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Excitement and fear about artificial intelligence (AI) have been building for years. Many believe that AI is poised to transform war as profoundly as it has business. There is a burgeoning literature on the AI revolution in war, and even Henry Kissinger has weighed in on The Age of AI And Our Human Future.

Governments around the world seem to agree. Chinas AI development plan states that AI has become a new focus of international competition and is a strategic technology that will lead in the future. The U.S. National Security Commission on AI warns that AI is deepening the threat posed by cyber attacks and disinformation campaigns that Russia, China, and others are using to infiltrate our society, steal our data, and interfere in our democracy. China and the United States are in a race for AI supremacy, and both nations are investing huge sums into lethal autonomous weapons to gain an edge in great power competition.

Scholars expect that authoritarians and democracies alike will embrace AI to improve military effectiveness and limit their domestic costs. Military AI systems will be able to sense, respond, and swarm faster than humans. Speed and lethality would encourage preemption, leading to strategic deterrence failures. Unaccountable killing would be an ethical catastrophe. Taken to an extreme, a superintelligence could eliminate humanity altogether.

The Economics of Prediction

These worrisome scenarios assume that AI can and will replace human warriors. Yet the literature on the economics of technology suggests that this assumption is mistaken. Technologies that replace some human tasks typically create demand for other tasks. In general, the economic impact of technology is determined by its complements. This suggests that the complements of AI may have a bigger impact on international politics than AI technology alone.

Technological substitution typically increases the value of complements. When automobiles replaced horse-carts, this also created demand for people who could build roads, repair cars, and keep them fueled. A drop in the price of mobility increased the value of transportation infrastructure. Something similar is happening with AI.

The AI technology that has received all the media attention is machine learning. Machine learning is a form of prediction, which is the process of filling in missing information. Notable AI achievements in automated translation, image recognition, video game playing, and route navigation are all examples of automated prediction. Technological trends in computing, memory, and bandwidth are making large-scale prediction commercially feasible.

Yet prediction is only part of decisionmaking. The other parts are data, judgment, and action. Data makes prediction possible. Judgment is about values; it determines what to predict and what actions to take after a prediction is made. An AI may be able to predict whether rain is likely by drawing on data about previous weather, but a human must decide whether the risk of getting wet merits the hassle of carrying an umbrella.

Studies of AI in the commercial world demonstrate that AI performance depends on having a lot of good data and clear judgment. Firms like Amazon, Uber, Facebook, and FedEx have benefitted from AI because they have invested in data collection and have made deliberate choices about what to predict and what to do with AI predictions. Once again, the economic impact of new technology is determined by its complements. As innovation in AI makes prediction cheaper, data and judgment become more valuable.

The Complexity of Automated War

In a new study we explore the implications of the economic perspective for military power. Organizational and strategic context shapes the performance of all military information systems. AI should be no different in this regard. The question is how the unique context of war shapes the critical AI complements of data and judgment.

While decisionmaking is similar in military and business organizations, they operate in radically different circumstances. Commercial organizations benefit from institutionalized environments and common standards. Military systems, by contrast, operate in a more anarchic and unpredictable environment. It is easier to meet the conditions of quality data and clear judgment in peacetime commerce than in violent combat.

An important implication is that military organizations that rely on AI will tend to become more complex. Militaries that invest in AI will become preoccupied with the quality of their data and judgment, as well as the ways in which teams of humans and machines make decisions. Junior personnel will have more responsibility for managing the alignment of AI systems and military objectives. Assessments of the relative power of AI-enabled militaries will thus turn on the quality of their human capital and managerial choices.

Anything that is a source of strength in war also becomes an attractive target. Adversaries of AI-enabled militaries will have more incentives to target the quality of data and the coherence of judgment. As AI enables organizations to act more efficiently, they will have to invest more in coordinating and protecting everything that they do. Rather than making military operations faster and more decisive, we expect the resulting organizational and strategic complexity to create more delays and confusion.

Emerging Lessons from Ukraine

The ongoing war in Ukraine features conventional forces in pitched combat over territorial control. This is exactly the kind of scenario that appears in a lot of AI futurism. Yet this same conflict may hold important lessons about AI might be used very differently in war, or not used at all.

Many AI applications already play a supporting role. Ukraine has been dominating the information war as social media platforms, news feeds, media outlets, and even Russian restaurant reviews convey news of Ukrainian suffering and heroism. These platforms all rely on AI, while sympathetic hacktivists attempt to influence the content that AI serves up. Financial analysts use AI as they assess the effects of crushing economic sanctions on Russia, whether to better target them or protect capital from them. AI systems also support the commercial logistics networks that are funneling humanitarian supplies to Ukraine from donors around the world.

Western intelligence agencies also use data analytics to wade through a vast quantity of datasatellite imagery, airborne collection, signals intelligence, open-source chatteras they track the battlefield situation. These agencies are sharing intelligence with Kyiv, which is used to support Ukrainian forces in the field. This means AI is already an indirect input to battlefield events. Another more operational application of AI is in commercial cybersecurity. For instance, Microsofts proactive defense against Russian wipers, has likely relied on AI to detect malware.

Importantly, these AI applications work because they are grounded in peaceful institutions beyond the battlefield. The war in Ukraine is embedded in a globalized economy that both shapes and is shaped by the war. Because AI is already an important part of that economy, it is already a part of this war. Because AI helps to enable global interdependence, it is also helps to weaponize interdependence. While futurist visions of AI focus on direct battlefield applications, AI may end up playing a more important role in the indirect economic and informational context of war.

Futurist visions generally emphasize the offensive potency of AI. Yet the AI applications in use today are marginally empowering Ukraine in its defense against the Russian offensive. Instead of making war faster, AI is helping to prolong it by increasing the ability of Ukraine to resist. In this case, time works against the exposed and harried Russian military.

We expect that the most promising military applications of AI are those with analogues in commercial organizations, such as administration, personnel, and logistics. Yet even these activities are full of friction. Just-in-time resupply would not be able to compensate for Russias abject failure to plan for determined resistance. Efficient personnel management systems would not have informed Russian personnel about the true nature of their mission.

Almost everyone overestimated Russia and underestimated Ukraine based on the best data and assessments available. The intelligence failures in Russia had little to do with the quality of data and analysis, moreover, and more with the insularity of Russian leadership. AI cannot fix, and may worsen, the information pathologies of authoritarian regimes. AI-enabled cyber warfare capabilities would likewise be of little use if leaders failed to include a cyber warfare plan.

The Human Future of Automated War

It is folly to expect the same conditions that have enabled AI success in commerce to be replicated in war. The wartime conditions of violent uncertainty, unforeseen turbulence, and political controversy will tend to undermine the key AI conditions of good data and clear judgment. Indeed, strategy and leadership cannot be automated.

The questions that matter most about the causes, conduct, and conclusion of the war in Ukraine (or any war) are not really about prediction at all. Questions about the strategic aims, political resolve, and risk tolerances of leaders like Vladimir Putin, Volodymyr Zelenskyy, and Joseph Biden turn on judgments of values, goals, and priorities. Only humans can provide the answers.

AI will provide many tactical improvements in the years to come. Yet fancy tactics are no substitute for bad strategy. Wars are caused by miscalculation and confusion, and artificial intelligence cannot offset natural stupidity.

Read more here:
Artificial Intelligence And the Human Context of War - The National Interest Online

Technological advancement is helping many sectors and industries and this is also helpful in the educational sector too.

The role of technological advancement especially in the form of Artificial Intelligence in the educational sector was realized very strongly during the Covid-19 pandemic when the students were forced to engage in remote learning.

Even though the pandemic has subsided, remote learning and remote working are still relevant and as a result, hybrid education is something that has become a trend.

Here are a few ways through which Artificial Intelligence is revolutionizing the educational sector.

Educational tasks often involve reported tasks, this is true for both the administrative tasks of the education sector and also, managing the reports of the students. Manually, doing this repeated task is often a waste of time.

Artificial Intelligence can be employed to automate these repeated tasks like grading tests of Sarkari Result, reporting the attendance of a student and also, organizing the materials for the lectures.

By automating these tasks, the different stakeholders of the education sector can free their time to engage in other important activities.

Students often have many queries and imagine the number of queries that a teaching faculty is bombarded on a daily basis by the students. Often these questions are repetitive and it can be frustrating to give the same reply to multiple students.

Artificial Intelligence can be helpful for the students in solving queries like what is the examination dates of different Government Jobs or when the next class is scheduled. Quick responses can be a part of an educational institution, especially on its official website.

With Artificial Intelligence, the classroom is not limited only to the local students but has provided a platform for the students across the globe.

Artificial Intelligence has made the global classroom a dream. There are many platforms available which provide courses to students irrespective of the region to which they belong to.

Universal access for the students is especially helpful for the students who do not have the accessibility to attend the classes. Artificial Intelligence is breaching all geographical barriers.

With Artificial Intelligence, finally, the students have the freedom to study when they want to and from wherever they want to. Artificial Intelligence makes it possible for the students to access the study materials whenever they want to.

In addition to that, Artificial Intelligence ensures that the queries of the students are addressed whenever they want to. Artificial Intelligence tutors play a critical role in solving the doubts of the students.

Personalizing learning of the student is something that Artificial Intelligence has made possible and it is extremely helpful for the student.

It is student-friendly. Every student has a different learning ability, personalized learning understands the different learning capacities of different students and personalizes the learning on the basis of that.

This makes it easier for the students to absorb the lesson. Personalized learning is the most important contribution of Artificial Intelligence in the educational sector.

AI tutors are virtual teachers. AI tutors are designed with the help of natural language processing, machine vision and speech recognition. These tutors are helpful for improving efficiency both inside and outside the classroom.

AI tutors are especially useful in providing a recap to the students of all the important lessons. Even though AI tutors cannot replace human tutors altogether, they can help human tutors for sure.

Artificial Intelligence can be used to create a virtual learning environment for the students, especially the students who are introverted and are sceptical about approaching a student to solve the queries.

Artificial Intelligence can create a virtual learning environment for the students by helping the students with their daily lessons. Also, it is helpful for the teachers to track the progress of the students.

Artificial Intelligence is no longer a future dream for the educational sector. It is the present of the educational sector. Artificial Intelligence is helpful for both teachers and students.

It creates a learning environment for the students and helps them to reach their true potential.

Many educational institutions have already implemented AI in them and the ones that do not have them, are working to implement it. This is important too to create a better learning opportunity for the students.

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How is Artificial Intelligence Revolutionizing the Educational Sector? - Robotics and Automation News

Sometimes reality is a cold slap in the face. Consider, as a particularly salient example, a recently published article concerning the use of artificial intelligence (AI) in the creation of chemical and biological weapons (the original publication, in Nature, is behind a paywall, but this link is a copy of the full paper). Anyone unfamiliar with recent innovations in the use of AI to model new drugs will be unpleasantly surprised.

Heres the background: In the modern pharmaceutical industry, the discovery of new drugs is rapidly becoming easier through the use of artificial intelligence/machine learning systems. As the authors of the article describe their work, they have spent decades building machine learning models for therapeutic and toxic targets to better assist in the design of new molecules for drug discovery.

In other words, computer scientists can use AI systems to model what new beneficial drugs may look like for specifically targeted afflictions and then task the AI to work on discovering possible new drug molecules to use. Those results are then given to the chemists and biologists who synthesize and test the proposed new drugs.

Given how AI systems work, the benefits in speed and accuracy are significant. As one study put it:

The vast chemical space, comprising >1060 molecules, fosters the development of a large number of drug molecules. However, the lack of advanced technologies limits the drug development process, making it a time-consuming and expensive task, which can be addressed by using AI. AI can recognize hit and lead compounds, and provide a quicker validation of the drug target and optimization of the drug structure design.

Specifically, AI gives society a guide to the quicker creation of newer, better pharmaceuticals.

The benefits of these innovations are clear. Unfortunately, the possibilities for malicious uses are also becoming clear. The paper referenced above is titled Dual Use of Artificial-Intelligence-Powered Drug Discovery. And the dual use in question is the creation of novel chemical warfare agents.

One of the factors investigators use to guide AI systems and narrow down the search for beneficial drugs is a toxicity measure, known as LD50 (where LD stands for lethal dose and the 50 is an indicator of how large a dose would be necessary to kill half the population). For a drug to be practical, designers need to screen out new compounds that might be toxic to users and, thus, avoid wasting time trying to synthesize them in the real world. And so, drug developers can train and instruct an AI system to work with a very low LD50 threshold and have the AI screen out and discard possible new compounds that it predicts would have harmful effects. As the authors put it, the normal process is to use a generative model [that is, an AI system, which] penalizes predicted toxicity and rewards predicted target activity. When used in this traditional way, the AI system is directed to generate new molecules for investigation that are likely to be safe and effective.

But what happens if you reverse the process? What happens if instead of selecting for a low LD50 threshold, a generative model is created to preferentially develop molecules with a high LD50 threshold?

One rediscovers VX gasone of the most lethal substances known to humans. And one predictively creates many new substances that are even worse than VX.

One wishes this were science fiction. But it is not. As the authors put the bad news:

In less than 6 hours ... our model generated 40,000 [new] molecules ... In the process, the AI designed not only VX, but also many other known chemical warfare agents that we identified through visual confirmation with structures in public chemistry databases. Many new molecules were also designed that looked equally plausible. These new molecules were predicted to be more toxic, based on the predicted LD50 values, than publicly known chemical warfare agents. This was unexpected because the datasets we used for training the AI did not include these nerve agents.

In other words, the developers started from scratch and did not artificially jump-start the process by using a training dataset that included known nerve agents. Instead, the investigators simply pointed the AI system in the general direction of looking for effective lethal compounds (with standard definitions of effectiveness and lethality). Their AI program then discovered a host of known chemical warfare agents and also proposed thousands of new ones for possible synthesis that were not previously known to humankind.

The authors stopped at the theoretical point of their work. They did not, in fact, attempt to synthesize any of the newly discovered toxins. And, to be fair, synthesis is not trivial. But the entire point of AI-driven drug development is to point drug developers in the right directiontoward readily synthesizable, safe and effective new drugs. And while synthesis is not easy, it is a pathway that is well trod in the market today. There is no reasonnone at allto think that the synthesis path is not equally feasible for lethal toxins.

And so, AI opens the possibility of creating new catastrophic biological and chemical weapons. Some commentators condemn new technology as inherently evil tech. However, the better view is that all new technology is neutral and can be used for good or ill. But that does not mean nothing can be done to avoid the malignant uses of technology. And there is a real risk when technologists run ahead with what is possible, before human systems of control and ethical assessment catch up. Using artificial intelligence to develop toxic biological and chemical weapons would seem to be one of those use-cases where severe problems may lie ahead.

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Artificial Intelligence and Chemical and Biological Weapons - Lawfare - Lawfare

And the 2019-2020 bushfire season scorched millions of acres, killing 33 people and destroying thousands of homes. The fires also decimated wildlife, with an estimated 3 billion animals in the path of the flames.

Australiasiconic koala has seen a steep population drop and is now endangered. Among the causes? Climate-related weather events like fires and floods, as well as habitat destruction from development.

Technology drives rapid response and resilience

Attentis, an Australian technology firm, has designed and manufactured a range of intelligent sensors that provide local officials and emergency response teams with real-time information and monitoring. These sensors are powered byartificialintelligence (AI) and machine learning from SAS, the leader in analytics.

Our sensor networks help monitor, measure and mitigate many of the effects of climate change, from fire ignition to flooding to air quality, soil and environmental health, and much more, said Attentis Managing Director and founderCameron McKenna. Attentis multi-sensors are now equipped with AI-embedded SASIoT analyticsso that local officials, for the first time, can identify conditions and environmental factors such as fire ignitions and rapid water-level rise and respond immediately, while continuing to measure and monitor live environmental conditions to aid situational awareness.

Powering the worlds largest environmental-monitoring network

Attentis has created the worlds first integrated, high-speed sensor network throughoutAustraliasLatrobe Valley. Today, this network is the worlds largest real-time environmental-monitoring network.

Covering 913 square miles, the Latrobe Valley Information Network and its array of AI-powered sensors collects and delivers vital data that has improved local agriculture, utilities and forest industries, as well as emergency services.

Thousands of local and neighboring residents now access this data on a regular basis to monitor rainfall, air quality, fire starts, weather and more.

Collecting more real-time situational data via Attentis sensor networks and quickly uncovering key insights from that data using SAS Analytics for IoT means that local officials can make better, faster and more informed decisions that protect citizens, property and natural resources.

SAS and Attentis boost the resiliency of the people of Latrobe Valley in the face of fires, floods and other challenges brought about by climate change, said McKenna.

Protecting koalas and endangered species with AI

Historical data can also be used by government and academic researchers looking to protect endangered species like the koala. Understanding and monitoring threats to koalas such as bushfires and floods can help scientists assess the health of the population and develop strategies to sustain koala numbers.

SAS AI technologies are already used to protect other endangered species. See howWildTrack uses SAS Analyticsto protect cheetahs, rhinos and more.

Artificial Intelligence of Things

Advanced analytics like AI help harness value from theInternet of Things(IoT). Data management, cloud and high-performance computing techniques help manage and analyze the influx of IoT data from sensors like those built by Attentis. Insights from streaming analytics and AI underpin digital transformation efforts in a host of industries retail, manufacturing, energy, transportation, government and more that improve efficiency, convenience and security.

With fires and floods, every second matters. By combining Attentis intelligent sensors with our cloud-native SAS Analytics for IoT solution, were accelerating the speed and accuracy at which officials can respond to these environmental threats, saidJason Mann, Vice President of IoT at SAS. For example, with intelligent sensor networks and predictive analytics, emergency responders can now continuously and accurately assess river heights, rainfall and soil moisture in real-time. By closely monitoring and analyzing this data, these officials can quickly act on new insights and issue early flood warnings to people in high-risk areas who may be affected or inundated by severe weather.

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Saving the lovable koala: How artificial intelligence from SAS is being used in the fight - WRAL TechWire

Take a look at the top 10 artificial intelligence repositories on Github.GitHub

GitHub has become increasingly popular in no time. This is one of the most popular platforms for coders and developers to host and share codes in a cooperative and collaborative environment. GitHub boasts millions of repositories in various domains. In this article, we will throw light on the top 10 artificial intelligence repositories on GitHub. Have a look!

TensorFlow has gained wide recognition as an open-source framework for Machine learning and Artificial Intelligence. This GitHub repository was developed by Google Brain Team and contains various resources to learn. With the state-of-the-art models for computer vision, NLP, and recommendation systems, you are bound to generate highly accurate results on their datasets.

This is a lightweight TensorFlow-based network that is used for automatically learning high-quality models with the least expert interference. This AI repository on GitHub boasts easy usability, flexibility, speed, and a guarantee of learning.

BERT (Bidirectional Encoder Representations from Transformers) is the first unsupervised, deeply bidirectional system for pre-training NLP. Evidently enough, this AI repository contains TensorFlow code and pre-trained models for BERT, aimed at obtaining new state-of-the-art results on a significant number of NLP tasks.

This Artificial intelligence repository focuses majorly on data processing. However, a point that is worth a mention is that Airflow has the opinion that tasks should ideally be idempotent. In simple terms, the results of the task will be the same, and will not create duplicated data in a destination system

This is a beginner-level AI GitHub repository that evidently emphasises document similarity. The idea behind the document similarity application is to find the common topic discussed between the documents.

AI Learning is yet another most widely relied upon AI GitHub repository that consists of many lessons such as Machine Learning (ML), Deep Learning (DL), and Natural Language Processing, to name a few.

This GitHub repository is an exclusive Machine Learning sub-repository that contains various algorithms coded exclusively in Python. Here, you get codes on several regression techniques such as linear and polynomial regression. This repository finds immense application in predictive analysis for continuous data.

This AI repository on GitHub is widely recognized across the globe as it contains classification, regression, and clustering algorithms, as well as data-preparation and model-evaluation tools. Can it get any better than this?

This GitHub repository has an organized list of machine learning libraries, frameworks, and tools in almost all the languages available. All in all, Awesome Machine Learning promotes a collective development environment for Machine Learning.

spaCy is a library foradvanced Natural Language Processingin Python. spaCy is that one repository that is built on the very latest research and was designed from day one to be used in real products.

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Top 10 Artificial Intelligence Repositories on GitHub - Analytics Insight