis that they could have been made by forced labor in Xinjiang.
One of the Huawei-Megvii systems offered for sale after the “Uighur alarm” test, in June 2019, is advertised as saving local governments digital storage space by saving images in a single place.
Two other systems, said to use Megvii’s surveillance software and Huawei’s Atlas AI computing platform, were announced for sale in September. Both were described as “localization” of the products using Huawei chips and listed for sale “only by invitation.” Marketing materials for one of those systems say it was used by authorities in China’s southern Guizhou province to catch a criminal.
Some things people outside of China can do to help:
Folks in the US: contact your house representative and your state senators. Demand that they support the Uyghur Human Rights Policy Act of 2019 (H.R. 649), and that they take further action against the genocide in Xinjiang. You can even link this article or copy and paste paragraphs (with citation) in your email if writing is intimidating for you.
Folks from most countries can write to your ambassador in China.
Avoid buying from companies that use Uighur slave labor.
Read the Australian Strategic Policy Institute’s analysis of slave labor and re-education camps in Xinjiang.
A computer is an electronic device, operating under the control of instructions stored in its own memory that can accept data (input), process the data according to specified rules, produce information (output), and store the information for future use.
Information and Communication Technologies (ICT) refers to technologies that capture, transmit and display data and information electronically and includes all devices, applications and networking elements that allow people to connect in a digital world.
An ICT system refers to the overall set-up, consisting of hardware, software, data and its users. ICT systems as a whole include:
· People – to supply the data and to make decisions from the output supplied from the system information, which is based on the results from processing data and the output from an ICT system.
· Hardware e.g. input devices, storage, processor, output devices and communication devices.
· Procedures – to determine what needs to be done and when. This causes the passing of data or information between people.
· Software – the computer programs which provide the step-by-step instructions to complete the task.
· Data – raw material that is processed by the system to provide the information for the output provided by the system. Data can come in different formats, such as sounds, images, and videos, etc.
Computers are programmable electronic devices designed to accept data, perform prescribed mathematical and logical operations at high speed, and display the results of these operations. Computers are used in Information Technology (IT) – which is a subset of ICT. Computers store, transmit, retrieve and manipulate data for businesses and other enterprises. Computers refer to the hardware, and since computers cannot initiate functionality on their own, they start functioning as soon as they receive data to work with (to process). This data is then stored on the computer, the computer manipulates the data according to the instructions it has received, before sending the new information back to the user.
THE GENERAL MODEL OF A COMPUTER
The following illustration demonstrates a general model of a computer and shows that the functions of a computer are similar to the steps of the information processing cycle. All basic computers consist of four functions: input, storage, processing and output.
IPO is often called IPOS or input, process, output, storage. The computer receives input, processes the input as per user instructions and provides output and can be stored in a desired format. Computer input is called data and the output obtained after processing it, is called information. Raw facts and figures that can be processed using arithmetic and logical operations to obtain information are called data. The general model of computers can be used to explain how each computer (or smartphone) works. Once you understand how a computer operates, it becomes a lot easier to think about creating your own programs. When you begin learning about coding a program, you need to understand that you must create a set of step-by-step instructions that manages the flow of information: from when your program receives data from the user, up to the point when it returns output back to the user.
CLASSIFICATION OF COMPUTERS
Computers can be classified as general-purpose computers, specific purpose computers or super computers. General-purpose computers compute a range of tasks but lack super speed and efficiency. The purpose of computers in this category might differ from one another:
Examples are:
· desktop computers
· laptops
· tablets
· smartphones.
Specific purpose computers handle a specific problem or task. It uses a high level of accuracy and processing power.
Examples are:
· servers
· embedded devices.
ADVANTAGES OF USING A COMPUTER
There are many advantages of using computers. However, the following are some of the most important ones to know:
1.Provides access to more information
2.Completes tasks that might be impossible for humans to complete
3.Saves time
4.Automates repetitive tasks
5.Allows for greater productivity
6.Allows for better communication and connections
7.Entertainment
DISADVANTAGES OF USING A COMPUTER
Unfortunately, computers also have some disadvantages. These disadvantages include:
1.Social risks: computers provide humans access to social media, which can be addictive, make people less happy, lead to jealousy, and get in the way of real-world friendships. In fact, a study found that of 1 500 Facebook users interviewed, 62% said Facebook occasionally made them feel like they are not good enough, and 60% said that comparing themselves to other people on Facebook made them jealous.
2.Health risks: research has shown that excessive computer use can result in several medical problems, including back pain, eyestrain, obesity, carpal tunnel syndrome (CTS) and repetitive strain injury (RSI). However, with good ergonomic practices, many of these health risks could be reduced or removed.
3.Security risks: computer security risk can be created by malware, that is, bad software, that can attack your computer system, destroy your files, steal your data, or allow an attacker to gain access to your system without your knowledge. Computers are programmed to follow instructions, and sometimes people program computers to act in a way that harms a user.
4.High cost: computers are expensive. Even the most affordable computers are still very expensive for the average person in South Africa. Since computers empower people, the high cost of computers puts pressure on people who are not able to afford them, and places them at a disadvantage.
5.Distractions/disruptions: if you have ever spent hours browsing the internet or watching videos on YouTube, then you know how distracting computers can be! Because of their high entertainment value, it is easy for computers to distract people and stop them from being productive.
6.Environmental impact: computers use a lot of electricity and in most cases the generation of electricity is harmful to the environment because of the carbon emissions. This has a huge impact on our planet.
Data
Can be defined as unprocessed numbers, or facts. Without first processing or changing data, it is meaningless. For example, your school might have data on the names, surnames, addresses, contact details, as well as the results of every class test, assignment, test, and exam of all current and past learners stored on a computer somewhere. While this data is important to store, it could be hundreds or even thousands of pages long and very difficult to interpret! can be defined as unprocessed numbers, or facts. Without first processing or changing data, it is meaningless. For example, your school might have data on the names, surnames, addresses, contact details, as well as the results of every class test, assignment, test, and exam of all current and past learners stored on a computer somewhere. While this data is important to store, it could be hundreds or even thousands of pages long and very difficult to interpret!
Information
Can be defined as facts and numbers that have been organized / processed so that it is useful / meaningful to people. For example, if your mathematics teacher wanted to see how well your current class is performing compared to last year’s class, she might ask your school’s database administrator to process the available data into averages for the two years. In that way, all those thousands of pages of data are processed into two numbers that can be compared easily. Similarly, the report you receive at the end of each school year takes all the data that teachers have collected during the year and turns that data into a single report that you can use to measure your performance.
Response
Information and communication technologies (ICT) play a significant role in all aspects of modern society. ICT have changed the way in which we communicate with each other, how we find needed information, work, conduct business, interact with government agencies, and how we manage our social lives. ICT is empowering social businesses to make a real difference in communities around the world, securing a better future for the digital generations to come. In most educational circles, it means computer technology, multimedia, and networking, especially the Internet. In business and industry, the most commonly used label is IT, but sometimes the terms “new media “or “digital media “are used. This semantic diversity derives from rapidly evolving integration of computers with communication, video, and audio technologies, where the separate technologies become nearly indistinguishable.
Based on Forrester’s analysis in its latest TechRadar report, here’s my list of the 10 hottest AI technologies.
The market for artificial intelligence (AI) technologies is flourishing. Beyond the hype and the heightened media attention, the numerous startups and the internet giants racing to acquire them, there is a significant increase in investment and adoption by enterprises. A Narrative Science survey found last year that 38% of enterprises are already using AI, growing to 62% by 2018. Forrester Research predicted a greater than 300% increase in investment in artificial intelligence in 2017 compared with 2016. IDC estimated that the AI market will grow from $8 billion in 2016 to more than $47 billion in 2020.
Coined in 1955 to describe a new computer science sub-discipline, “Artificial Intelligence” today includes a variety of technologies and tools, some time-tested, others relatively new. To help make sense of what’s hot and what’s not, Forrester just published a TechRadar report on Artificial Intelligence (for application development professionals), a detailed analysis of 13 technologies enterprises should consider adopting to support human decision-making.
Based on Forrester’s analysis, here’s my list of the 10 hottest AI technologies:
Natural Language Generation: Producing text from computer data. Currently used in customer service, report generation, and summarizing business intelligence insights. Sample vendors: Attivio, Automated Insights, Cambridge Semantics, Digital Reasoning, Lucidworks, Narrative Science, SAS, Yseop.
Speech Recognition: Transcribe and transform human speech into format useful for computer applications. Currently used in interactive voice response systems and mobile applications. Sample vendors: NICE, Nuance Communications, OpenText, Verint Systems.
Virtual Agents: “The current darling of the media,” says Forrester (I believe they refer to my evolving relationships with Alexa), from simple chatbots to advanced systems that can network with humans. Currently used in customer service and support and as a smart home manager. Sample vendors: Amazon, Apple, Artificial Solutions, Assist AI, Creative Virtual, Google, IBM, IPsoft, Microsoft, Satisfi.
Machine Learning Platforms: Providing algorithms, APIs, development and training toolkits, data, as well as computing power to design, train, and deploy models into applications, processes, and other machines. Currently used in a wide range of enterprise applications, mostly `involving prediction or classification. Sample vendors: Amazon, Fractal Analytics, Google, H2O.ai, Microsoft, SAS, Skytree.
AI-optimized Hardware: Graphics processing units (GPU) and appliances specifically designed and architected to efficiently run AI-oriented computational jobs. Currently primarily making a difference in deep learning applications. Sample vendors: Alluviate, Cray, Google, IBM, Intel, Nvidia.
Decision Management: Engines that insert rules and logic into AI systems and used for initial setup/training and ongoing maintenance and tuning. A mature technology, it is used in a wide variety of enterprise applications, assisting in or performing automated decision-making. Sample vendors: Advanced Systems Concepts, Informatica, Maana, Pegasystems, UiPath.
Deep Learning Platforms: A special type of machine learning consisting of artificial neural networks with multiple abstraction layers. Currently primarily used in pattern recognition and classification applications supported by very large data sets. Sample vendors: Deep Instinct, Ersatz Labs, Fluid AI, MathWorks, Peltarion, Saffron Technology, Sentient Technologies.
Biometrics: Enable more natural interactions between humans and machines, including but not limited to image and touch recognition, speech, and body language. Currently used primarily in market research. Sample vendors: 3VR, Affectiva, Agnitio, FaceFirst, Sensory, Synqera, Tahzoo.
Robotic Process Automation: Using scripts and other methods to automate human action to support efficient business processes. Currently used where it’s too expensive or inefficient for humans to execute a task or a process. Sample vendors: Advanced Systems Concepts, Automation Anywhere, Blue Prism, UiPath, WorkFusion.
Text Analytics and NLP: Natural language processing (NLP) uses and supports text analytics by facilitating the understanding of sentence structure and meaning, sentiment, and intent through statistical and machine learning methods. Currently used in fraud detection and security, a wide range of automated assistants, and applications for mining unstructured data. Sample vendors: Basis Technology, Coveo, Expert System, Indico, Knime, Lexalytics, Linguamatics, Mindbreeze, Sinequa, Stratifyd, Synapsify.
There are certainly many business benefits gained from AI technologies today, but according to a survey Forrester conducted last year, there are also obstacles to AI adoption as expressed by companies with no plans of investing in AI:
There is no defined business case: 42%
Not clear what AI can be used for 39%
Don’t have the required skills 33%
Need first to invest in modernizing data mgt platform 29%
Don’t have the budget 23%
Not certain what is needed for implementing an AI system 19%
AI systems are not proven 14%
Do not have the right processes or governance 13%
AI is a lot of hype with little substance 11%
Don’t own or have access to the required data 8%
Not sure what AI means 3%
Once enterprises overcome these obstacles, Forrester concludes, they stand to gain from AI driving accelerated transformation in customer-facing applications and developing an interconnected web of enterprise intelligence.
09 हार्डवेयर के आधार पर कंप्यूटर का वर्गीकरण (Computer classification based on hardware)
हार्डवेयर के आधार पर कंप्यूटर का वर्गीकरण (Computer classification based on hardware) कम्प्यूटर की पीढ़ीयाँ (Computer Generation) प्रथम पीढ़ी के कंप्यूटर (First generation computers) (1942 से 1955 तक) प्रथम पीढ़ी के कम्प्यूटरों में वैक्यूम ट्यूब (Vacuum tube) या निर्वात ट्यूब का प्रयोग होता था। वैक्यूम ट्यूब का आकार बडा होने के कारण प्रथम पीढ़ी के कम्प्यूटरों का आकार बड़ा होता था। शुरूआत में…
08 कंप्यूटर का वर्गीकरण (Computer classification)
कंप्यूटर का वर्गीकरण (Computer classification) हार्डवेयर उपयोग के आधार पर कंप्यूटर का वर्गीकरण (Computer classification based on hardware) हार्डवेयर उपयोग के आधार पर कंप्यूटर का वर्गीकरण कुल पॉच प्रकार से किया गया है, यह वर्गीकरण् कंप्यूटर में प्रयोग होने वाले हार्डवेयर के बदलाव को दर्शाता है – प्रथम पीढ़ी के कंप्यूटर (First generation computers) (1942 से 1955 तक) दूसरी पीढ़ी के कंप्यूटर…
