Category Archives: Ai

The best way to gain a grip on AI tools and techniques is by using AI and data science cheat sheets

There are various tools and techniques in AI and data science that you need to bear in mind. But its quite challenging for everyone to recall all the functions, formulas, and operations of each of the concepts. But the best way to gain a grip on them is by using AI and data science cheat sheets. AI and Data science cheat sheets are amazing resources for learning and practicing shortcut information about a certain topic. If you are looking for such information, then here are the top AI and data science cheat sheets for you.

Keras is an easy-to-use and powerful library for Theano and TensorFlow that provides a high-level neural networks API to develop and evaluate deep learning models. In no time, this Keras cheat sheet will make you familiar with how you can load datasets from the library itself, preprocess the data, build up a model architecture, and compile, train, and evaluate it.

Given the fact that its one of the fundamental packages for scientific computing, NumPy is one of the packages that you must be able to use and know if you want to do AI with Python. It offers a great alternative to Python lists, as NumPy arrays are more compact, allow faster access to reading and writing items, and are more convenient and more efficient overall.

Pandas library is one of the most preferred tools for data manipulation and analysis, and youll have explored the fast, flexible, and expressive Pandas data structures, maybe with the help of DataCamps Pandas Basics cheat sheet.

Use-Case Based Cheat Sheet by Yogita Kinha, currently Associate Managing Consultant at Mastercard. This one can be considered good because it does a good job aligning the algorithm with the use case. For instance, a regression can be used to predict product demand or sales figures, anomaly detection for fraud, and clustering for customer segmentation.

This machine learning cheat sheet will help you find the right estimator for the job which is the most difficult part. Scikit-learn (formerly scikits. learn) is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means, and DBSCAN.

If you ever observe the specific AI tutorials, you find that the code implementation is done using Jupyter Notebooks. Jupyter Notebooks are great for building various computer science applications and sharing your code with others. It can contain code, text, and visualization all in the same place.

NLP is the most popular branch of AI in the market. It deals with enabling the computer to understand and comprehend natural language. NLP is a technology that enables many of todays advanced technologies like automatic translators and virtual assistants.

Data visualization is a key concept and it is not just used in finding results from the beginning of the project to explore the data and know-how to analyze it but also to find patterns or trends within it. It is one of the best AI and data science cheat sheets to use for 2022.

While talking about visualization, you can design and create your visualization in Python using Matplotlib. And then from Matplotlib to data visualization Pandas are applied for data analysis. It is a potent and sufficient library that allows you to create various types of visualizations with ease.

Data science is a field that makes the collecting and analysis of data to predict future data and events. It helps businesses find trends, patterns, and others. It also helps people to better understand behavior and language. This is one of the best data science cheat sheets that cover the basics of statistics in a short manner. It covers all the information one needs to make decisions and predictions concerning the projects.

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Top 10 AI and Data Science Cheat Sheets that Aspirants Should Refer To - Analytics Insight

The United States and its allies may have built the Maginot Line of the information age. But just as the German armored units broke through the Ardennes Forest in ways the French did not expect, so the Chinese Peoples Liberation Army may break through the United States information-age arsenal, no matter how cutting-edge, if the technology remains tied to the operational concepts of a previous era. China is developing a new concept of warfare, which they call intelligentized warfare (). First mentioned by the government in 2019, it is an innovative military concept with a focus on human cognition, which Beijing intends to use to bring Taiwan under its control without waging conventional warfare. However, only a few of the many studies on intelligentized warfare have focused on this aspect of human cognition.

Chinese thinkers have clearly stated that the core operational concept of intelligentized warfare is to directly control the enemys will. The idea is to use AI to directly control the will of the highest decision-makers, including the president, members of Congress, and combatant commanders, as well as citizens. Intelligence dominance or control of the brain will become new areas of the struggle for control in intelligentized warfare, putting AI to a very different use than most American and allied discussions have envisioned.

This article analyzes the essence of Chinas intelligentized warfare, its possibilities, and limitations, and suggests measures that the United States and its allies should take.

Why China Needs a New Operational Concept

There is a lot of debate about the likelihood and timeline for Chinas strongly desired annexation of Taiwan. Considering Chinas recent military activities around Taiwan, the shortest potential time frame for war to begin is the next two years. Also, given concerns about the sustainability of Chinas economic growth model, there are arguments that war is most likely to occur in the late 2020s as Xi Jinping seeks to build a legacy before the economy falls into long-term stagnation. However, assuming Chinas economic growth continues, another analysis suggests that a war in the 2030s is more likely.

On the other hand, there are debates about the feasibility of occupying Taiwan through conventional warfare. Many studies point out that invading Taiwan through conventional operations would be difficult under present conditions. The tidal currents and shallow seabed in the Taiwan Strait make it difficult for submarines to operate, and landing craft are vulnerable to anti-ship missiles. Chinas existing landing forces are limited, and considering the area of Taiwan, it would not be easy to completely occupy the island using conventional operations alone. In addition, the Chinese military has never fought using modern warfare, and China itself has pointed out in many documents that there are major structural problems with its capabilities.

The initiation of war depends on the decisions of political leaders, and the existence of these problems does not guarantee that a conventional war will not occur. Many possibilities exist that could trigger a war, such as a Taiwanese move toward independence or a Chinese miscalculation regarding the strategic ambiguity of U.S. support for Taiwan. Historically, uncertainty about the intentions of other countries has often been a cause of war.

However, a conventional war would come at a high cost to China. Many studies have pointed out that missile surprise attacks against U.S. military assets, cyber-attacks, and attacks against satellites are likely to take place in the early stages of the war to prevent U.S. support for Taiwan. However, such attacks could arouse public opinion in the United States and lead to full-scale U.S. intervention, which could lead to a long, messy war between the United States and China.

Considering these problems, direct attacks on human cognition are highly logical for China. To solve the problems with their political goal of resolving the Taiwan issue, the Chinese government needs a new operational concept, distinct from an extension of conventional warfare. In an invasion of Taiwan based on intelligentized warfare, the theory is that unmanned weapons would affect the human cognition of Taiwan, the United States, and its allies, resulting in victory without using conventional weapons. The development of such an option would have great appeal to Chinese policymakers.

Intelligentized Warfare as the Solution

In July 2019, the Peoples Liberation Army of China, in its first defense white paper in four years, wrote that war is evolving in form towards informationized warfare, and intelligentized warfare is on the horizon, indicating their recognition that a new form of warfare had emerged. Although the Chinese government has not provided its official definition, several Chinese researchers explain this concept as, integrated warfare waged in land, sea, air, space, electromagnetic, cyber, and cognitive arenas using intelligent weaponry and equipment and their associated operation methods, underpinned by the IoT [internet of things] information system.

Chinese researchers have consistently referred to the cognitive domain () when explaining intelligentized warfare, and this has become a distinctive feature of it. However, only a few analyses in the United States of the intelligentized warfare concept mention the cognitive arena. The Department of Defenses 2021 report to Congress on Chinese military capabilities, which provides the most detailed analysis of intelligentized warfare, focuses on the technology used, defining it as the expanded use of AI and other advanced technologies at every level of warfare, but there is no mention of the cognitive domain. This concept describes human cognition in warfare in parallel with the land, sea, air, space, and cyber arenas, a concept not defined by the United States or its allies.

From the perspective of using AI in warfare, intelligentized warfare is not a new concept. On the contrary, the United States is far ahead of China. The Third Offset Strategy announced in November 2014, long before intelligentized warfare was announced, emphasized leveraging AI and automation. Moreover, U.S. analysts have conducted many excellent recent studies, such as a study on information and command in a techno-cognitive confrontation in addition to decision-centric operations that exploit AI and autonomous systems. Chinas intelligentized warfare overlaps with these concepts in many respects.

The characteristics of intelligentized warfare described by Chinese researchers are the improvement of information-processing capabilities, rapid decision-making using AI, the use of swarms, and the fact that the cognitive domain will become the next most important battlefield after the physical and information space. In the United States, many studies discuss AI in relation to conventional concepts of warfare. However, in Chinas intelligentized warfare, the military will use AI for an entirely new purpose: direct influence on the enemys cognition.

What might this look like in practice? Consider a hypothetical example from a Chinese strategist. An ultra-small intelligent unmanned system, perhaps simulating a small animal, can enter the rooms of the highest decision-makers (president, members of Congress, combatant commanders) without being detected. It would be activated to threaten the target or their family at the right moment, using lethal or non-lethal means, drugs, or some yet-to-be-determined form of mind control. It can also project text, voice, and images to convey its demands, thereby subduing the enemys will and controlling it. If a country threatens or kills decision-makers in this way, citizens may raise a backlash against the enemy country. For this reason, intelligentized warfare will also manipulate public opinion. Fake news and disinformation could discredit the target countrys government, with unmanned systems operating in cyberspace potentially used for this purpose. This manipulation conditions citizens to accept the policy changes caused by decision-makers succumbing to this technique.

These specific methods are described in a book published by Chinese strategist Pang Hong Liang and do not represent an official Chinese operational plan. However, his work is worth paying attention to because he is a pioneer of intelligentized warfare, and he proposed the concept as early as 2004, with an eye to the possibilities of AI in the future. In the 2000s, only a few theorists were discussingintelligentizedwarfare, but the Chinese government finally adopted the concept officially in 2019. Chinese military officers are active in publishing military theories, and often their personalwritingsare mistakenforthe official views of the Chinese government. For example, two Chinese Air Force colonels who did not specialize in strategic analysis personally publishedUnrestricted Warfarein 1999, which was never adopted as Chinas official strategy, but was translated into English withthesubtitleChinas Master Plan to Destroy America, and was erroneously understood by media and policymakers. But this is not a case of mistaking a personal theory for official strategy: the fact that Xi Jinping has officially adopted the theory that Pang HongLiang has been studying for nearly two decades is remarkable.

In addition, it is not only Pang Hong Liang who described these concepts. According to the writings of many Chinese theorists, China plans to avoid escalation through physical attacks, instead attacking the cognition of the people and elites of the United States and its allies, and their intelligence and command systems preemptively, if possible. As mentioned above, if there are major problems with conquering Taiwan through conventional warfare, these methods of intelligentized warfare would be attractive to Chinese policymakers.

Feasibility of A Surprise Attack

Military organizations that operate new technologies using the previous eras operational thought have usually been defeated. Germanys quick defeat of France at the beginning of World War II is one such example. The reason for this was Germanys innovative military concept of blitzkrieg, one of the core technologies of which was the tank. The French had many tanks with better performance than the Germans. French military thought had not changed since World War I, however, and they treated tanks as support weapons for infantry. They could not cope with the blitzkrieg assault from the Ardennes Forest by German armored divisions composed of tanks.

The core technology in Chinas intelligentized warfare is AI. China aims to use AI to develop an unprecedented and innovative operational concept like Germanys blitzkrieg. Chinese strategists believe that even recent warfare strategies using cutting-edge information technologies will become obsolete if this is realized. As happened with France in World War II, even if a country uses new technologies such as tanks or AI, they will not achieve victory in the war if they continue to use the operational concept of the previous era.

In the age of information technology, information networks stretching from the ocean floor to outer space have been at the core of advanced military technology. Information networks have made it possible to shoot precisely and achieve excellent effects with less ammunition. Also, the coordination between sensors and firepower has become much better, making it possible to detect targets and unleash firepower immediately. The symbolic theory of this was Network Centric Warfare, proposed by Arthur K. Cebrowski in 1998. He insisted that rapid decision-making is possible in a networked organization, and overwhelming victory could be expected due to the superiority of decision-making speed.

The United States has built up a powerful military force in the information age and has demonstrated tremendous results. China is devising an asymmetric combat strategy to counter this powerful military force. In addition to missile attacks, cyber-attacks and attacks on satellites can disrupt U.S. information networks, thereby giving China an advantage in the information space. Such asymmetric operations interfere with the accuracy and speed of firepower achieved by information technology.

Chinese theorists, however, are looking further ahead. They believe that the development of information technology has reached its limits, and that future wars will occur in the cognitive domain. The Ardennes Forest of future wars that the Chinese Peoples Liberation Army intends to exploit is a pathway of direct attack against human cognition, using AI and unmanned weapons. The French builders of the Maginot Line could not imagine the assault of German armored forces from the Ardennes Forest. Likewise, to those of us who have been accustomed to almost three decades of information-age warfare since the Gulf War, intelligentized or cognitive warfare seems a strange and unrealistic way of thinking.

Influencing human cognition requires a large amount of detailed personal information to identify influential individuals or to conduct influential operations according to the characteristics of subgroups of people. China has already collected a massive amount of personal information on government officials and ordinary U.S. citizens, ensuring a foundation for influencing peoples cognition. This includes the confidential data of 21.5 million people from the U.S. Office of Personnel Management, the personal information of 383 million people from a major hotel, and sensitive data on more than 100,000 U.S. Navy personnel. The Chinese government has then allowed Chinese IT giants to process this large amount of data, making it useful for intelligence activities. In this way, China has accumulated an enormous amount of data over the years which could be weaponized in the future. China has even succeeded in identifying CIA agents operating in foreign countries using such data. These activities are particularly aggressive and coercive in Taiwan and Hong Kong, which the Chinese government considers its territory. Attempts to use digital means to influence elections have also been seen inTaiwansrecent presidential election.

The idea of a direct attack on human cognition, however, is not new. A representative example is Giulio Douhets aerial warfare in the 1920s. He argued that strategic bombing of enemy capitals would become possible with the advent of airplanes. As a result, citizens, seized with fear, would be expected to demand that their governments end the war, bringing it to an immediate conclusion. However, in World War II, no country surrendered because of strategic bombing, and the new technology of aircraft did not directly affect the will of belligerent countries. The idea of directly influencing human cognition through the latest technology of AI may fail in the same way. The advent of new technology often results in overconfidence in its potential, and the idea that it would solve previously unsolvable military problems has arisen time and again throughout history.

There is an abundance of debate about the use of AI in future warfare, and the consensus that AI will change the characteristics of warfare is growing. There are various analyses of Chinas use of AI, but some suggest that Chinese theorists have overlooked the inherent vulnerabilities of AI and autonomous systems, and have placed too much emphasis on their capabilities. As mentioned above, these theories were adopted out of political necessity to achieve the political goal of annexing Taiwan and may overestimate its feasibility. However, leaving it unnoticed as an object of analysis may lead to a future surprise attack in the Ardennes Forest.

The task now is to ascertain whether the AI in intelligentized warfare is the tank in blitzkrieg or the strategic bomber in aerial warfare.

Measures the United States and Its Allies Should Take

The United States and its allies should analyze intelligentized warfare more to avoid surprise attacks in future wars. They should also designate the cognitive arena as a new operational arena, along with land, air, sea, space, and cyberspace, to raise awareness and invest resources. Furthermore, it is necessary to consider how to win the battle of narratives to counter the manipulation of public opinion in wartime.

Future warfare comes from innovative theory and cannot be derived from existing weapons. In the 1920s, when Germany developed the concept of blitzkrieg warfare, the country did not have any tanks, as the Treaty of Versailles banned them. Even in 1939, when Germany led the blitzkrieg, less than 10 percent of the German troops were armored forces. Most of Chinas colossal military still has outdated equipment, and only a tiny percentage of its troops have modern intelligence equipment. The vision of future warfare lies not in existing equipment, but in military thought. The United States and its allies have to evaluate hypotheses about the future, rigorously and effectively.

Regardless of whether Chinas intelligentized warfare succeeds or not, it is important to pay attention to the cognitive domain in warfare and consider the means to win in it. The idea of directly influencing human cognition is not new, but with the development of AI, it may be more feasible. Intelligentized warfare uses AI to intimidate the enemys decision-makers and manipulate public opinion. Dealing with the direct manipulation of public opinion requires a complex operation. There are many studies about the manipulation of public opinion by China and Russia in peacetime, but there have been few analyses on wartime efforts. In warfare, both sides will use their own narratives. For example, in the case of the Taiwan-China conflict, the Chinese narrative will be something like, These are Chinas domestic problems that other countries should not be involved in. In contrast, the narrative of the United States and its allies will likely be about the defense of democratic society. Many sub-narratives will support these narratives. There will be a battle of the narratives to determine which narratives will penetrate and gain support in the international community.

Chinas intelligentized warfare is a far cry from the information age wars that have been waged in the past and is not simply the use of AI or unmanned weapons systems in warfare. Its feasibility is unknown and may have been overestimated, out of political necessity. But with its goals of influencing human cognition directly and controlling the enemys will, it is a revolutionary idea.

Col. Koichiro Takagi is a senior fellow of Training Evaluation Research and Development Command, Japan Ground Self-Defense Force. All views in the article are his own. He is a military theorist in Japan who has published many peer-reviewed articles on future wars. He is a former deputy chief, Defense Operation Section, 1st Operations Division, J-3, Joint Staff Japan, and has designed joint operation plans and orders in the severe security environment in East Asia.

Image: geralt, pixabay license

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New Tech, New Concepts: China's Plans for AI and Cognitive Warfare - War on the Rocks

So youre into the latest gadgets, and youve updated your home with things like smart locks, coffee makers, and more. Well, that just scratches the surface of what futuristic tech has to offer, and these gadgets coming soon in 2022 prove it.

Related: Latest tech gadgets to buy in 2022: Dyson Zone, eero Pro 6E & more

From things like autonomous delivery vehicles to wearable 3D computer mice, todays list has a ton of gadgets youve probably never heard of. And we sure hope to see them this year!

Using robotics, AI, and electric vehicle technology, the Udelv Transporter autonomous delivery vehicle aims to revolutionize package delivery, going driverless. This smart autonomous vehicle can travel highways and haul up to 2,000 lbs. The Deliver-as-a-Service platform even offers end-to-end trip planning.

This gadgets price is TBA. Reserve yours on the official website.

Show your clients 3D models of homes, designs, and more without needing to render with the Acer ConceptD 7 SpatialLabs Edition laptop. It uses AI tech to automatically convert 2D images into stereo 3D.

This gadget is coming soon and is priced at $3,499.99. Learn more about it on the official website.

Give presentations without looking down at notes, or see directions while keeping your eyes on the road with the Mojo Vision Lens AR contact lens. It looks like a typical contact lens but using microelectronics, it shows you useful information about your environment, making it one of our favorite gadgets coming soon in 2022.

This gadget is coming soon, and its price is TBA. Learn more about it on the official website.

Wouldnt it be nice if you could control more gadgets with your voice or just a gesture? The Next Industries Tactigon SKIN wearable 3D mouse lets you do just that. It attaches to your hand, recognizing 48 different gestures. Then, its voice command lets you open and close programs, zoom, and much more.

This gadget is coming soon, and its price is TBA.

Eliminate dead zones in your home or office with the TP-Link Archer AXE200 Omni Wi-Fi 6E router. Yes, those antennas actually rotate, while the tri-band Wi-Fi with a new 6 GHz band delivers over 10 Gbps, making it one of the best gadgets coming soon in 2022.

This gadget is coming soon, and its price is TBA.

Connect with smart home devices in every corner of your home with the Samsung Home Hub smart home dashboard. It works with gadgets in the Cooking, Pet, Energy, Air, Clothing Care, and Home Care Wizard categories.

This gadget is coming soon, and its price is TBA.

Interact with guests and let them in from anywhere with the eufy Security Video Smart Lock. With this futuristic smart lock, you can watch visitors in 2K clarity, get alerts when your kids leave, unlock the door quickly, and so much more.

Preorder this gadget for $269 on Kickstarter.

Designed for creators, the Snap Spectacles 4th Generation lightweight AR glasses feature dual 3D waveguide displays that you can program and interact with, making them some of the most innovative coming soon gadgets in 2022.

This gadgets price is TBA. Learn how to reserve a pair on the official website.

Elevate your VIVE Focus 3 VR Headset experience by pairing it with the HTC VIVE Wrist Tracker. Its smaller and lighter than the controller. Plus, its intuitive to use and tracks from your fingertips to your elbow.

This gadget is coming soon and is priced at $129. Learn more about it on the official website.

Rumor has it that the PlayStation VR2 headset will launch later this year. For now, Sony says it boasts a comfortable design, breathtaking visual fidelity in 4K HDR, headset feedback, and a more intuitive controller. Whatever the final specs, this gadget is sure to immerse you in other worlds.

This gadgets price is TBA. Read more about it on the official blog.

If youre like us, were sure youre looking forward to these gadgets coming soon in 2022. Do you know of any upcoming tech we should add to future lists? Let us know in the comments.

Want more news, reviews, and guides from Gadget Flow? Follow us on Apple News, Google News, Feedly, and Flipboard. If you use Flipboard, you should definitely check out our Curated Stories. We publish 3 new stories every day, so make sure to follow us to stay updated!

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Lauren has been writing and editing since 2008. She loves working with text and helping writers find their voice. When she's not typing away at her computer, she cooks and travels with her husband and two daughters.

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Gadgets coming soon in 2022from AI to VR, these futuristic tech gadgets deserve a space in your life - Gadget Flow

OAKLAND, Calif., April 6 (Reuters) - Microsoft Corp's (MSFT.O) LinkedIn boosted subscription revenue by 8% after arming its sales team with artificial intelligence software that not only predicts clients at risk of canceling, but also explains how it arrived at its conclusion.

The system, introduced last July and described in a LinkedIn blog post on Wednesday, marks a breakthrough in getting AI to "show its work" in a helpful way.

While AI scientists have no problem designing systems that make accurate predictions on all sorts of business outcomes, they are discovering that to make those tools more effective for human operators, the AI may need to explain itself through another algorithm.

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The emerging field of Explainable AI, or XAI, has spurred big investment in Silicon Valley as startups and cloud giants compete to make opaque software more understandable and has stoked discussion in Washington and Brussels where regulators want to ensure automated decision-making is done fairly and transparently.

AI technology can perpetuate societal biases like those around race, gender and culture. read more Some AI scientists view explanations as a crucial part of mitigating those problematic outcomes.

U.S. consumer protection regulators including the Federal Trade Commission have warned over the last two years that AI that is not explainable could be investigated. The EU next year could pass the Artificial Intelligence Act, a set of comprehensive requirements including that users be able to interpret automated predictions.

Proponents of explainable AI say it has helped increase the effectiveness of AIs application in fields such as healthcare and sales. Google Cloud (GOOGL.O) sells explainable AI services that, for instance, tell clients trying to sharpen their systems which pixels and soon which training examples mattered most in predicting the subject of a photo.

But critics say the explanations of why AI predicted what it did are too unreliable because the AI technology to interpret the machines is not good enough.

LinkedIn and others developing explainable AI acknowledge that each step in the process - analyzing predictions, generating explanations, confirming their accuracy and making them actionable for users - still has room for improvement.

But after two years of trial and error in a relatively low-stakes application, LinkedIn says its technology has yielded practical value. Its proof is the 8% increase in renewal bookings during the current fiscal year above normally expected growth. LinkedIn declined to specify the benefit in dollars, but described it as sizeable.

Before, LinkedIn salespeople relied on their own intuition and some spotty automated alerts about clients' adoption of services.

Now, the AI quickly handles research and analysis. Dubbed CrystalCandle by LinkedIn, it calls out unnoticed trends and its reasoning helps salespeople hone their tactics to keep at-risk customers on board and pitch others on upgrades.

LinkedIn says explanation-based recommendations have expanded to more than 5,000 of its sales employees spanning recruiting, advertising, marketing and education offerings.

"It has helped experienced salespeople by arming them with specific insights to navigate conversations with prospects. Its also helped new salespeople dive in right away," said Parvez Ahammad, LinkedIn's director of machine learning and head of data science applied research.

TO EXPLAIN OR NOT TO EXPLAIN?

In 2020, LinkedIn had first provided predictions without explanations. A score with about 80% accuracy indicates the likelihood a client soon due for renewal will upgrade, hold steady or cancel.

Salespeople were not fully won over. The team selling LinkedIn's Talent Solutions recruiting and hiring software were unclear on how to adapt their strategy, especially when the odds of a client not renewing were no better than a coin toss.

Last July, they started seeing a short, auto-generated paragraph that highlights the factors influencing the score.

For instance, the AI decided a customer was likely to upgrade because it grew by 240 workers over the past year and candidates had become 146% more responsive in the last month.

In addition, an index that measures a client's overall success with LinkedIn recruiting tools surged 25% in the last three months.

Lekha Doshi, LinkedIn's vice president of global operations, said that based on the explanations sales representatives now direct clients to training, support and services that improve their experience and keep them spending.

But some AI experts question whether explanations are necessary. They could even do harm, engendering a false sense of security in AI or prompting design sacrifices that make predictions less accurate, researchers say.

Fei-Fei Li, co-director of Stanford University's Institute for Human-Centered Artificial Intelligence, said people use products such as Tylenol and Google Maps whose inner workings are not neatly understood. In such cases, rigorous testing and monitoring have dispelled most doubts about their efficacy.

Similarly, AI systems overall could be deemed fair even if individual decisions are inscrutable, said Daniel Roy, an associate professor of statistics at University of Toronto.

LinkedIn says an algorithm's integrity cannot be evaluated without understanding its thinking.

It also maintains that tools like its CrystalCandle could help AI users in other fields. Doctors could learn why AI predicts someone is more at risk of a disease, or people could be told why AI recommended they be denied a credit card.

The hope is that explanations reveal whether a system aligns with concepts and values one wants to promote, said Been Kim, an AI researcher at Google.

"I view interpretability as ultimately enabling a conversation between machines and humans," she said.

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Reporting by Paresh Dave; Editing by Kenneth Li and Lisa Shumaker

Our Standards: The Thomson Reuters Trust Principles.

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AI is explaining itself to humans. And it's paying off - Reuters

Its not new that the emerging artificial intelligence technology aims to take over the writing space.

High-end and intermediate writers have expressed cynical views and even fears over the AI writing software introduction.

For proponents of the AI writing application, its not so! According to them, the concept behind the creation is to help lessen the workload of writers.

In the meantime, the number of AIs has surpassed expectations. From small companies to big names in tech, AIs are attempting to become the next big thing for content marketing.

In fact, due to the improvement in its machine language and data analytics, some companies prefer AI content marketing.

This begs the question, is AI the future of writing? Or will it replace the human writing form?

Read on!

If youve been wondering what goes on behind every AI, its simple, a machine language.

AI writing tools usenatural language generationto produce written words from mere data. You just input data in, and the rest is history.

An AI is effective when a large amount of data needs conversion into written language that anyone can understand.

Scientists didnt stop at a mere natural language generation; more work began after the discovery in 2016.

They rebranded and created a more advanced AI that didnt need data labeling while saving time and money.

In May 2020, another model was created. Its called OpenAIs GPT-3 (Generative Pre-trained Transformer-3).

This new and advanced machine language is the largest neural network globally. The machine has a model with over 175 million parameters.

The GPT-3 is different from other AIs because it processes information like the human thinking faculty.

It executes tasks like answering questions, filling in blanks, publishing articles, writing songs, jokes, and even questions about the philosophical aspect of life!

There are even better and more advanced ALs being created. In particular, some companies have copied the language system of the OpenAIsGTP-3and made better improvements.

In May 2020, Google launched a new chatbot called LAMDA. Its designed to hold meaningful, emotional, and intellectual conversations.

Whats more, Beijing has attempted to create the first living AI. In June 2020, the Beijing Academy of Artificial Intelligence (BAAI) launched a new AI calledWu Dao 2.0.

The AI gave life to its first virtual student,Hua Zhubingto write songs and codes and possess a large memory.

This has become a lingering question in every writers mind and probably a writers worst fear, especially writers in the business ofcontent writing or copywriting.

While AI technology keeps advancing, its arguably not going to be the future of writing.

Writers are more skilled in capturing the essence and reader perception. Itll take years of research for any AI to exhibit such traits. An AI cant write emphatically as a human would.

Although AI has shown great dexterity and expertise in writing, there are still major gaps that cant be filled.

Below are a few reasons why writers need not worry about AIs for now:

An AI lacks the uniqueness human writers bring to their articles. Its an intricate factor that distinguishes the pro from the amateur.

AIs may be perfect for data gathering and analyzing complex words but possess poor creative analytics.

They poorly express themselves due to a lack of cognition and emotion. Only humans can process such complexities.

AIs produce whatever you run into them. The process is like garbage in garbage out.

The workload still falls on a human to carefully reread and edit AI-generated articles.

Yes, it might be difficult to detect an AI-written article. However, AIs struggle to compose coherent and engaging content to captivate readers. Engagement is the footstone of every good content.

Writers are more skilled in capturing the essence of every article. It may take years of research for AIs to exhibit such traits.

If theres one thing an AI greatly lacks in information presentation, its a lack of direct and multiple evaluations.

For instance, an AI cant interpret a proverb or an idiom. They arent recognizable in data analysis.

Also, they cant differentiate between the linguistic complexities, like when not to use offensive words.

For now, human writers have nothing to worry about. AIs and humans can coexist symbiotically without one dominating the other.

Though many believe its economical and more reliable than human writers. However, the barrier to the above statement is the cost of an AI to start up. Only big tech companies can afford excellent and effective AI writing tools.

The risk-on human writers are quite low. However, it shouldnt stop you from honing your skill!

Sam Altman, CEO of Open AIs, in a tweet published in early June 2021, stated that AIs might likely affect physical jobs more than remote jobs such as coding, writing, administrative jobs, and co.

Whether we like it or not, AIs are here to stay. We cant fight them. However, we can create a means to incorporate them into the physical fold without any job losses.

They immensely contribute to accelerating a writers process and simplifying the workload.

We already use low-resource AIs like Grammarly and plagiarism checkers. Still, human editors and proofreaders are thriving.

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Is artificial intelligence the future of writing? - The Rude Baguette

In machine learning, understanding why a model makes certain decisions is often just as important as whether those decisions are correct. For instance, a machine-learning model might correctly predict that a skin lesion is cancerous, but it could have done so using an unrelated blip on a clinical photo.

While tools exist to help experts make sense of a models reasoning, often these methods only provide insights on one decision at a time, and each must be manually evaluated. Models are commonly trained using millions of data inputs, making it almost impossible for a human to evaluate enough decisions to identify patterns.

Now, researchers at MIT and IBM Research have created a method that enables a user to aggregate, sort, and rank these individual explanations to rapidly analyze a machine-learning models behavior. Their technique, called Shared Interest, incorporates quantifiable metrics that compare how well a models reasoning matches that of a human.

Shared Interest could help a user easily uncover concerning trends in a models decision-making for example, perhaps the model often becomes confused by distracting, irrelevant features, like background objects in photos. Aggregating these insights could help the user quickly and quantitatively determine whether a model is trustworthy and ready to be deployed in a real-world situation.

In developing Shared Interest, our goal is to be able to scale up this analysis process so that you could understand on a more global level what your models behavior is, says lead author Angie Boggust, a graduate student in the Visualization Group of the Computer Science and Artificial Intelligence Laboratory (CSAIL).

Boggust wrote the paper with her advisor, Arvind Satyanarayan, an assistant professor of computer science who leads the Visualization Group, as well as Benjamin Hoover and senior author Hendrik Strobelt, both of IBM Research. The paper will be presented at the Conference on Human Factors in Computing Systems.

Boggust began working on this project during a summer internship at IBM, under the mentorship of Strobelt. After returning to MIT, Boggust and Satyanarayan expanded on the project and continued the collaboration with Strobelt and Hoover, who helped deploy the case studies that show how the technique could be used in practice.

Human-AI alignment

Shared Interest leverages popular techniques that show how a machine-learning model made a specific decision, known as saliency methods. If the model is classifying images, saliency methods highlight areas of an image that are important to the model when it made its decision. These areas are visualized as a type of heatmap, called a saliency map, that is often overlaid on the original image. If the model classified the image as a dog, and the dogs head is highlighted, that means those pixels were important to the model when it decided the image contains a dog.

Shared Interest works by comparing saliency methods to ground-truth data. In an image dataset, ground-truth data are typically human-generated annotations that surround the relevant parts of each image. In the previous example, the box would surround the entire dog in the photo. When evaluating an image classification model, Shared Interest compares the model-generated saliency data and the human-generated ground-truth data for the same image to see how well they align.

The technique uses several metrics to quantify that alignment (or misalignment) and then sorts a particular decision into one of eight categories. The categories run the gamut from perfectly human-aligned (the model makes a correct prediction and the highlighted area in the saliency map is identical to the human-generated box) to completely distracted (the model makes an incorrect prediction and does not use any image features found in the human-generated box).

On one end of the spectrum, your model made the decision for the exact same reason a human did, and on the other end of the spectrum, your model and the human are making this decision for totally different reasons. By quantifying that for all the images in your dataset, you can use that quantification to sort through them, Boggust explains.

The technique works similarly with text-based data, where key words are highlighted instead of image regions.

Rapid analysis

The researchers used three case studies to show how Shared Interest could be useful to both nonexperts and machine-learning researchers.

In the first case study, they used Shared Interest to help a dermatologist determine if he should trust a machine-learning model designed to help diagnose cancer from photos of skin lesions. Shared Interest enabled the dermatologist to quickly see examples of the models correct and incorrect predictions. Ultimately, the dermatologist decided he could not trust the model because it made too many predictions based on image artifacts, rather than actual lesions.

The value here is that using Shared Interest, we are able to see these patterns emerge in our models behavior. In about half an hour, the dermatologist was able to make a confident decision of whether or not to trust the model and whether or not to deploy it, Boggust says.

In the second case study, they worked with a machine-learning researcher to show how Shared Interest can evaluate a particular saliency method by revealing previously unknown pitfalls in the model. Their technique enabled the researcher to analyze thousands of correct and incorrect decisions in a fraction of the time required by typical manual methods.

In the third case study, they used Shared Interest to dive deeper into a specific image classification example. By manipulating the ground-truth area of the image, they were able to conduct a what-if analysis to see which image features were most important for particular predictions.

The researchers were impressed by how well Shared Interest performed in these case studies, but Boggust cautions that the technique is only as good as the saliency methods it is based upon. If those techniques contain bias or are inaccurate, then Shared Interest will inherit those limitations.

In the future, the researchers want to apply Shared Interest to different types of data, particularly tabular data which is used in medical records. They also want to use Shared Interest to help improve current saliency techniques. Boggust hopes this research inspires more work that seeks to quantify machine-learning model behavior in ways that make sense to humans.

This work is funded, in part, by the MIT-IBM Watson AI Lab, the United States Air Force Research Laboratory, and the United States Air Force Artificial Intelligence Accelerator.

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Does this artificial intelligence think like a human? - MIT News

Fake faces created by artificial intelligence (AI) are considered more trustworthy than images of real people, a study has found.

The results highlight the need forsafeguards to prevent deep fakes, which have already been used for revenge porn, fraud and propaganda, the researchers behind the report say.

Real (R) and synthetic (S) faces were rated for trustworthiness with statistically significant results. (Image: PNAS)

The study by Dr Sophie Nightingale from Lancaster University in the UK and Professor Hany Farid from the University of California, Berkeley, in the US asked participants to identify a selection of 800 faces as real or fake, and to rate their trustworthiness.

After three separate experiments, the researchers found the AI-created synthetic faces were on average rated 7.7% more trustworthy than the average rating for real faces. This is statistically significant, they add. Thethree faces rated most trustworthy were fake, while the four faces rated most untrustworthy were real, according to the magazine New Scientist.

The fake faces were created usinggenerative adversarial networks (GANs), AI programmes that learn to create realistic faces through a process of trial and error.

The study,AI-synthesized faces are indistinguishable from real faces and more trustworthy, is published in the journal, Proceedings of the National Academy of Sciences of the United States of America (PNAS).

It urges safeguards to be put into place, which could include incorporating robust watermarks into the image to protect the public from deep fakes.

Guidelines on creating and distributing synthesized images should also incorporate ethical guidelines for researchers, publishers, and media distributors, the researchers say.

The four most (top row) and four least (bottom row) trustworthy faces, according to the study. (Image: PNAS)

Using AI responsibly is the immediate challenge facing the field of AI governance, the World Economic Forum says.

In its report,The AI Governance Journey: Development and Opportunities, the Forum says AI has been vital in progressing areas like innovation, environmental sustainability and the fight against COVID-19. But the technology is also challenging us with new and complex ethical issues and racing ahead of our ability to govern it.

The report looks at a range of practices, tools and systems for building and using AI.

These include labelling and certification schemes; external auditing of algorithms to reduce risk; regulating AI applications, and greater collaboration between industry, government, academia and civil society to develop AI governance frameworks.

Republished with permission of the World Economic Forum. Read theoriginal article.

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People trust AI fake faces more than real ones, study finds - Big Think

AI is improving and despite the many obstacles that it faces, researchers and scientists are still focusing on the many upgrades and factors that may better the intelligent system. OpenAI brought its new DALL-E 2 to the world and it focuses on bringing better AI to its systems, particularly with a new vision and language that aims to surpass its past ventures.

(Photo : Steve Jennings/Getty Images for TechCrunch)SAN FRANCISCO, CALIFORNIA - OCTOBER 03: OpenAI Co-Founder & CEO Sam Altman speaks onstage during TechCrunch Disrupt San Francisco 2019 at Moscone Convention Center on October 03, 2019 in San Francisco, California.

OpenAI's DALL-E 2is a new system from the company and its aim is to bring realistic images and art that will border on a natural language that the system would understand and perceive better. The new system picks up from the DALL-E's first release, and from there, the AI's capabilities are seeing an improvement in its offers.

Text is enough for the DALL-E 2 to create realistic images and art from scratch. OpenAI improved its systems for better use of AI, and the results bring impeccable results that may only be seen on science fiction shows that create these types of futuristic AIs for the public.

The new AI brings a focus on the system that aims to help the many creators now.

Read Also:Drug-Developing AI Identifies 40,000 Bioweapon Chemicals For Just 6 Hours, But Here's a Warning

According toTech Crunch, some of the keys to improving AI is to bring a significant change to vision and language, particularly with the way it perceives things and creates output from them. The research and many changes to the code for deep learning are a massive move for science, especially now that the world is heavily reliant on AI.

Many companies use AI now, and most of the world's systems are relying on self-learning technology that focuses on deep knowledge of the many systems in the world. A recent venture by NVIDIA focused onbringing 3D images from 2D renders it has, with the use of artificial intelligence to help its cause.

There are many speculations and doubts against AI, especially with the skills that it can do that rival the sentient beings from the many processes and outputs in life. One example would be thecapabilities of AI to create art on its own, and the system focuses on an original artwork that it conceptualized without the need for human intervention.

Artificial intelligence is a vast study now, and it focuses on the many features that aim to help humans in their daily lives and the processes that humans face every day. OpenAI's efforts in the AI industry are a massive help to many, bringing their capabilities to the table, aiming to bring many offers to everyone in their daily lives.

Related Article:Cardiac Arrest-Detecting AI Now Under Development; Here's How It Reduces Death Cases

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AI to Improve with Vision and LanguageNew OpenAIs DALL-E 2 is One Promising Technology - Tech Times

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Register today!

One of the main roadblocks preventing the enterprise from putting artificial intelligence (AI) into action is the transition from development and training to production environments. To gain real benefits from the technology, this must be done at the speed and scale of todays business environment, which few organizations are capable of doing.

This is why the interest in merging AI with devops is gaining steam. Forward-leaning enterprises are trying to blend machine learning (ML) in particular with the traditional devops model, which creates an MLops process that streamlines and automates the way intelligent applications are developed and deployed and then updated on a continual basis to increase the value of its operations over time.

According to data scientist Aymane Hachcham, MLops helps the enterprise deal with a number of significant issues when it comes to effectively building and managing intelligent applications. For one thing, the data sets used in the training phase are extremely large and are continuously expanding and changing. This requires constant monitoring, experimentation, adjustment and retraining of AI models, all of which becomes time-consuming and expensive under traditional, manually driven development and production models.

To effectively implement MLops, the enterprise will need to develop a number of core capabilities, such as full lifecycle tracking, metadata optimized for model training, hyperparameter logging and a solid AI infrastructure consisting not only of server, storage and networking solutions but software tools capable of rapid iteration of new machine learning models. And all of this will have to be designed around the two main forms of MLops: predictive, which attempts to chart future outcomes based on past data and prescriptive, which strives to make recommendations before decisions are made.

Mastering this discipline is the only plausible way for AI to trickle down from the Fortune 500 enterprise to the rest of the world, says Greenfield Partners Shay Grinfeld and Itay Inbar. The fact is, upwards of 90 % of ML projects fail under current development and deployment frameworks, which is simply not tenable for the vast majority of organizations. MLops provides a dramatically more efficient development pipeline that not only reduces the overall cost of the process but can turn failures into successes at a rapid pace. The end result is that the barriers to AI implementation drop to a level that is comfortable for the vast majority of enterprises, leading to widespread distribution and eventual integration into mainstream data operations.

MLops is still an emerging field, so it may be tempting to write it off as just another techy buzzword, says business analytics and data science consultant Sibanjan Das. But its track-record so far has been pretty good, provided it is designed the right way and targeted at the proper goal: to maximize model performance and improve ROI. This requires careful coordination between the various components that create an MLops environment, such as the CI/CD pipeline itself, as well as model serving, version control and data monitoring. And dont forget to build robust security and governance mechanisms to minimize the risk of the ML models activities and the chance of it being compromised.

Even though MLops is designed for automation and even autonomy, dont overlook the human element as a key driver of successful outcomes. A recent report by Dataiku noted that over the past year, companies have come to the realization that they cannot scale AI without building diverse teams that can implement and benefit from the technology. MLops should be a critical component of this strategy because it supports diversification in the development, deployment and management of AI projects. And just judging by Gartners MLops framework, a broad set of skills will be required to ensure that outcomes provide top value to the enterprise business model.

Even the most advanced technology is of little value if it cannot successfully transition from the lab to the real world. AI is now at the point where it must begin making a valuable contribution to humanity or it will become the digital equivalent of the Edsel: flashy and full of gadgets but with little practical value.

MLops cannot guarantee success, of course, but it can lower the cost of experimentation and failure, while at the same time putting it in the hands of more people who can figure out for themselves how to use it.

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MLops: The Key to Pushing AI into the Mainstream - VentureBeat

ByLisa Ercolano

Johns Hopkins University and Amazon are teaming up to harness the power of artificial intelligence to transform the way humans interact online and with the world. The new JHU + Amazon Initiative for Interactive AI, housed in the Johns Hopkins Whiting School of Engineering, will leverage the university's world-class expertise in interactive AI to advance groundbreaking technologies in machine learning, computer vision, natural language understanding, and speech processing; democratize access to the benefits of AI innovations; and broaden participation in research from diverse, interdisciplinary scholars and other innovators.

Amazon's investment will span five years, comprising doctoral fellowships, sponsored research funding, gift funding, and community projects. Sanjeev Khudanpur, an associate professor of electrical and computer engineering at the Whiting School, will serve as the initiative's founding director. Khudanpur is an expert in the application of information-theoretic methods to human language technologies such as automatic speech recognition, machine translation, and natural language processing.

"Hopkins is already renowned for its pioneering work in these areas of AI, and working with Amazon researchers will accelerate the timetable for the next big strides," Khudanpur said. "I often compare humans and AI to Luke Skywalker and R2D2 in Star Wars: They're able to accomplish amazing feats in a tiny X-wing fighter because they interact effectively to align their complementary strengths. I am very excited at the prospect of the Hopkins AI community coming together under the auspices of this initiative, and charting the future of transformational, interactive AI together with Amazon researchers,"

Ed Schlesinger, dean of the Whiting School, said, "We are very excited to work with Amazon in this new initiative. We value the challenges that they bring us and the life-changing potential of the solutions we will create together, and look forward to strengthening our work together over the coming years."

Amazon's funding will support a broad range of activities, including annual fellowships for doctoral students; research projects led by Hopkins Engineering faculty in collaboration with postdoctoral researchers, undergraduate and graduate students, and research staff; and events and activities, such as lectures, workshops, and competitions aimed at making AI activities more accessible to the general public in the Baltimore-Washington region.

Prem Natarajan, Alexa AI vice president of natural understanding, says the partnership underscores Amazon's commitment to addressing the greatest challenges in Al, democratizing access to the benefits of Al innovations, and broadening participation in research from diverse, interdisciplinary scholars and other innovators.

"This initiative brings together the top talent at Amazon and Johns Hopkins in a joint mission to drive groundbreaking advances in interactive and multimodal AI," Natarajan said. "These advances will power the next generation of interactive AI experiences across a wide variety of domainsfrom home productivity to entertainment to health."

The two organizations have teamed up in the past, with four Johns Hopkins faculty members joining Amazon as part of its Scholars program: Ozge Sahin, a professor of operations management and business analytics at the Johns Hopkins Carey Business School, in 2019, and in 2020, Gregory Hager, Mandell Bellmore Professor of Computer Science; Ren Vidal, Herschel Seder Professor of Biomedical Engineering and director of the Mathematical Institute for Data Science; and Marin Kobilarov, associate professor of mechanical engineering.

The new initiative will build on Hopkins Engineering's existing strengths in the areas of machine learning, computer vision, natural language understanding, and speech processing. Its Mathematical Institute for Data Science conducts cutting-edge research on the mathematical, statistical, and computational foundations of machine learning and computer vision. The Center for Imaging Science and the Laboratory for Computational Sensing and Robotics conduct fundamental and applied research in nearly every area of basic and applied computer vision. The university's Center for Language and Speech Processing, one of the largest and most influential academic research centers of its kind in the world, conducts research in acoustic processing, automatic speech recognition, cognitive modeling, computational linguistics, information extraction, machine translation, and text analysis. CLSP researchers conducted some of the foundational research that led to the development of digital voice assistants.

"AI has tremendous potential to enhance human abilities, and to reach it, AI of the future will interact with humans the same way we naturally interact with each other. What endeared Amazon Alexa to users was the effortlessness of the interaction. I envision that the research done under this initiative will make it possible for us to use much more powerful AI in equally effortless ways, regardless of our own physical limitations," Khudanpur said.

Hager, a director for Amazon Physical Retail, and Vidal, currently an Amazon Scholar in visual search and AR, were instrumental in helping Amazon and JHU establish the collaboration.

"Computer vision and machine learning are transforming the way in which humans shop, share content, and interact with each other," Vidal said. "This partnership will lead to new collaborations between JHU and Amazon scientists that will help translate cutting-edge advances in deep learning and visual recognition into algorithms that help humans interact with the world."

Seth Zonies, a director of business development for Johns Hopkins Technology Ventures, the university's commercialization and industry collaboration arm, said, "This collaboration represents the opportunity to harness academic ingenuity to address needs in society through industry collaboration. The engineering faculty at Johns Hopkins are committed to applied research, and Amazon is at the forefront of product development in this field. We expect this collaboration to result in deployable, high-impact innovation."

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Johns Hopkins and Amazon collaborate to explore transformative power of AI - The Hub at Johns Hopkins