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A groundbreaking artificial intelligence (AI) project seeks to have sensors accepting and transmitting information in a method similar to the human brain. Being developed by the Defense Advanced Research Projects Agency (DARPA) along with SRI International, the Systems of Neuromorphic Adaptive Plastic Scalable Electronics, or SyNAPSE, project is surpassing traditional mathematical algorithms and attempting to process information in complex environments autonomously by learning relevant and probabilistically stable features and associations automatically.

Today, the military primarily uses AI to process large amounts of information, including videos, signals and intelligence that must be deciphered and analyzed quickly. As developments in AI continue, these tasks may seem almost menial. "A robot would be able to recognize, from the activities its video cameras capture, what the people it's observing are doing," explains Dr. Raymond Perrault, director, , SRI. "It can tell you that someone is walking through the door, or that two people have met and exchanged a package or that a person is digging a hole by the side of the road." A robot like this with sensors could decide what information is pertinent and report the data to a group of warfighters.

These intelligence systems can perceive their environment and adjust. "They manage to do [their mission] while the world changes around them," Perrault says. To accomplish this task, they organize ideas utilizing mathematical logic. Using sensory data, the programs prove simple theorems by plugging the data into the algorithms, which results in a solution and consequent action.

An alternative U.S. Defense Department use for AI involves virtual personal assistants that respond to voice commands and complete complex tasks on the user's behalf. These AI programs analyze enormous amounts of data and make decisions. They interact and learn but don't necessarily need to perceive the environment. "You communicate what you would like to have done, and they have a dialogue with you to get the job done," he explains.

DARPA and SRI completed this kind of application in the 2009 Personalized Assistant that Learns (PAL) program; SRI calls the application the Cognitive Assistant that Learns and Organizes (CALO). The software program can reason, learn, understand instructions, explain what it is doing and react. It responds to unknown situations and can debrief after an operation is complete.

CALO takes information from a user's e-mails, contacts, tasks and projects. It then creates a relational model of the user's world and learns from it. From there, it can manage scheduling constraints, handle conflicts and negotiate with other parties on the user's behalf.

Recent AI technology currently does not come in the form of state-of-the-art robot companions. Most of the technology is still in standard computer hardware. What is changing, however, is the way this technology helps people: through programs that learn and hardware that will eventually imitate the human brain, Perrault notes.

Because of the variety in AI, many unforeseen uses could be right around the corner. Perrault observes that the practical usage of interactive computers has imitated human nature so well that the incorporation of AI, though impressive, won't look like a sci-fi movie. It will just naturally integrate into everyday life.

A supercomputer that runs operations like the HAL 9000 from 2001: A Space Odyssey does not yet exist, Perrault confirms. " The things that are being done are progress and are useful, but they will not allow machines to take over the world. When it comes to real tasks in the real world like dealing with perception and language, we still have a long way to go," he emphasizes.

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Artificial Intelligence Advances in Military Ranks

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A groundbreaking artificial intelligence (AI) project seeks to have sensors accepting and transmitting information in a method similar to the human brain. Being developed by the Defense Advanced Research Projects Agency (DARPA) along with SRI International, the Systems of Neuromorphic Adaptive Plastic Scalable Electronics, or SyNAPSE, project is surpassing traditional mathematical algorithms and attempting to process information in complex environments autonomously by learning relevant and probabilistically stable features and associations automatically.

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Artificial intelligence applied in farming

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Artificial intelligence applied in farming

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NIIAS成员听到人工智能的应用，农业以及它在最近的一次会议养殖增加生存能力。

经济学将要求从人工智能到农业的实际应用的理论概念的飞跃，根据杰伊·约翰斯顿，里奇的首席执行官饲料和种子公司

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我们很容易在你的车辱骂当它事惹恼你，就像当你迟到了一个会议击穿，但有一天你的车可能有智能响应！这是不是从电影中的一幕“金龟去香蕉”这是个人交通的未来。

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Around 200 years ago the industrial revolution immutably remoulded society. Today another revolution is underway with potentially even further reaching consequences.

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Hiro Ono's OdysseyDream, work, and life of a NASA engineer

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Hiro Ono's OdysseyDream, work, and life of a NASA engineer

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There is a surge of interest in artificial intelligence (AI), which is my area of research, thanks to the recent victory of the Alpha GO algorithm over a human champion.

Arguably the most famous AI in fictional universes, at least for space geeks like myself or movie lovers, would be HAL 9000 in 2001 Space Odyssey. The human-comparable machine intelligence was on-board Discovery on a Jupiter mission as the sixth crew member. It was a very memorable scene when HAL 9000 sang Daisy Bell as it was deactivated by David Bowman.

In the real universe, AI has already played important roles in space exploration. We are still far behind HAL 9000. However, I am confident that it will play increasingly greater roles in future exploration missions. In this article I will overview the current status of AI in space exploration, as well as expectations for the future.

The Two-decade Long History of AI in Space

In the last few years, we observed the emergence and rapid growth of the autonomous driving technologies here on Earth.

On Mars, in fact, autonomous driving is a decade-old technology. An autonomous navigation and driving system called AutoNav has been behind the wheel of Spirit and Opportunity rovers, which landed on Mars in 2004. AutoNav is also on the latest rover, Curiosity.

Driving on Mars is both easier and more difficult than on Earth. There is no rules of the road on Mars nor cops who would give you tickets. There are no pedestrians or traffics - very unfortunately, the Martian civilization, which we once firmly believed to exist since Giovanni Schiaparelli found "canals" on the red planet, turned out to be an illusion. This in turn means there are no paved roads on Mars, so a rover has to avoid rocks, dents, and dangerous sand dunes which would trap the rover.

An AI "scientist" is also onboard the rovers. An algorithm called AEGIS is capable of autonomously identifying interesting rocks and terrain features. Why this is important? It is because the rover cannot send all the photos it takes during the drives back to the Earth, due to the limited communication capacity. By intelligently choosing the photo targets using AEGIS, we can avoid missing interesting things without significantly increasing the data volume.

The history of AI in space exploration dates back to 1998. On board a comet probe Deep Space 1 was an AI algorithm called Remote Agent, which was co-created by my PhD advisor Brian Williams when he was at NASA Ames. Remote Agent demonstrated the capabilities of planning activities and diagnosing failures on-board. The offspring of Remote Agent are still used by many space missions, though mostly for ground operations instead of being on-board.

Challenges in AI in Space

You might think AI is already so advanced to the point of beating a human champion in Go that it will soon replace human astronauts and ground operators.

Fortunately or unfortunately, it is not that easy. There are two reasons.

First, the computers on space probes are very slow. For example, Curiosity's brain is a CPU called RAD750, which is a variant of PowerPC 750, the CPU used for Macintosh computers in late 1990s. It is clocked at 200 MHz with just 256 MB of memory (RAM). Modern personal computers typically have a CPU clocked at 2-3 GHz and 4-16 GB of RAM. Yes, giga Hz and giga bytes, not mega. There is an order of magnitude difference between them. Furthermore, modern CPUs usually have multiple cores. Though it is not straightforward to quantitatively compare computation power, I think there is more than an order of magnitude difference.

Why do space probes use such medieval computers? A main reason is radiation. If you bring your PC or Mac to space, radiation would soon screw it up. The development of a radiation-tolerant CPU needs some time and cost. Also, it has to go through extensive tests to validate that it has a required level of radiation tolerance. For these reasons, computers used in space are typically a few generations behind the ones in your laptop.

The second reason is reliability. Of course, we are not worrying (at least for now) that AI rebels against humans as HAL 9000 did. However, AI can make mistakes. And we definitely do not want AI to crash a billion-dollar Mars rover just because it fails to identify a pitfall, even though the chance is very low. Therefore, even if AI is available, human operators prefer to fly spacecraft manually as much as possible and check everything by themselves. Spacecrafts are operated quite conservatively.

For the same reason, any autonomy algorithms on spacecraft are designed very conservatively. When it detects any slightest anomaly, it stops a rover or bring a spacecraft into the safe mode immediately and wait for human's instructions. In fact, the typical distance that AutoNav drives on Mars is merely ~10-20 meters per Sol, or a Martial day (which is about 24 hours 40 minutes.) Still, it means a lot because manual driving cannot go beyond the line-of-sight distance, which is ~100 meters at most. But the point here is that spacecraft is operated very carefully in order not to lose them, and any AI algorithms that go behind the wheel of spacecraft must meet a very high standard of reliability.

Future Roles of AI in Space Exploration

Despite the challenges, I believe AI will, or has to, play vital roles in future space exploration.

In the next Mars rover missions, the rovers will be required to drive a significantly increased distance on average. As I said, the manual driving is limited by the line-of-site distance, which means that the autonomous driving distance must be significantly extended in order to achieve the goal.

One of my ongoing research is to increase the reliability of AutoNav. The goal of the research is to combine a machine learning-based terrain classifier and an advanced path planning algorithm, which would allow rovers to drive on complex terrain with reduced conservatism but without compromising in reliability.

Mars is by no means the final destination. To explore more challenging destinations will require even more advanced AI. For example, there is a concept of exploring the subsurface ocean of a Jovian moon, Europa, which is hiding beneath a thick (~10 km) ice crust. Why diving into a freezing water? Because scientists speculate that it may harbor life. (Those who have read 2010: Odyssey Two must be grinning!)

Unlike Mars surface, orbital reconnaissance of the subsurface world is almost impossible (I said almost because the Europa Clipper spacecraft will be able to "see" beneath the ice using the ice penetrating radar, although not to the detail as the surface of Mars). Communication to Earth is even more limited. The probe might be trapped in ice if it waits for the instruction of human operators. Without an advanced autonomy, exploration of such a remote world would be severely limited, if not impossible.

I am also involved in a research of an advanced autonomy, dubbed as Resilient Spacecraft Executive (RSE). Remind the final scene of Terminator, where T-800 kept going after Sarah Connor even after its limbs were crippled. Well, please relax, we are not building a machine intelligence that would terminate humans, of course. We are creating a machine intelligence that can make flexible decisions in an uncertain situations, and continue the mission even under partial hardware failures by rapidly adapting to the changes.

RSE is still in an early stage of development. It would take a long time to fly. As I said, due to the enormous cost of failure, space exploration is very risk averse and hence very slow to adopt new technologies.

However, everyone would agree that technical innovations are necessary to boldly go where no man has gone before. We want to go to the subsurface ocean of Europa. We want to explore the cave systems of Moon and Mars. We want to sail the methane ocean of Titan. And someday we want to discover extraterrestrial life, which would completely change the view of not only the universe but also our own ecosystem. And I believe AI is a key enabler.

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Will AI Revolutionize Space Exploration?

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There is a surge of interest in artificial intelligence (AI), which is my area of research, thanks to the recent victory of the Alpha GO algorithm over a human champion. Arguably the most famous AI in fictional universes, at least for space geeks like myself or movie lovers, would be HAL 9000 in 2001 Space...

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Will AI Revolutionize Space Exploration?在过去的几年中，我们观察到的出现和在地球上的自主驾驶技术的快速增长。

在火星上，事实上，自主驾驶是一个十年之久的技术自主导航和驾驶系统，称为AutoNav的英文一直方向盘后面的。精神状语从句：机遇流动站，其登陆火星于2004年年年年年年年年年年AutoNav也是最新的流动站，好奇。

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Long Island dermatologist Kavita Mariwalla knows how to treat acne, burns, and rashes. But when a patient came in with a potentially disfiguring case of bullous pemphigoid-a rare skin condition that causes large, watery blisters-she was stumped. The medication doctors usually prescribe for the autoimmune disorder wasn't available. So she logged in to Modernizing Medicine, a web-based repository of medical information and insights.

Within seconds, she had the name of another drug that had worked in comparable cases. "It gives you access to data, and data is king," Mariwalla says of Modernizing Medicine. "It's been very helpful, especially in clinically challenging situations."

The system, one of a growing number of similar tools around the country, lets Mariwalla tap the collective knowledge gathered from roughly 3,700 providers and more than 14 million patient visits, as well as data on treatments other doctors have provided to patients with similar profiles. Using the same kind of artificial intelligence that underpins some of the web's largest sites, it instantly mines this data and spits out recommendations. It's a bit like Amazon.com recommending purchases based on its massive trove of data about what people have bought in the past.

>Using the same kind of artificial intelligence that underpins some of the web's largest sites, it instantly mines this data and spits out recommendations.

Tech titans like Google, Amazon, Microsoft, and Apple already have made huge investments in artificial intelligence to deliver tailored search results and build virtual personal assistants. Now, that approach is starting to trickle down into health care, thanks in part to the push under the health reform law to leverage new technologies to improve outcomes and reduce costs-and to the availability of cheaper and more powerful computers. In an effort to better treat their patients, doctors are now exploring the use of everything from IBM's Watson supercomputer, the machine that won at Jeopardy, to iPhone-like pop-up notifications that appear in your online medical records.

Artificial intelligence is still in the very early stages of development-in so many ways, it can't match our own intelligence-and computers certainly can't replace doctors at the bedside. But today's machines are capable of crunching vast amounts of data and identifying patterns that humans can't. Artificial intelligence-essentially the complex algorithms that analyze this data-can be a tool to take full advantage of electronic medical records, transforming them from mere e-filing cabinets into full-fledged doctors' aides that can deliver clinically relevant, high-quality data in real time. "Electronic health records [are] like large quarries where there's lots of gold, and we're just beginning to mine them," said Dr. Eric Horvitz, who is the managing director of Microsoft Research and specializes in applying artificial intelligence in health care settings.

Increasingly, physician practices and hospitals around the country are using supercomputers and homegrown systems to identify patients who might be at risk for kidney failure, cardiac disease, or postoperative infections, and to prevent hospital re-admissions, another key focus of health reform. And they're starting to combine patients' individual health data-including genetic information-with the wealth of material available in public databases, textbooks, and journals to help come up with more personalized treatments.

For now, the recommendations from Modernizing Medicine are largely based on what is most popular among fellow professionals-say, how often doctors on the platform prescribe a given drug or order a particular lab test. But this month, the system will display data on patient outcomes that the company has collected from its subscribers over the past year. Doctors will also be able to double-check the information against the latest clinical research by querying Watson, IBM's artificially intelligent supercomputer. "What happens in the real world should be informed by what's happening in the medical journals," said Daniel Cane, CEO of Florida-based Modernizing Medicine. "That information needs to get to the provider at the point of care."

'Quick and Seamless'

Using homegrown systems, doctors at Vanderbilt University Medical Center in Nashville and St. Jude's Medical Center in Memphis are getting pop-up notifications within individual patients' electronic medical records. The alerts tell them, for instance, when a drug might not work for a patient with certain genetic traits. It shows up in bright yellow at the top of a doctor's computer screen-hard to miss. "With a single click, the doctor can prescribe another medication. It's a very quick and seamless process," says Vanderbilt's Dr. Joshua Denny, one of the researchers who developed the system there.

>'Computers are notoriously bad at understanding English. It's a slow haul, but I'm still optimistic.'

Denny and others used e-medical records on 16,000 patients to help computers predict which patients were likely to need certain medications in the future. Take the anti-blood clot medication Plavix. Some people can't break it down. The Vanderbilt system warns doctors to give patients likely to need the medication a genetic test to see whether they can. If not, it gives physicians suggestions on alternative drugs.

Doctors heed the computer's advice about two-thirds of the time, figuring in, for example, the risks associated with the alternative medication. "The algorithm is pretty good," says Denny, referring to its ability to predict who's going to need a certain drug. "It was smarter than my intuition."

So far, computers have gotten really good at parsing so-called structured data-information that can easily fit in buckets, or categories. In health care, this data is often stored as billing codes or lab test values. But this data doesn't capture patients' full-range of symptoms or even their treatments. Images, radiology reports, and the notes doctors write about each patient can be more useful. That's unstructured data, and computers are less savvy at handling it because it requires making inferences and a certain understanding of context and intent.

That's the stuff humans are really good at doing-and it's what scientists are trying to teach machines to do better. "Computers are notoriously bad at understanding English," said Peter Szolovits, the director of MIT's Clinical Decision Making Group. "It's a slow haul, but I'm still optimistic."

The Challenge Ahead

Computers are getting better at reading unstructured information. Suppose a patient says he doesn't smoke. His doctor checks 'no' in a box-structured data, easily captured by a machine. But then the doctor notes that the patient's teeth are discolored or that there are nicotine stains on his fingers-a clue that the patient in fact does smoke. Soon a computer may be able to highlight such discrepancies, bringing to the fore information that otherwise might have been overlooked.

In recent years, universities, tech companies, and venture capital firms have invested millions into making computers better at analyzing images and words. Companies are popping up to capitalize on findings in studies suggesting that artificial intelligence can be used to improve care. "Artificial intelligence-ultimately that's where the biggest quality improvements will be made," says Euan Thomson, a partner at venture capital firm Khosla Ventures.

>The data is often stored in servers at individual clinics or hospitals, making it difficult to build a comprehensive reservoir of medical information.

But many challenges remain, experts say. Among them is the tremendous expense and difficulty of gaining access to high-quality data and of developing smart models and training them to pick up patterns. Most electronic medical record-keeping systems aren't compatible with each other. The data is often stored in servers at individual clinics or hospitals, making it difficult to build a comprehensive reservoir of medical information.

Moreover, the systems often aren't hooked up to the internet and therefore can't be widely distributed or accessed like other information in the cloud. So, unlike the vast amount of data on Google and Facebook, the information can't be mined from anywhere by those interested in analyzing it. From the perspective of privacy advocates, this makes some good sense: A researcher's treasure trove is a hacker's playground. "It's not the greatest time to talk about" health records on the web, given security scandals such as the Edward Snowden leaks and the Heartbleed bug, says Dr. Russ Altman, the director of Stanford University's biomedical informatics training program.

Drawing the Line

Also standing in the way are concerns about how far computers should encroach on doctors' turf. As artificial intelligence systems get smarter, experts say, the line between making recommendations and making decisions could become more murky. That could cause regulators to view the systems as a medical devices, subject to the review of the U.S. Food and Drug Administration.

Wary of the time and expense required for FDA approval, companies engineering the systems-at least for now-are careful not to describe them as diagnostic tools but rather as information banks. "The FDA would be down on them like a ton of bricks because then they would be claiming to practice medicine," says MIT's Szolovits.

At the moment, he said, the technology isn't good enough to tell doctors with 100 percent certainty what the best course of treatment for a patient may be. Others agree. "It's going to be a long road," says Michael Matheny, a biostatistician at the Vanderbilt School of Medicine.

Back at her clinic in Long Island, Dr. Mariwalla is thankful for the information that the artificial intelligence system can provide. For the patient with that blistering skin condition, she took the machine's suggestion for an alternative medication. The patient has recovered, Mariwalla says, but she's careful to add that she made the call herself-based in part on her conversation with her patient. "That's where medical judgment comes in," she says. "You can't [just] rely on a system to tell you what to do."
Kaiser Health News is an editorially independent program of the Henry J. Kaiser Family Foundation, a nonprofit, nonpartisan health policy research and communication organization not affiliated with Kaiser Permanente.

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Artificial Intelligence Is Now Telling Doctors How to Treat You

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Artificial intelligence is still in the very early stages of development-in so many ways, it can't match our own intelligence-and computers certainly can't replace doctors at the bedside. But today's machines are capable of crunching vast amounts of data and identifying patterns that humans can't.

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人工智能是现在告诉医生如何对待你
LONG ISLAND皮肤科医生卡维塔Mariwalla知道如何治疗痤疮，烧伤和皮疹。但是，当一个病人进来时的大疱性类天疱疮，一种罕见的皮肤状况潜在的毁容案件，导致大，水汪汪的大水泡，她被难住了。用药医生通常规定为自身免疫性疾病不可用。于是，她登录到现代化医药，医疗信息和见解一个基于Web的存储库。

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术语“人工智能”的定义是将先进的硬件和软件，精心数据库和知识为基础的处理模型来证明有效的人类决策的特色系统对于人工系统的标准包括如下：1）正常：该系统必须能够执行对于其已被设计的功能; 2）能够制造：该系统必须是能够通过现有的制造工艺所制造; 3）可设计：系统的设计必须是在他们的文化背景下工作的设计师想象; 4）销售：比较时系统必须被视为服务于某种目的不够好，要竞争的方法，以保证它的设计和制造。

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When decisions have to be made quickly---sometimes instantly---taking into account an enormous amount of information, and when lives are at stake, artificial intelligence can provide crucial assistance. From developing intricate flight plans to implementing complex supply systems or creating training simulation exercises, AI is a natural partner in the modern military.

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What are some applications of AI in the field of agriculture?

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Answer (1 of 12): There are incredible opportunities for AI in agriculture. The problems are that most folks don't understand how to deploy AI in meaningful or cost-effective ways in agriculture. I've found that there is a vast knowledge void between farmers and technologists that means that te...

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Artificial intelligence (AI) has been used in applications throughout industry and academia. Similar to electricity or computers, AI serves as a general-purpose technology that has numerous applications. Its applications span language translation, image recognition, decision-making, credit scoring, e-commerce and various other domains.

A recommendation system predicts the rating or preference a user would give to an item. Artificial intelligence recommendation systems are designed to offer suggestions based on previous behavior. These systems have been used by companies such as Netflix, Amazon, and YouTube, where they generate personalized playlists, product suggestions, and video recommendations.

Machine learning is also used in web feeds such as for determining which posts should show up in social media feeds. Various types of social media analysis also make use of machine learning and there is research into its use for (semi-)automated tagging/enhancement/correction of online misinformation and related filter bubbles.

AI is used to target web advertisements to those most likely to click or engage in them. It is also used to increase time spent on a website by selecting attractive content for the viewer. It can predict or generalize the behavior of customers from their digital footprints. Both AdSense and Facebook use AI for advertising.

Online gambling companies use AI to improve customer targeting.

Search engines that use artificial intelligence include Google Search and Bing Chat.

AI has been used to automatically translate spoken language and textual content, in products such as Microsoft Translator, Google Translate and DeepL Translator. Additionally, research and development are in progress to decode and conduct animal communication.

Image labeling has been used by Google to detect products in photos and to allow people to search based on a photo. Image labeling has also been demonstrated to generate speech to describe images to blind people. Facebook's DeepFace identifies human faces in digital images.

In agriculture, AI has helped farmers identify areas that need irrigation, fertilization, pesticide treatments or increasing yield. Agronomists use AI to conduct research and development. AI has been used to predict the ripening time for crops such as tomatoes, monitor soil moisture, operate agricultural robots, conduct predictive analytics, classify livestock pig call emotions, automate greenhouses, detect diseases and pests, and save water.

Cyber security companies are adopting neural networks, machine learning, and natural language processing to improve their systems.

Applications of AI in cyber security include:

AI tutors such as Khanmigo and Duolingo allow students to get one-on-one help. They can reduce anxiety and stress for students stemming from tutor labs or human tutors.

AI applications that hinder students' ability to stay on task, such as recommender systems, can create a dysfunctional environment, sometimes with revenge effects. AI can be also used to provide early prediction of student success in a virtual learning environment (VLE) such as Moodle.

In the education process, students can personalize their training with the help of artificial intelligence. And for teaching professionals, the technology provided by AI can improve the quality of the educational process and teaching skills.

Since the launch of OpenAI's API in 2022, some students have used the software to generate their essay assignments. AI text detectors can be used to scan essays generated by artificial intelligence in order to try to establish genuine authorship. However, a study found that seven of the most used of these detectors often wrongly flagged articles written by those whose first language was not English as AI-generated, thus discriminating against so called 'non-native' English speakers.

Financial institutions have long used artificial neural network systems to detect charges or claims outside of the norm, flagging these for human investigation. The use of AI in banking began in 1987 when Security Pacific National Bank launched a fraud prevention taskforce to counter the unauthorized use of debit cards. Kasisto and Moneystream use AI.

Banks use AI to organize operations, for bookkeeping, investing in stocks, and managing properties. AI can react to changes when business is not taking place. AI is used to combat fraud and financial crimes by monitoring behavioral patterns for any abnormal changes or anomalies.

The use of AI in applications such as online trading and decision making has changed major economic theories. For example, AI-based buying and selling platforms estimate individualized demand and supply curves and thus enable individualized pricing. AI machines reduce information asymmetry in the market and thus make markets more efficient. The application of artificial intelligence in the financial industry can alleviate the financing constraints of non-state-owned enterprises. Especially for smaller and more innovative enterprises.

Large financial institutions use AI to assist with their investment practices. BlackRock's AI engine, Aladdin, is used both within the company and by clients to help with investment decisions. Its functions include the use of natural language processing to analyze text such as news, broker reports, and social media feeds. It then gauges the sentiment on the companies mentioned and assigns a score. Banks such as UBS and Deutsche Bank use SQREEM (Sequential Quantum Reduction and Extraction Model) to mine data to develop consumer profiles and match them with wealth management products.

ZestFinance's Zest Automated Machine Learning (ZAML) platform is used for credit underwriting. This platform uses machine learning to analyze data including purchase transactions and how a customer fills out a form to score borrowers. The platform is particularly useful to assign credit scores to those with limited credit histories.

AI makes continuous auditing possible. Potential benefits include reducing audit risk, increasing the level of assurance, and reducing audit duration.

AI software, such as LaundroGraph which uses contemporary suboptimal datasets, could be used for anti-money laundering (AML). AI can be used to "develop the AML pipeline into a robust, scalable solution with a reduced false positive rate and high adaptability". A study about deep learning for AML identified "key challenges for researchers" to have "access to recent real transaction data and scarcity of labelled training data; and data being highly imbalanced" and suggests future research should bring-out " explainability, graph deep learning using natural language processing (NLP), unsupervised and reinforcement learning to handle lack of labelled data; and joint research programs between the research community and industry to benefit from domain knowledge and controlled access to data".

In the 1980s, AI started to become prominent in finance as expert systems were commercialized. For example, Dupont created 100 expert systems, which helped them to save almost $10 million per year. One of the first systems was the Protrader expert system that predicted the 87-point drop in the Dow Jones Industrial Average in 1986. "The major junctions of the system were to monitor premiums in the market, determine the optimum investment strategy, execute transactions when appropriate and modify the knowledge base through a learning mechanism."

One of the first expert systems to help with financial plans was PlanPowerm and Client Profiling System, created by Applied Expert Systems (APEX). It was launched in 1986. It helped create personal financial plans for people.

AI can enhance entrepreneurial activity and AI is one of the most dynamic areas for start-ups, with significant venture capital flowing into AI.

Various countries are deploying AI military applications. The main applications enhance command and control, communications, sensors, integration and interoperability. Research is targeting intelligence collection and analysis, logistics, cyber operations, information operations, and semiautonomous and autonomous vehicles. AI technologies enable coordination of sensors and effectors, threat detection and identification, marking of enemy positions, target acquisition, coordination and deconfliction of distributed Joint Fires between networked combat vehicles involving manned and unmanned teams. AI was incorporated into military operations in Iraq and Syria.

In the 2023 Israel-Hamas war, Israel used two AI systems to generate targets to strike: Habsora (translated: " the gospel") was used to compile a list of buildings to target, while "Lavender" produced a list of people. "Lavender" produced a list of 37,000 people to target. The list of buildings to target included Gazan private homes of people that were suspected of affiliation to Hamas operatives. The combination of AI targeting technology with policy shift away from avoiding civilian targets resulted in unprecedented numbers of civilian deaths. IDF officials say the program addresses the previous issue of the air force running out of targets. Using Habsora, officials say that suspected and junior Hamas members homes significantly expand the "AI target bank." An internal source describes the process as a "mass assassination factory".

Worldwide annual military spending on robotics rose from US$5.1 billion in 2010 to US$7.5 billion in 2015. Military drones capable of autonomous action are in wide use. Many researchers avoid military applications.

X-ray of a hand, with automatic calculation of bone age by a computer software

A patient-side surgical arm of Da Vinci Surgical System

Microsoft's AI project Hanover helps doctors choose cancer treatments from among the more than 800 medicines and vaccines. Its goal is to memorize all the relevant papers to predict which (combinations of) drugs will be most effective for each patient. Myeloid leukemia is one target. Another study reported on an AI that was as good as doctors in identifying skin cancers. Another project monitors multiple high-risk patients by asking each patient questions based on data acquired from doctor/patient interactions. In one study done with transfer learning, an AI diagnosed eye conditions similar to an ophthalmologist and recommended treatment referrals.

Another study demonstrated surgery with an autonomous robot. The team supervised the robot while it performed soft-tissue surgery, stitching together a pig's bowel judged better than a surgeon.

Artificial neural networks are used as clinical decision support systems for medical diagnosis, such as in concept processing technology in EMR software.

Other healthcare tasks thought suitable for an AI that are in development include:

Machine learning in sentiment analysis can spot fatigue in order to prevent overwork. Similarly, decision support systems can prevent industrial disasters and make disaster response more efficient. For manual workers in material handling, predictive analytics may be used to reduce musculoskeletal injury. Data collected from wearable sensors can improve workplace health surveillance, risk assessment, and research.

AI can auto- code workers' compensation claims. AI-enabled virtual reality systems can enhance safety training for hazard recognition. AI can more efficiently detect accident near misses, which are important in reducing accident rates, but are often underreported.

AlphaFold 2 can determine the 3D structure of a ( folded) protein in hours rather than the months required by earlier automated approaches and was used to provide the likely structures of all proteins in the human body and essentially all proteins known to science (more than 200 million).

Machine learning has been used for drug design. It has also been used for predicting molecular properties and exploring large chemical/reaction spaces. Computer-planned syntheses via computational reaction networks, described as a platform that combines "computational synthesis with AI algorithms to predict molecular properties", have been used to explore the origins of life on Earth, drug-syntheses and developing routes for recycling 200 industrial waste chemicals into important drugs and agrochemicals (chemical synthesis design). There is research about which types of computer-aided chemistry would benefit from machine learning. It can also be used for " drug discovery and development, drug repurposing, improving pharmaceutical productivity, and clinical trials". It has been used for the design of proteins with prespecified functional sites.

There are various types of applications for machine learning in decoding human biology, such as helping to map gene expression patterns to functional activation patterns or identifying functional DNA motifs. It is widely used in genetic research.

Schema of the process of a semi-automated robot scientist process that includes Web statement extraction and biological laboratory testing

Similarly, there is research and development of biological " wetware computers" that can learn (e.g. for use as biosensors) and/or implantation into an organism's body (e.g. for use to control prosthetics). Polymer-based artificial neurons operate directly in biological environments and define biohybrid neurons made of artificial and living components.

Moreover, if whole brain emulation is possible via both scanning and replicating the, at least, bio-chemical brain - as premised in the form of digital replication in The Age of Em, possibly using physical neural networks - that may have applications as or more extensive than e.g. valued human activities and may imply that society would face substantial moral choices, societal risks and ethical problems such as whether (and how) such are built, sent through space and used compared to potentially competing e.g. potentially more synthetic and/or less human and/or non/less-sentient types of artificial/semi-artificial intelligence. An alternative or additive approach to scanning are types of reverse engineering of the brain.

However, biological computers, even if both highly artificial and intelligent, are typically distinguished from synthetic, often silicon-based, computers - they could however be combined or used for the design of either. Moreover, many tasks may be carried out inadequately by artificial intelligence even if its algorithms were transparent, understood, bias-free, apparently effective, and goal-aligned and its trained data sufficiently large and cleansed - such as in cases were the underlying or available metrics, values or data are inappropriate. Computer-aided is a phrase used to describe human activities that make use of computing as tool in more comprehensive activities and systems such as AI for narrow tasks or making use of such without substantially relying on its results (see also: human-in-the-loop). A study described the biological as a limitation of AI with "as long as the biological system cannot be understood, formalized, and imitated, we will not be able to develop technologies that can mimic it" and that if it was understood this does not mean there being "a technological solution to imitate natural intelligence". Technologies that integrate biology and are often AI-based include biorobotics.

Astronomy, space activities and ufology

Artificial intelligence is used in astronomy to analyze increasing amounts of available data and applications, mainly for "classification, regression, clustering, forecasting, generation, discovery, and the development of new scientific insights" for example for discovering exoplanets, forecasting solar activity, and distinguishing between signals and instrumental effects in gravitational wave astronomy. It could also be used for activities in space such as space exploration, including analysis of data from space missions, real-time science decisions of spacecraft, space debris avoidance, and more autonomous operation.

In the search for extraterrestrial intelligence (SETI), machine learning has been used in attempts to identify artificially generated electromagnetic waves in available data - such as real-time observations - and other technosignatures, e.g. via anomaly detection. In ufology, the SkyCAM-5 project headed by Prof. Hakan Kayal and the Galileo Project headed by Prof. Avi Loeb use machine learning to detect and classify peculiar types of UFOs. The Galileo Project also seeks to detect two further types of potential extraterrestrial technological signatures with the use of AI: 'Oumuamua-like interstellar objects, and non-manmade artificial satellites.

Loeb has speculated that one type of technological equipment the project may detect could be "AI astronauts" and in 2021 - in an opinion piece - that AI "will" "supersede natural intelligence", while Martin Rees stated that there "may" be more civilizations than thought with the "majority of them" being artificial. In particular, mid/far future or non-human applications of artificial intelligence could include advanced forms of artificial general intelligence that engages in space colonization or more narrow spaceflight-specific types of AI. In contrast, there have been concerns in relation to potential AGI or AI capable of embryo space colonization, or more generally natural intelligence-based space colonization, such as "safety of encounters with an alien AI", suffering risks (or inverse goals), moral license/responsibility in respect to colonization-effects, or AI gone rogue (e.g. as portrayed with fictional and HAL 9000). See also: space law and space ethics. Loeb has described the possibility of "AI astronauts" that engage in "supervised evolution" (see also: directed evolution, uplift, directed panspermia and space colonization).

Archaeology, history and imaging of sites

Machine learning can help to restore and attribute ancient texts. It can help to index texts for example to enable better and easier searching and classification of fragments.

Artificial intelligence can also be used to investigate genomes to uncover genetic history, such as interbreeding between archaic and modern humans by which for example the past existence of a ghost population, not Neanderthal or Denisovan, was inferred.

It can also be used for "non-invasive and non-destructive access to internal structures of archaeological remains".

A deep learning system was reported to learn intuitive physics from visual data (of virtual 3D environments) based on an unpublished approach inspired by studies of visual cognition in infants. Other researchers have developed a machine learning algorithm that could discover sets of basic variables of various physical systems and predict the systems' future dynamics from video recordings of their behavior. In the future, it may be possible that such can be used to automate the discovery of physical laws of complex systems.

AI could be used for materials optimization and discovery such as the discovery of stable materials and the prediction of their crystal structure.

In November 2023, researchers at Google DeepMind and Lawrence Berkeley National Laboratory announced that they had developed an AI system known as GNoME. This system has contributed to materials science by discovering over 2 million new materials within a relatively short timeframe. GNoME employs deep learning techniques to efficiently explore potential material structures, achieving a significant increase in the identification of stable inorganic crystal structures. The system's predictions were validated through autonomous robotic experiments, demonstrating a noteworthy success rate of 71%. The data of newly discovered materials is publicly available through the Materials Project database, offering researchers the opportunity to identify materials with desired properties for various applications. This development has implications for the future of scientific discovery and the integration of AI in material science research, potentially expediting material innovation and reducing costs in product development. The use of AI and deep learning suggests the possibility of minimizing or eliminating manual lab experiments and allowing scientists to focus more on the design and analysis of unique compounds.

Machine learning is used in diverse types of reverse engineering. For example, machine learning has been used to reverse engineer a composite material part, enabling unauthorized production of high quality parts, and for quickly understanding the behavior of malware. It can be used to reverse engineer artificial intelligence models. It can also design components by engaging in a type of reverse engineering of not-yet existent virtual components such as inverse molecular design for particular desired functionality or protein design for prespecified functional sites. Biological network reverse engineering could model interactions in a human understandable way, e.g. bas on time series data of gene expression levels.

One concern relates to algorithmic bias, AI programs may become biased after processing data that exhibits bias. ProPublica claims that the average COMPAS-assigned recidivism risk level of black defendants is significantly higher than that of white defendants.

In 2019, the city of Hangzhou, China established a pilot program artificial intelligence-based Internet Court to adjudicate disputes related to ecommerce and internet-related intellectual property claims. Parties appear before the court via videoconference and AI evaluates the evidence presented and applies relevant legal standards.

Another application of AI is in human resources. AI can screen resumes and rank candidates based on their qualifications, predict candidate success in given roles, and automate repetitive communication tasks via chatbots.

AI has simplified the recruiting /job search process for both recruiters and job seekers. According to Raj Mukherjee from Indeed, 65% of job searchers search again within 91 days after hire. An AI-powered engine streamlines the complexity of job hunting by assessing information on job skills, salaries, and user tendencies, matching job seekers to the most relevant positions. Machine intelligence calculates appropriate wages and highlights resume information for recruiters using NLP, which extracts relevant words and phrases from text. Another application is an AI resume builder that compiles a CV in 5 minutes. Chatbots assist website visitors and refine workflows.

An automated online assistant providing customer service on a web page

AI underlies avatars ( automated online assistants) on web pages. It can reduce operation and training costs. Pypestream automated customer service for its mobile application to streamline communication with customers.

Image restoration

AI applications analyze media content such as movies, TV programs, advertisement videos or user-generated content. The solutions often involve computer vision.

Typical scenarios include the analysis of images using object recognition or face recognition techniques, or the analysis of video for scene recognizing scenes, objects or faces. AI-based media analysis can facilitate media search, the creation of descriptive keywords for content, content policy monitoring (such as verifying the suitability of content for a particular TV viewing time), speech to text for archival or other purposes, and the detection of logos, products or celebrity faces for ad placement.

Deep-fakes can be used for comedic purposes but are better known for fake news and hoaxes.

In January 2016, the Horizon 2020 program financed the InVID Project to help journalists and researchers detect fake documents, made available as browser plugins.

In June 2016, the visual computing group of the Technical University of Munich and from Stanford University developed Face2Face, a program that animates photographs of faces, mimicking the facial expressions of another person. The technology has been demonstrated animating the faces of people including Barack Obama and Vladimir Putin. Other methods have been demonstrated based on deep neural networks, from which the name deep fake was taken.

In 2018, Darius Afchar and Vincent Nozick found a way to detect faked content by analyzing the mesoscopic properties of video frames. DARPA gave 68 million dollars to work on deep-fake detection.

Audio deepfakes and AI software capable of detecting deep-fakes and cloning human voices have been developed.

Respeecher is a program that enables one person to speak with the voice of another.

Video content analysis, surveillance and manipulated media detection

Artificial Intelligence is also starting to be used in video production, with tools and softwares being developed that utilize generative AI in order to create new video, or alter existing video. Some of the major tools that are being used in these processes currently are DALL-E, Midjourney, and Runway. Waymark Studios utilized the tools offered by both DALL-E and Midjourney to create a fully AI generated film called The Frost in the summer of 2023. Waymark Studios is experimenting with using these AI tools to generate advertisements and commercials for companies in mere seconds. Yves Bergquist, a director of the AI & Neuroscience in Media Project at USC's Entertainment Technology Center, says post production crews in Hollywood are already using generative AI, and predicts that in the future more companies will embrace this new technology.

AI has been used to compose music of various genres.

AIVA (Artificial Intelligence Virtual Artist), composes symphonic music, mainly classical music for film scores. It achieved a world first by becoming the first virtual composer to be recognized by a musical professional association.

At Sony CSL Research Laboratory, the Flow Machines software creates pop songs by learning music styles from a huge database of songs. It can compose in multiple styles.

The Watson Beat uses reinforcement learning and deep belief networks to compose music on a simple seed input melody and a select style. The software was open sourced and musicians such as Taryn Southern collaborated with the project to create music.

South Korean singer Hayeon's debut song, "Eyes on You" was composed using AI which was supervised by real composers, including NUVO.

Narrative Science sells computer-generated news and reports. It summarizes sporting events based on statistical data from the game. It also creates financial reports and real estate analyses. Automated Insights generates personalized recaps and previews for Yahoo Sports Fantasy Football.

TALESPIN made up stories similar to the fables of Aesop. The program started with a set of characters who wanted to achieve certain goals. The story narrated their attempts to satisfy these goals. Mark Riedl and Vadim Bulitko asserted that the essence of storytelling was experience management, or "how to balance the need for a coherent story progression with user agency, which is often at odds".

South Korean company Hanteo Global uses a journalism bot to write articles.

Literary authors are also exploring uses of AI. An example is David Jhave Johnston's work (2017-2019), where the poet created a daily rite of editing the poetic output of a neural network to create a series of performances and publications.

A local Dutch media group used AI to create automatic coverage of amateur soccer, set to cover 60,000 games in just a single season. NDC partnered with United Robots to create this algorithm and cover what would have never been able to be done before without an extremely large team.

Millions of its articles have been edited by bots which however are usually not artificial intelligence software. Many AI platforms use Wikipedia data, mainly for training machine learning applications. There is research and development of various artificial intelligence applications for Wikipedia such as for identifying outdated sentences, detecting covert vandalism or recommending articles and tasks to new editors.

Machine translation has also be used for translating Wikipedia articles and could play a larger role in creating, updating, expanding, and generally improving articles in the future. A content translation tool allows editors of some Wikipedias to more easily translate articles across several select languages.

In video games, AI is routinely used to generate behavior in non-player characters (NPCs). In addition, AI is used for pathfinding. Some researchers consider NPC AI in games to be a "solved problem" for most production tasks. Games with less typical AI include the AI director of (2008) and the neuroevolutionary training of platoons in Supreme Commander 2 (2010). AI is also used in Alien Isolation (2014) as a way to control the actions the Alien will perform next.

Kinect, which provides a 3D body-motion interface for the Xbox 360 and the Xbox One, uses algorithms that emerged from AI research.

A "cyborg elf" generated by Stable Diffusion

AI platforms such as " DALL-E", Stable Diffusion, Imagen, and Midjourney have been used for generating visual images from inputs such as text or other images. Some AI tools allow users to input images and output changed versions of that image, such as to display an object or product in different environments. AI image models can also attempt to replicate the specific styles of artists, and can add visual complexity to rough sketches.

Since their design in 2014, generative adversarial networks (GANs) have been used by AI artists. GAN computer programming, generates technical images through machine learning frameworks that surpass the need for human operators. Examples of GAN programs that generate art include Artbreeder and DeepDream.

AI has been in use since the early 2000s, most notably by a system designed by Pixar called "Genesis". It was designed to learn algorithms and create 3D models for its characters and props. Notable movies that used this technology included Up and The Good Dinosaur. AI has been used less ceremoniously in recent years. In 2023, it was revealed Netflix of Japan was using AI to generate background images for their upcoming show to be met with backlash online. In recent years, motion capture became an easily accessible form of AI animation. For example, Move AI is a program built to capture any human movement and reanimate it in its animation program using learning AI.

Power electronics converters are used in renewable energy, energy storage, electric vehicles and high-voltage direct current transmission. These converters are failure-prone, which can interrupt service and require costly maintenance or catastrophic consequences in mission critical applications. AI can guide the design process for reliable power electronics converters, by calculating exact design parameters that ensure the required lifetime.

Machine learning can be used for energy consumption prediction and scheduling, e.g. to help with renewable energy intermittency management (see also: smart grid and climate change mitigation in the power grid).

Many telecommunications companies make use of heuristic search to manage their workforces. For example, BT Group deployed heuristic search in an application that schedules 20,000 engineers. Machine learning is also used for speech recognition (SR), including of voice-controlled devices, and SR-related transcription, including of videos.

In the 1990s early AIs controlled Tamagotchis and Giga Pets, the Internet, and the first widely released robot, Furby. Aibo was a domestic robot in the form of a robotic dog with intelligent features and autonomy.

Mattel created an assortment of AI-enabled toys that "understand" conversations, give intelligent responses, and learn.

Side view of a Waymo-branded self-driving car

AI in transport is expected to provide safe, efficient, and reliable transportation while minimizing the impact on the environment and communities. The major development challenge is the complexity of transportation systems that involves independent components and parties, with potentially conflicting objectives.

AI-based fuzzy logic controllers operate gearboxes. For example, the 2006 Audi TT, VW Touareg and VW Caravell feature the DSP transmission. A number of Škoda variants ( Škoda Fabia) include a fuzzy logic-based controller. Cars have AI-based driver-assist features such as self-parking and adaptive cruise control.

Transportation's complexity means that in most cases training an AI in a real-world driving environment is impractical. Simulator-based testing can reduce the risks of on-road training.

Autonomous trucks are in the testing phase. The UK government passed legislation to begin testing of autonomous truck platoons in 2018. A group of autonomous trucks follow closely behind each other. German corporation Daimler is testing its Freightliner Inspiration.

AI has been used to optimize traffic management, which reduces wait times, energy use, and emissions by as much as 25 percent.

Aircraft simulators use AI for training aviators. Flight conditions can be simulated that allow pilots to make mistakes without risking themselves or expensive aircraft. Air combat can also be simulated.

AOD uses the Interactive Fault Diagnosis and Isolation System, or IFDIS, which is a rule-based expert system using information from TF-30 documents and expert advice from mechanics that work on the TF-30. This system was designed to be used for the development of the TF-30 for the F-111C. The system replaced specialized workers. The system allowed regular workers to communicate with the system and avoid mistakes, miscalculations, or having to speak to one of the specialized workers.

Speech recognition allows traffic controllers to give verbal directions to drones.

Artificial intelligence supported design of aircraft, or AIDA, is used to help designers in the process of creating conceptual designs of aircraft. This program allows the designers to focus more on the design itself and less on the design process. The software also allows the user to focus less on the software tools. The AIDA uses rule-based systems to compute its data. This is a diagram of the arrangement of the AIDA modules. Although simple, the program is proving effective.

Neural networks are used by situational awareness systems in ships and boats. There also are autonomous boats.

Autonomous ships that monitor the ocean, AI-driven satellite data analysis, passive acoustics or remote sensing and other applications of environmental monitoring make use of machine learning.

For example, "Global Plastic Watch" is an AI-based satellite monitoring-platform for analysis/tracking of plastic waste sites to help prevention of plastic pollution - primarily ocean pollution - by helping identify who and where mismanages plastic waste, dumping it into oceans.

Code suggestions could be incorrect, and should be carefully reviewed by software developers before accepted.

Tabnine was created by Jacob Jackson and was originally owned by Tabnine company. In late 2019, Tabnine was acquired by Codota. Tabnine tool is available as plugin to most popular IDEs. It offers multiple pricing options, including limited "starter" free version.

Other tools: SourceGraph Cody, CodeCompleteFauxPilot, Tabby

Machine learning has been used for noise-cancelling in quantum technology, including quantum sensors. Moreover, there is substantial research and development of using quantum computers with machine learning algorithms. For example, there is a prototype, photonic, for neuromorphic (quantum-)computers (NC)/ artificial neural networks and NC-using quantum materials with some variety of potential neuromorphic computing-related applications, and quantum machine learning is a field with some variety of applications under development. AI could be used for quantum simulators which may have the application of solving physics and chemistry problems as well as for quantum annealers for training of neural networks for AI applications. There may also be some usefulness in chemistry, e.g. for drug discovery, and in materials science, e.g. for materials optimization/discovery (with possible relevance to quantum materials manufacturing).

AI researchers have created many tools to solve the most difficult problems in computer science. Many of their inventions have been adopted by mainstream computer science and are no longer considered AI. All of the following were originally developed in AI laboratories:

Business websites and social media platforms for businesses like use chatbots for customer interactions like helping in answering frequently asked questions. Chatbots offers 24/7 support and replaces humans thereby helping in cutting business costs.

Ai prompted imagery of architecture

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Applications of artificial intelligence - Wikipedia

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Artificial intelligence (AI) has been used in applications throughout industry and academia. Similar to electricity or computers, AI serves as a general-purpose technology that has numerous applications. Its applications span language translation, image recognition, decision-making, credit scoring, e-commerce and various other domains. A recommendation system predicts the rating or preference a user would give to an item.

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NASA Artificial Intelligence Could Help Astronauts Work More Efficiently in Space

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NASA Artificial Intelligence Could Help Astronauts Work More Efficiently in Space

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In the vast blackness of space, where Earth appears to be just a small dot, artificial intelligence someday will help astronauts and robots make swift decisions and plan quickly, without much assistance from home.

Even at the speed of light, about 186,000 miles per second, radio messages take at least 11 minutes to reach Mars (depending on its varying distance from Earth.) Because of time delays like this, human and robotic crews exploring deep space must depend less on help radioed from colleagues on Earth. Far away in space, astronauts will not only have to craft complex plans in 10 minutes or less, but they may need to make difficult, split-second decisions on their own.

According to NASA scientists, this vision of the future also includes potential cost reductions, since there would be less need for large teams on Earth to make detailed daily plans for deep space missions.

NASA is developing ever more potent artificial intelligence planning software to make distant missions more practical. In the near term, NASA scientists are creating software to reduce Mars teams' daily planning time and costs for missions that are about to begin. For example, in comparison with the Mars Exploration Rover (MER) missions, scientists hope to reduce planning time by a factor of 10 for missions in the near future. Increasingly streamlined, resource-saving approaches like this will play an important role as early as the Phoenix mission to the red planet's North Pole region, scheduled for launch in 2007.

Phoenix's 2008 landing zone is located in an ice-rich region. The lander's robotic arm will dig into the arctic terrain in search of clues to the geologic history of water on Mars. The spacecraft also will look for evidence of zones that could support microbial life.

Meanwhile, more than a year after landing on the red planet in January 2004, two Mars Exploration Rovers continued to look for clues that water may have existed in large amounts on the planet. Mars science teams made detailed plans to orchestrate the rovers' moves.

Image right: Artist rendition of the Mars Rover exploring the surface of Mars. Image courtesy: NASA.

"The Mars Exploration Rover activity planning has taken the human team on Earth about one-and-a-half hours every day for each of the two rovers, " said Alonso Vera, who leads a group developing artificial intelligence software at NASA Ames Research Center, located in California's Silicon Valley. "Our goal is to reduce planning time to about 10 minutes for a typical Mars surface mission, and we are a good part of the way there now," he ventured.

A team of a half dozen scientists at NASA Ames also is improving planning software, which has been used by the Mars rover teams. This software is called the Mixed-initiative Activity Plan Generator (MAPGEN). More than a year after the spacecraft landed on the red planet, scientists continued to use this automated planning and scheduling software to help build and edit science activity plans for the Mars Exploration Rovers based on a 'wish list' of observations and the rovers' available resources.

"MAPGEN is based on 'Europa,' an artificial intelligence planning tool that supported the Deep Space 1 mission before being used on the Mars Exploration Rover (mission)," explained Vera.

NASA launched the 945-pound Deep Space 1 probe on Oct. 24, 1998, to test 12 advanced technologies, including Remote Agent, an artificial intelligence program, intended to lower the cost and risk to future science-driven missions. Remote Agent's basic goal was to demonstrate that spacecraft could operate and be controlled with minimal human help.

Image left: Artist rendition of Remote Agent Spacecraft. Image courtesy: NASA.

NASA's continuing efforts to evolve artificial intelligence for use in future space mission operations are pushing the development envelope, according to Vera. Currently, new software is under development for daily team use during upcoming Mars surface missions. This software is called the 'Science Planning Interface for Engineering' (SPIFe), pronounced 'spiffy.' Scientists hope it will "dramatically" reduce the number of people needed to achieve mission goals, according to Vera.

"The heritage we're looking at with SPIFe is the Constraint Editor software and the MAPGEN software, used for the two Mars Exploration Rovers," said Mike McCurdy, a member of the SPIFe software development team at NASA Ames.

Constraint Editor has allowed Mars Exploration Rover mission scientists to make direct, single changes in their daily rover activity plans. The software also adjusts entire plans to take into account individual changes that scientists make. Meanwhile, MAPGEN software has made some mission choices, and scientists have made the rest of the mission decisions, according to McCurdy.

With artificial intelligence software like MAPGEN - 'mixed initiative' systems -- "the human and the computer are equal partners in a dialog that happens around the generation of a plan," Vera asserted. "Each brings different skills to the table. The human can focus on what humans are good at (high-level decisions, for example), while the computer can focus on what computers are good at -- making thousands of smaller decisions -- or automatically enforcing rules and constraints," he explained.

Image right: Team of NASA scientists working with software. Image courtesy: NASA.

"Before, people have been sitting down and deciding step-by-step what is going to happen during a mission," McCurdy noted.

"Generally, what we are looking at is what kind of information people need when they are planning the rover's work," McCurdy said. "We're looking at how we can make that information readily available."

"A scientist may say, 'Go look at that rock,' and the computer could say, 'in order to look at the rock, you must consider x-y-z.' There is a dialogue between the human beings and the software," McCurdy explained.

"We're still in a prototyping phase at this point," McCurdy said. "Because of our Mars Exploration Rover involvement, we can use that experience as a baseline. We can take some of the Mars Exploration Rover data and software and use that to prototype SPIFe."

"This is a team effort," said McCurdy. "The whole collaboration is called Ensemble, which is shared between the NASA Jet Propulsion Laboratory, Pasadena, Calif., (JPL) and NASA Ames. The JPL component is based on the Mars Exploration Rover Science Activity Planner, now called Maestro," McCurdy explained.

SPIFe software also is vital, because it may well be the forerunner of future software that could help astronauts work independently far from Earth. An important part of the Vision for Space Exploration is to enable crews in spaceships or on the moon to be more self-sufficient. Crews using advanced planning software would not have to depend on people on Earth for plans, according to Vera. Human-robot teams could inspect their vehicles or habitats and make repairs unassisted, Vera ventured.

Image left: Scientists testing software to allow for wireless communication. Humans do not have to dedicate continuous attention to the robot. Image courtesy: NASA.

"A lot of space station time is spent doing repairs," said Vera. "If you are going to provide tools that allow an astronaut to plan a complex sequence of activities for a human-robot team, that astronaut has to be able to plan very quickly in order to adapt to the specific conditions. An hour-and-a-half to plan is excessive," Vera added. "You've got to be able to do it in 10 minutes or less."

One of the capabilities added to SPIFe is multiple timelines for scientists to use instead of the one timeline that MAPGEN provides, according to Vera. Also, SPIFe enables users to view information in many different ways so scientists can better see and grasp problems and processes, said Vera.

"You can view plans in a timeline, or a waterfall-like time link," Vera said. "An activity plan has hundreds of parts. One change can affect all the other activities," he explained.

"We built in a lot of feedback about how a change affects everything else," Vera said. "But in the end, it's the human who is responsible for the plan."

John Bluck
NASA Ames Research Center, Moffett Field, Calif.
Phone: 650/604-5026
Email: jbluck@mail.arc.nasa.gov

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NASA - National Aeronautics and Space Administration

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In the vast blackness of space, where Earth appears to be just a small dot, artificial intelligence someday will help astronauts and robots make swift decisions and plan quickly, without much assistance from home. Even at the speed of light, about 186,000 miles per second, radio messages take at least 11 minutes to reach Mars (depending on its varying distance from Earth.)

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Robotic science has always been a basis for Hollywood entertainment, Sci-Fi novels and childhood fantasies. Artificial intelligence isn't a new concept, and while the technology hasn't advanced to Spielbergian expectations, AI is being used to transform manufacturing, education, business, and even the health care industry. Take a look at some of the ways that AI is improving medicine.

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9 Ways Artificial Intelligence is Affecting the Medical Field | HealthCentral

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Robotic science has always been a basis for Hollywood entertainment, Sci-Fi novels and childhood fantasies. Artificial intelligence isn't a new concept, and while the technology hasn't advanced to Spielbergian expectations, AI is being used to transform manufacturing, education, business, and even the health care industry.

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Artificial intelligence (AI) is arguably the most exciting field in robotics. It's certainly the most controversial: Everybody agrees that a robot can work in an assembly line, but there's no consensus on whether a robot can ever be intelligent.

Like the term "robot" itself, artificial intelligence is hard to define. Ultimate AI would be a recreation of the human thought process -- a man-made machine with our intellectual abilities. This would include the ability to learn just about anything, the ability to reason, the ability to use language and the ability to formulate original ideas. Roboticists are nowhere near achieving this level of artificial intelligence, but they have made a lot of progress with more limited AI. Today's AI machines can replicate some specific elements of intellectual ability.

Computers can already solve problems in limited realms. The basic idea of AI problem-solving is very simple, though its execution is complicated. First, the AI robot or computer gathers facts about a situation through sensors or human input. The computer compares this information to stored data and decides what the information signifies. The computer runs through various possible actions and predicts which action will be most successful based on the collected information. Of course, the computer can only solve problems it's programmed to solve -- it doesn't have any generalized analytical ability. Chess computers are one example of this sort of machine.

Some modern robots also have the ability to learn in a limited capacity. Learning robots recognize if a certain action (moving its legs in a certain way, for instance) achieved a desired result (navigating an obstacle). The robot stores this information and attempts the successful action the next time it encounters the same situation. Again, modern computers can only do this in very limited situations. They can't absorb any sort of information like a human can. Some robots can learn by mimicking human actions. In Japan, roboticists have taught a robot to dance by demonstrating the moves themselves.

Some robots can interact socially. Kismet, a robot at M.I.T's Artificial Intelligence Lab, recognizes human body language and voice inflection and responds appropriately. Kismet's creators are interested in how humans and babies interact, based only on tone of speech and visual cue. This low-level interaction could be the foundation of a human-like learning system.

Kismet and other humanoid robots at the M.I.T. AI Lab operate using an unconventional control structure. Instead of directing every action using a central computer, the robots control lower-level actions with lower-level computers. The program's director, Rodney Brooks, believes this is a more accurate model of human intelligence. We do most things automatically; we don't decide to do them at the highest level of consciousness.

The real challenge of AI is to understand how natural intelligence works. Developing AI isn't like building an artificial heart -- scientists don't have a simple, concrete model to work from. We do know that the brain contains billions and billions of neurons, and that we think and learn by establishing electrical connections between different neurons. But we don't know exactly how all of these connections add up to higher reasoning, or even low-level operations. The complex circuitry seems incomprehensible.

Because of this, AI research is largely theoretical. Scientists hypothesize on how and why we learn and think, and they experiment with their ideas using robots. Brooks and his team focus on humanoid robots because they feel that being able to experience the world like a human is essential to developing human-like intelligence. It also makes it easier for people to interact with the robots, which potentially makes it easier for the robot to learn.

Just as physical robotic design is a handy tool for understanding animal and human anatomy, AI research is useful for understanding how natural intelligence works. For some roboticists, this insight is the ultimate goal of designing robots. Others envision a world where we live side by side with intelligent machines and use a variety of lesser robots for manual labor, health care and communication. A number of robotics experts predict that robotic evolution will ultimately turn us into cyborgs -- humans integrated with machines. Conceivably, people in the future could load their minds into a sturdy robot and live for thousands of years!

In any case, robots will certainly play a larger role in our daily lives in the future. In the coming decades, robots will gradually move out of the industrial and scientific worlds and into daily life, in the same way that computers spread to the home in the 1980s.

The best way to understand robots is to look at specific designs. The links below will show you a variety of robot projects around the world.

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Artificial intelligence (AI) is arguably the most exciting field in robotics. It's certainly the most controversial: Everybody agrees that a robot can work in an assembly line, but there's no consensus on whether a robot can ever be intelligent. Like the term "robot" itself, artificial intelligence is hard to define.

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(Reuters) - U.S. chipmaker Nvidia Corp () showed off on Monday a smaller and more efficient artificial intelligence computer for self-driving cars, saying it would power Baidu's () mapping and autonomous vehicle technology.

Chinese web services company Baidu will deploy Nvidia's new Drive PX 2 as its in-vehicle car computer for its self-driving system, Nvidia said in a press release as‮ ‬it‮ ‬unveiled the computer at the GPU Technology Conference in Beijing.

As more carmakers develop plans for self-driving technology to roll out in their vehicles in the next decade or less, Nvidia is trying to lower the barriers to entry, providing powerful computers to help automakers enter the market.

Earlier this month, Nvidia and Baidu announced a partnership to develop a full self-driving car architecture from the cloud to the vehicle using both companies' expertise in artificial intelligence (AI).

Nvidia said its new Drive PX 2 computer uses 10 watts of power and is half the size of the original version, launched in January. That solves a problem faced by carmakers incorporating self-driving technology - how to pack the punch of AI, which helps cars make decisions, into a compact computer suitable for production-ready vehicles.

Configured with a single processor, the Drive PX 2 fuses incoming data from sensors and uses deep neural networks to produce a complex picture of objects around a vehicle.

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Nvidia shows off smaller artificial intelligence computer for Baidu...

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(Reuters) - U.S. chipmaker Nvidia Corp () showed off on Monday a smaller and more efficient artificial intelligence computer for self-driving cars, saying it would power Baidu's () mapping and autonomous vehicle technology.