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Artificial Intelligence Has a ‘Last Mile’ Problem, and Machine Learning Operations Can Solve It – Built In

August 1, 2022 | Author: admin

With headlines emerging about artificial intelligence (AI) reaching sentience, its clear that the power of AI remains both revered and feared. For any AI offering to reach its full potential, though, its executive sponsors must first be certain that the AI is a solution to a real business problem.

And as more enterprises and startups alike develop their AI capabilities, were seeing a common roadblock emerge known as AIs last mile problem. Generally, when machine learning engineers and data scientists refer to the last mile, theyre referencing the steps required to take an AI solution and make it available for generalized, widespread use.

The last mile describes the short geographical segment of delivery of communication and media services or the delivery of products to customers located in dense areas. Last mile logistics tend to be complex and costly to providers of goods and services who deliver to these areas.(Source: Investopedia).

Democratizing AI involves both the logistics of deploying the code or model as well as using the appropriate approach to track the models performance. The latter becomes especially challenging, however, since many models function as black boxes in terms of the answers that they provide. Therefore, determining how to track a models performance is a critical part of surmounting the last-mile hurdle. With less than half of AI projects ever reaching a production win, its evident that optimizing the processes that comprise the last mile will unlock significant innovation.

The biggest difficulty developers face comes after they build an AI solution. Tracking its performance can be incredibly challenging as its both context-dependent and varies based on the type of AI model. For instance, while we must compare the results of predictive models to a benchmark, we can examine outputs from less deterministic models such as personalization models with respect to their statistical characteristics. This also requires a deep understanding of what a good result actually entails. For example, during my time working on Google News, we created a rigorous process to evaluate AI algorithms. This involved running experiments in production and determining how to measure their success. The latter required looking at a series of metrics (long vs. short clicks, source diversity, authoritativeness, etc.) to determine if in fact the algorithm was a win. Another metric that we tracked on Google News is new source diversity in personalized feeds. In local development and experiments, the results might appear good, but at scale and as models evolve, the results may skew.

The solution, therefore, is two-fold:

Machine learning operations (MLOps) is becoming a new category of products necessary to adopt AI. MLOps are needed to establish good patterns and the tools required to increase confidence in AI solutions. Once AI needs are established, decision-makers must weigh the fact that while developing in-house may look attractive, it can be a costly affair given the approach is still nascent.

Looking ahead, cloud providers will start offering AI platforms as a commodity. In addition, innovators will consolidate more robust tooling, and the same rigors that we see with traditional software development will standardize and operationalize within the AI industry. Nonetheless, tooling is only a piece of the puzzle. There is significant work required to improve how we take an AI solution from idea to test to reality and ultimately measure success. Well get there more quickly when AIs business value and use case is determined from the outset.

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Artificial Intelligence Act in the European Union (EU): Risks and regulations – MediaNama.com

August 1, 2022 | Author: admin

The European Commission proposedthe Artificial Intelligence Act (AI Act) last April, after over two years of public consultations. The Act lays down a uniform legal framework [across the EU] for the development, marketing and use of artificial intelligence in conformity with Union values. These values are indicated by democracy, freedom, and equality.

The Act uses a risk-based regulatory approach to all AI systems providers in the EU irrespective of whether they are established within the Union or in a third country. It prohibits certain kinds of AI, places higher regulatory scrutiny on High Risk AI, and limits the use of certain kinds of surveillance technologies, among other objectives.To implement the regulations, the Act establishes the formation of a Union-level European Artificial Intelligence Board. Individual Member States are to direct one or more national competent authorities to implement the Act.

The Act was introduced amid growing recognition of the usefulness of AI in the EUfor example investing in AI and promoting its use can provide businesses with competitive advantages that support socially and environmentally beneficial outcomes.However, it also appears cognizant of the many risks associated with AIwhich can harm protected fundamental rights as well as the public interest. The Act states that it is an attempt to strike a proportionate balance between supporting AI innovation and economic and technological growth, and protecting the rights and interests of EU citizens. Ultimately, the legislation aims to establish a legal framework for trustworthy AI in Europe that helps instil consumer confidence in the technology.

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Why it matters: Described by MIT Technology Review as the most important AI law youve never heard of, commentators suggest that if passed, the Act could once again shape the contours of global technology regulation according to European values. The European Unions (EU) General Data Protection Regulation (GDPR) is already an inspiration for data protection laws in multiple countriesa success story for the EUs brand of Internet regulation that the AI Act explicitly seeks to replicate amid geopolitical rifts in cyber governance. However, some commentators believe the Acts arbitrarilydefinedrisks may stifle innovation by batting so heavily for civil libertiesif not, the Act may prohibitively raise compliance costs for companies seeking to do business with the EU. Additionally, the proposed Act reportedly complements the GDPR, other IT laws in the Union, and various EU charters on fundamental rightsa relatively harmonious regulatory approach that may be useful to India as it negotiates IT legislation and harms across a battery of emerging sectors.

Article 3 of the AI Actdefines AI as any software that is developed with one or more of the techniques and approaches listed in Annex I and can, for a given set of human-defined objectives, generate outputs such as content, predictions, recommendations, or decisions influencing the environments they interact with.

This definition is intended to be technology neutral and future proofwhich means that it hopes to be broad enough to counter new uses of AI in the coming years.

Protecting citizen rights and freedoms is critical, as the Act notes. However, doing so should not outright hinder how all AI is used across the EUafter all, some AI systems demand higher levels of scrutiny than others. The Acts approach centres around maintaining regulatory proportionality.

What this means: it deploys a risk-based regulatory approach that casts restrictions and transparency obligations on AI systems based on their potential to cause harm. This, it hopes, will limit regulatory oversight to only sensitive AI systemsresulting in fewer restrictions on the trade and use of AI within the single market. Two types of AI systems are largely discussed in the Act: Prohibited and High Risk AI systems.

Unacceptable or Prohibited AI Systems: The Act prohibits the use of certain types of AI for the unacceptable risks they pose. These systems can be used for manipulative, exploitative and social control practices. They would violate Union values of freedom, equality and democracy, among others. They would also violate Fundamental Rights in the EU, including rights to non-discrimination and privacy, as well as the rights of a child.

What harms do these systems pose?: For example, AI systems that distort human behaviour may cause psychological harm through subliminal actions that humans cannot perceive. AI social scoring systems (parallels of which are seen in China) may discriminate against individuals or social groups based on data that is devoid of context. Facial Recognition Technologies used by law enforcement agencies are also considered violations of the right to privacy and should be prohibitedexcept in three narrowly defined scenarios where protecting the public interest outweighs the risks of the AI system. These include when searching for the victims of a crime, when investigating terrorist threats or threats to a persons life and safety, or the detection, localisation, identification or prosecution of the perpetrators of specific crimes in the EU.

High Risk AI Systems: High Risk AI systems are those which may significantly harm either the safety, health, or fundamental rights of people in the EU.These systems are often incorporated into larger human-operated services.

What harms do these systems pose?: Examples include autonomous robots performing complex tasks (such as in the automotive industry). In the education sector, testing systems powered by AI could perpetuate discriminatory and stigmatising attitudes toward specific students, affecting their education and livelihood. The same is the case for AI systems determining credit worthinessgiven that they can shape who has access to financial resources.

How are they regulated?:High Risk systems are not as concerning as Unacceptable systems in the Actbut they still face stronger regulatory scrutiny and can only be placed on the Union market or put into service if they comply with certain mandatory requirements. To develop a high level of trustworthiness of high-risk AI systems [among consumers], these systems have to pass a conformity assessment before entering the market, to ensure they meet these uniform standards.

Some ring-fencing initiatives that systems providers must comply with include ensuring that only high-quality data sets are used to power AI systemsto avoid errors and discrimination. Systems providers should also keep detailed records on how the AI system functions to ensure compliance with the Act. To inform users of potential risks better, High Risk systems should be accompanied by relevant documentation and instructions of use and include concise and clear information, including in relation to possible risks to fundamental rights and discrimination. They should be designed such that human beings can oversee their functioning, as well as be resilient to malicious cyber attacks that attempt to change their behaviours (leading to new harms). In certain cases, users should also be notified that they are interacting with an AI system. The proposal suggests that by 2025, compliance costs for suppliers of an average High Risk AI system worth 170,000 could range between 6,000-7,000.

In order to foster innovation, the Act encourages EU Member States to develop artificial intelligence regulatory sandboxeswhere research can be conducted on these technologies under strict supervision before they reach the market.

Non-High Risk AI Systems: Some AI systems may not induce harms as significant as those above. In this case, they could be assumed to be every AI system that is not Prohibited or High Risk. While the Acts provisions dont apply to these simpler systems, it encourages them to comply with them to improve public trust in these systems. The Act has little else to say on these systems.

In many ways, the Actre-emphasises the importance of harmonised business and trade across the EUs single marketas well as Brussels dominance in shaping overarching laws for the bloc. The language of the Act is categorically wary of Member State-level legislation on regulating AIreiterating that conflicting legislation will only complicate the protection of fundamental rights and ease of doing business in the EU. Thats why the Act positions itself as one that harmonises European values across Member States.

That being said, the language of the Act balances domestic interests with extra-territorial ambition. While it seeks to achieve the above objectives, it repeatedly speaks of the Acts potential to shape global regulation on AI, in line with European values. This is not an unfounded hope for a bloc now known to steer technology laws.

Such outward-looking planks can also be read against a growing discourse in global cyber governancewhere debatable dichotomies are drawn by States between the relatively free Internet of democracies, and the walled Internet of China.

While acknowledging the legitimate concerns of algorithmic biases and profiling, some commentators note that the Acts compliance requirements for High Risk AI Systems providers may be impossible to meet. For example, AI systems make use of massive data setsensuring that they are error-free may be a tall order. Additionally, it may not always be possible for a systems operator to fully comprehend how AI worksespecially given the increasing complexity of the technology. If these mechanisms cannot be entirely deciphered, then estimating their potential harms also becomes difficult. Others add that the scope of what constitutes High Risk AI is simply too wideand may stifle innovation due to exorbitant compliance costs.

Additionally, countries like France oppose prohibiting the use of Facial Recognition Technology, while Germany approves an all-out ban on its use in public spaces. Further deliberations and potential amendments may be the only way out of this intra-EU stalemate.

A report by the UK-based Ada Lovelace Institute further argues that the Act mistakenly conceives AI systems to be a final product. Instead, they are systems delivered dynamically through multiple hands,which means that they impact people not just at the implementation stage, but before that as well. The Act doesnt account for this life cycle of AI. Additionally, it focuses entirely on the risk-based approach, with little isolated discussion of the role played by citizens consuming these services. The report argues that this approach is incompatible with legislation concerned with Fundamental Rights. The report further describes the perceived risks of AI as arbitrary, calling for an assessment of these systems based on reviewable criteria. Finally, while the Act spends much time on reviewing the risks of prohibited and High Risk AI, it fails to review the risks of all AI services at large.

EU Member States are currently proposing changes to the Actwhether these deficiencies will be addressed, and when, remains to be seen.

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How is the healthcare sector inclining toward artificial intelligence worldwide? – The Financial Express

August 1, 2022 | Author: admin

By Dr. Shreeram Iyer

As awareness about artificial intelligence and its potential is spreading, so is the faith of various industries in its capabilities of improving production and quality of life. Artificial intelligence is greatly upgrading every point in a sector, enhancing various aspects which are crucial to their longevity. Many sectors have already adapted artificial intelligence in many creative methods, which have been very productive in improving production and supplementing manpower for greater sales and market value.

One sector that is more openly inclining towards artificial intelligence today is the healthcare sector. AI has huge potential in healthcare, as it can bring new improvements and supplement hospitals and other medical institutions, greatly reducing their workload and helping them treat a larger number of patients at one time. Healthcare can make use of machines to analyze and act on medical data, usually with the goal of predicting a particular outcome. Using patient data and other information, AI can help doctors and medical providers deliver more accurate diagnoses and treatment plans and help make healthcare more predictive and proactive by analyzing big data to develop improved preventive care recommendations for patients.

AI can assist doctors, nurses, and other healthcare workers in their daily work. AI in healthcare can enhance preventive care and quality of life, produce more accurate diagnoses and treatment plans, and lead to better patient outcomes overall. It can also predict and track the spread of infectious diseases by analyzing data from a government, healthcare, and other sources. As a result, it can play a crucial role in global public health as a tool for combatting epidemics and pandemics. Smart devices can be critical for monitoring patients in the ICU and anywhere else.

Using artificial intelligence to enhance the ability to identify deterioration or sense the development of complications can significantly improve outcomes and may reduce costs related to hospital-acquired condition penalties. Machine learning algorithms and their ability to synthesize highly complex datasets may be able to illuminate new options for targeting therapies to an individuals unique genetic makeup.

Almost all consumers now have access to devices with sensors that can collect valuable data about their health. From smartphones with step trackers to wearables that can track a heartbeat around the clock, a growing proportion of health-related data is generated on the go. Collecting and analyzing this data and supplementing it with patient-provided information through apps and other home monitoring devices can offer a unique perspective into individual and population health. Artificial intelligence will play a significant role in extracting actionable insights from this large and varied treasure trove of data.

Artificial Intelligence has enhanced the precision ofrobot-assisted surgery and made Improvements in deep learning techniques and data logs in rare diseases, helping in developing countermeasures to these diseases. Trained machines can detect any dormant ailments or illnesses within a persons body, allowing early formulation and execution of treatment plans before any complications would occur.

This can be achieved in a remote manner as well by incorporating artificial intelligence into digital consultation apps to give medical consultations based on the personal medical histories of users as well as information accessible on the internet. Users will report their symptoms onto the application, which can compare against a database of illnesses. The apps can then offer recommendations while taking into account the persons medical history. This type of technology can be utilized to diagnose and accurately assist people in nations where fewer doctors or medical facilities are available to people. With the increasing capabilities of AI over the internet, advanced machine learning algorithms can allow patients to get accurately diagnosed when they would previously have no way of knowing if they had a life-threatening disease or not.

Using AI in developing nations that do not have the resources will diminish the need for outsourcing and can improve patient care. AI can allow for not only diagnosis of a patient in areas where healthcare is scarce but also allow for a good patient experience by resourcing files to find the best treatment for a patient. The ability of AI to adjust course as it goes also allows customized treatment plans to be developed for each patient; a level of individualized care that is nearly non-existent in developing countries.

(The author is a Founder & CEO, Prisma AI. Views expressed are personal and do not reflect the official position or policy of FinancialExpress.com.)

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The Impact of AI on the Future of VPN Technology – IoT For All

August 1, 2022 | Author: admin

Artificial intelligence is no longer tied to the realm of science fiction. Machine learning is here and can be found in your pocket, car, online, and offline.Machine learning looks for patterns, and any successful guesses are logged to create the next generation of AI. This duplication process continues until you have an algorithm able to make decisions for itself. There are, however, drawbacks to such learning technology, and the most obvious downsides concern our privacy, security, and individuality.

AI can be used by the powerful for wrongful actions. It currently helps governments find new ways to censor material online. Artificial Intelligence can collect data in secret and gain access to the personal information of users worldwide.This is where Virtual Private Networks (VPN) become seemingly necessary. VPNs work by serving as a middleman to trick the host website into thinking youre physically somewhere else. This means data collectors cant get an exact read on your geographic, historic, or personal information. Once you choose your VPN protocol, you can enjoy certain anonymity in a world where this is seemingly impossible.

AI is even more beneficial for VPN technology than it is for bad actors. A study from the Journal of Cyber Security Technology revealed that AI and machine learning allow modern VPNs to achieve 90 percent accuracy. Simply put, VPNs are critical to any action or conversation involving cyber security awareness.

This is done through AI-based routing, which allows Internet users to connect to a VPN server that is closest to the destination server. This not only optimizes ping but also makes connections more secure by allowing traffic to stay within the network. It also makes the user much harder to track.Home-based networks are much more secure with AI-powered VPNs. The average security breach is just as common on a home network as it is on corporate infrastructure.

Because VPNs using AI can help counter other AI-based algorithms, they play an especially important role in dodging censorship. Censorship is increasingly common in many nations, and one of the primary uses of VPNs is tricking a host server into thinking you are somewhere else. While this usually amounts to gaining access to streaming platforms not available in certain regions, this is also important for getting outside sources for news, information, and web services. Its a small wonder that VPNs are used frequently in regions like China where national firewalls prevent even basic services from companies like Google, PayPal, or Amazon.

Despite what VPNs can offer now, AI-powered changes to VPN technology are coming. Future versions of VPNs will offer the following technologies:

When it comes to AI, VPNs are using fire to fight fire. Machine learning can help combat the AI threats online which helps boost your ability to stay secure and private.When you go online, your actions are tracked and cataloged whether you like it or not. Each piece of information seems banal on its own, but when amalgamated, your online persona becomes apparent. This is why after browsing a retail site, you will often see advertisements for that same site.

VPNs, coupled with AI, help to counter this and more. Bad actors on the internet can use these pieces of information to breach your secure documents or invade your privacy for nefarious purposes. With AI helping to plug these breaches, VPNs are more secure than ever before.

The rapid advancement of internet technology has made it easy to overlook potential threats that come with it. Security breaches average damages of over $4 million as of 2021, and its only getting worse. VPN technology, coupled with AI and machine learning, serves as an example of important security measures that internet users worldwide should start to see as a necessity.

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DALL-E Proves the Unbounded Abilities of Artificial Intelligence – Study Breaks

August 1, 2022 | Author: admin

The creative power of the human mind has often been recognized as the greatest force in art. The ability to internalize real-world circumstances and transmit thought into visual form, storytelling or music is a facet of human society that can be traced back to the beginning of recorded history. The sanctity of the human mind within the realm of art has long gone unchallenged, yet modern technology has posed some counterarguments to the assertion that sentience is required to produce creative works. Artificial intelligence, or AI, is a broad category of machine learning technology whereby computer programs are exposed to data and subsequently begin to work independently to complete tasks. One recently announced program has demonstrated abilities that are leaps and bounds beyond the limits of its contemporaries, and has unlocked the yet unforeseen power of AI-generated art.

The new program, known as DALL-E, has demonstrated that the sky is the limit for creative artificial intelligence. DALL-E was developed in 2021 by OpenAI, an artificial intelligence lab that has spent the last seven years programming applications that approximate human ability in various fields. The platform derives its name from two radically different influences: Spanish painter Salvador Dali and the lovable robotic protagonist of Pixars WALL-E. It has garnered a devoted online following for its revolutionary ability to understand complex phrases and produce unique, original computer-generated visuals based upon written sentences.

The platforms user interface is reminiscent of many search engines, with a text bar for users to input phrases that serve as instructions for generating the original images. Within 30 seconds of a user hitting enter, half a dozen rendered images appear onscreen. The content of the images varies slightly from one picture to the next, with some demonstrating a literal interpretation of the searched phrase while others explore implied meanings of the searched words. The truly remarkable ability to interpret the strings of words in several manners demonstrates an inventive level of textual understanding that feels impossibly human for an AI. The platforms website advertises many of its most impressive capabilities, such as: creating anthropomorphized versions of animals and objects, combining unrelated concepts in plausible ways, rendering text, and applying transformations to existing images. These descriptions only scratch the surface of what DALL-E is capable of, yet OpenAI has already moved beyond this first program in a quest to code something even closer to sentient life.

DALL-E was quickly followed by DALL-E 2, a similar application that performs nearly the same function but displays crisper images and has a more advanced understanding of English language syntax. Neither application is available for public use, with the latter in beta testing and made available to select online personalities to advertise its features. It is not apparent when or if the platforms will be released for general use, though it seems likely that it would exist behind a paywall should a public version be developed. The lack of general knowledge concerning the complete functionality of the program or its technical foundation has left many to speculate about what code powers the two applications, though OpenAIs website provides a wealth of knowledge about certain components of their inner workings.

Since its inception in the 1940s, digital computer technology has been able to interpret human inputs and produce a desired response, typically in the form of text. When a search engine or website is asked to display an image, such as on Google Images, it does so by retrieving an existing file that it understands to be linked with the search terms via machine learning processes. DALL-E is built upon the framework of Generative Pre-trained Transformer 3 (GPT-3), a language algorithm that learns to predict and generate sequences of text. The platform uses this coding model and expands upon it, housing its own database of reference images in a manner reminiscent of a search engine. It harnesses GPT-3 to recognize the order and significance of words and to scan multiple images that are associated with different words in a search. Once it comprehends the string of input vocabulary using these references, it can then generate an original image by combining the disparate content in the search phrase.

There are countless reasons to praise the minds behind DALL-E for concocting a creative tool that has such an elevated understanding of language and visual art, though there is also cause for concern. The art world was immediately concerned about a marketplace in which artificial intelligence can push living artists out of a job. The frenzied discourse around DALL-E is sensible for those who are concerned about their careers, though this is not the first time visual artists have been threatened by, but ultimately survived, the march of technology. Photography was also once a feared new medium, with the ease of capturing real-life imagery seemingly challenging the job security of portrait artists and impressionist painters. Though the medium could have replaced the demand for painted artworks, the classical forms of the visual arts have survived in the era of cameras because photography constituted a separate sector of the art world and was often used by painters to provide inspiration for their work. OpenAIs stated goal for developing the DALL-E programs is to assist graphic designers by giving them a tool to quickly generate reference images that can be used in several ways for further artistry. The ability to generate reference images in a rapid manner and of a style that the artist may not have considered is an incredible asset for those who learn to use it and will likely contribute more to artists than it will take away.

The impressive technology at play within DALL-E proposes another ethical dilemma. The significant difference between a sentient artist and a robotic curator is the presence of a moral compass within the former. DALL-E can render photorealistic visuals and could hypothetically be asked to depict damaging content without much participation from a user. In preparation for such circumstances, the AI refuses to generate images using some violent or explicit search terms and will also avoid producing visuals containing public figures. These decisions have pre-emptively circumvented some forms of abusing the technology, though crafty users can search precise, uncensored terms to generate imagery that approximates what the program would refuse to depict with censored terminology. It is easy to blame DALL-E for this defect, though the user is still the driving force behind any reprehensible works the application makes. Human artists have also shown tendencies to produce despicable art without the wonders of 21st-centurytechnology, as numerous propaganda artists of past centuries demonstrate. Any method of communication can be channeled for questionable aims, yet it is not sensible to blame the tool for an issue that lies squarely with its user.

Though the platforms name references Dali, it is actually worth examining the difference between the program and the painter to ease the concerns of those who find DALL-E and its successor dangerous. Salvador Dali was an eccentric abstractionist painter who was instrumental in the 20th-century shift away from impressionist painting toward postmodern art. His incredibly stylized work is instantaneously recognizable and the product of his ingenuity; his brush brought into existence contours and compositions that nobody had previously imagined. DALL-E, on the other hand, can only emulate, and its ability to create new styles or forms beyond what exists in its database of visuals is limited. The program cannot follow in Dalis footsteps and take the next quantum leap in artistic thought in the same way aspiring artists of today undoubtedly will. Whether or not it is being used to originate, emulate, or outright copy a style or form, it still requires a creative mind to take the wheel and lead it in a certain direction. DALL-E doesnt need to ring alarm bells for a war against technology, but rather, it reminds us that even when artificial intelligence progresses, we can recognize it as an extension of ourselves.

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Artificial Intelligence in Aviation Market to be Worth $9995.83 Billion by 2029 | Global Market Vision – Digital Journal

August 1, 2022 | Author: admin

The Artificial Intelligence in Aviation market will exhibit a CAGR of 46.3% for the forecast period of 2022-2029 and is likely to reach the USD 9,995.83 million by 2029.

The global Ai in aviation market is expected to witness a significant rise during the forecast period, owing to the rising usage of big data analytics in the aerospace industry. The rapidly increasing investments by the aerospace companies in towards the adoption of the cloud-based technologies and services is boosting the growth of the global AI in aviation market. The airlines industry and the airports are increasingly adopting the latest and novel technologies like artificial intelligence to improve services and smooth operations. The rising operational costs and rising need for improving the profitability is fostering the adoption of AI in the aviation industry. Airways has now become an important medium of transport across the globe and hence the rising focus on the improvement of the customer services is significantly boosting the demand for the AI in aviation industry. There has been a significant rise in the adoption of the AI based chat bots that facilitates the travelers in online ticket booking.

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The adoption of the AI and machine learning technologies are expected to enhance the air traffic control and predictive maintenance activities in the near future. The adoption of AI for observation tasks such as time series analysis, natural language processing, and computer vision. The ongoing developments and rising investments on the research activities are expected to surge the number of applications of AI in the various complex operations of the aviation industry. EHang, a China-based company and Airbus are collectively engaged in developing AI-based navigation technology. EHang uses AI in its autonomous aircrafts and Airbus has completed its first taxi, take-off and landing using the vision-based AI. Therefore, the rising focus on the adoption of the AI for performing different operations in the aviation industry is significantly boosting the growth of the global AI in aviation market.

Key Market Developments

Some of the prominent players in the global artificial intelligence in aviation market include:

Intel, NVIDIA, IBM, Micron, Samsung, Xilinx, Amazon, Microsoft, Airbus, Boeing, General Electric, Thales, Lockheed Martin, Garmin, Nvidia, GE, Pilot AI Labs, Neurala, Northrop Grumman, IRIS Automation, Kittyhawk and others

Segments Covered in the Report

By Offering

By Technology

By Application

Artificial Intelligence in Aviation Market by Region

Table of Content (TOC):

Chapter 1: Introduction and Overview

Chapter 2: Industry Cost Structure and Economic Impact

Chapter 3: Rising Trends and New Technologies with Major key players

Chapter 4:Global Artificial intelligence in aviation Market Analysis, Trends, Growth Factor

Chapter 5: Artificial intelligence in aviation Market Application and Business with Potential Analysis

Chapter 6: Global Artificial intelligence in aviation Market Segment, Type, Application

Chapter 7: Global Artificial intelligence in aviation Market Analysis (by Application, Type, End User)

Chapter 8: Major Key Vendors Analysis of Artificial intelligence in aviation Market

Chapter 9: Development Trend of Analysis

Chapter 10: Conclusion

Conclusion:At the end of Artificial intelligence in aviation Market report, all the findings and estimation are given. It also includes major drivers, and opportunities along with regional analysis. Segment analysis is also providing in terms of type and application both.

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This helps to understand the overall market and to recognize the growth opportunities in the global Artificial intelligence in aviation Market. The report also includes a detailed profile and information of all the major Artificial intelligence in aviation market players currently active in the global Artificial intelligence in aviation Market. The companies covered in the report can be evaluated on the basis of their latest developments, financial and business overview, product portfolio, key trends in the Artificial intelligence in aviation market, long-term and short-term business strategies by the companies in order to stay competitive in the Artificial intelligence in aviation market.

If you have any special requirements, please let us know and we will offer you the report at a customized price.

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Markt fr medizinische Bildanalysesoftware globales Wachstum, Trends, Hauptakteure und Prognose 2029 | AGFA-GEVAERT-GRUPPE, AQUILAB, BARCO, BRUKERGift Card Market Growing Demand, Competition, Investment Opportunities & Forecast Global Market Vision

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Nutrunner Market Analysis with Industry Trends and Growth Rate by Manufacturers, Future Plans and Size Forecast 2022-2029| ESTIC Corporation, Atlas Copco, Bosch Rexroth, Sanyo Machine Works

OPV2022| ARMOR GroupAdvent Technologies Inc.Mitsubishi ChemicalAGC

: , , , , 2022-2029 | Emerson Electric, Festo AG and Co.KG, Parker Hannifin, Bimba Manufacturing

(LPS) 2022-2029 | Abb, Dehn International, , Ecle

Der Markt fr Erdbewegungsmaschinen boomt weltweit | Atlas Copco, Hyundai Heavy Industries, Caterpillar, Bharat Erdbewegungsmaschinen

Le march des appareils ORL devrait connatre un taux de croissance important | Cochlear Limited, Medtronic, Stryker, William Demant

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Knuckleboom Loaders Market Growth Prospect and Future Scenario by Key Players Caterpillar, John Deere, Tigercat, Barko

RF| Broadcom LimitedTexas InstrumentsQorvoSkyworks Solutions Inc.

| Nikon, Carl Zeiss, Leupold Stevens, Bushnell

2022 Philips Lighting, Inventtronics, Harvard Engineering, Mean Well

Marktprofil fr Hydraulikbagger: Caterpillar, Volvo Construction Equipment, Hitachi Construction Machinery, Komatsu

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Markt fr medizinische sterile Handschuhe: Globale Branchentrends, Anteil, Gre, Wachstum, Chancen und Prognosen 2022-2029 | Ansell Healthcare, Hartalega Holdings, Supermax Corporation Berhad, Markt fr medizinische sterile Handschuhe: Globale Branchentrends, Anteil, Gre, Wachstum, Chancen und Prognosen 2022-2029 | Ansell Healthcare, Hartalega Holdings, Supermax Corporation Berhad, Kossan Rubber ProductsRubber ProductsArt Gallery Management Software Market Future Scope Analysis 2030 Global Market Vision

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Luxury Duvet Market will touch a new level in upcoming year with Norvegr Down Duvets AS, Makoti Down Products, DOWN INC, Canadian Down and Feather Company

RF2022 Broadcom LimitedTexas InstrumentsQorvoSkyworks Solutions Inc.

, , 2029 | Parker Hannifin Corporation, Vesta Automation Srl, Staiger GmbH Co.KG, Metal Work SPA

2022 | Murata Power Solutions, Red Lion Controls, Omron, Innovista

Markt fr hydraulische Raupenkrane bis 2029 mit Schlsselakteuren Kobelco, Zoomlion, Casagrande SpA, Sumitomo

March des sels enrichis: Tendances mondiales de lindustrie, part, taille, croissance, opportunit et prvisions 2022-2029 | Tata Chemicals, Sel Cargill, Compass Minerals

Der Markt fr Mikro-Rechenzentren wird voraussichtlich eine erhebliche Wachstumsrate erfahren | Rittal GmbH, Schneider Electric, Dataracks, Elliptical Mobile Solutions

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Artificial Intelligence in Aviation Market to be Worth $9995.83 Billion by 2029 | Global Market Vision - Digital Journal

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Northeastern Launches AI Ethics Board to Chart a Responsible Future in AI – Northeastern University

August 1, 2022 | Author: admin

The world of artificial intelligence is expanding, and a group of AI experts at Northeastern wants to make sure it does so responsibly.

Self-driving cars are hitting the road and others cars. Meanwhile a facial recognition program led to the false arrest of a Black man in Detroit. Although AI has the potential to alter the way we interact with the world, it is a tool made by people and brings with it their biases and limited perspectives. But Cansu Canca, founder and director of the AI Ethics Lab, believes people are also the solution to many of the ethical barriers facing AI technology.

With the AI Ethics Advisory Board, Canca, co-chair of the board and AI ethics lead of the Institute for Experiential AI at Northeastern, and a group of more than 40 experts hope to chart a responsible future for AI.

There are a lot of ethical questions that arise in developing and using AI systems, but also there are a lot of questions regarding how to answer those questions in a structured, organized manner, Canca said. Answering both of those questions requires experts, especially ethics experts and AI experts but also subject matter experts.

The board is one of the first of its kind, and although it is housed in Northeastern, it is made up of multidisciplinary experts from inside and outside the university, with expertise ranging from philosophy to user interface design.

The AI Ethics Advisory Board is meant to figure out: What is the right thing to do in developing or deploying AI systems? Canca said. This is the ethics question. But to answer it we need more than just AI and ethics knowledge.

The boards multidisciplinary approach also involves industry experts like Tamiko Eto, the research compliance, technology risk, privacy and IRB manager for healthcare provider Kaiser Permanente. Eto stressed that whether AI is utilized in healthcare or defense, the impacts need to be analyzed extensively.

The use of AI-enabled tools in healthcare and beyond requires a deep understanding of the potential consequences, Eto said. Any implementation must be evaluated in the context of bias, privacy, fairness, diversity and a variety of other factors, with input from multiple groups with context-specific expertise.

The AI Ethics Advisory Board will function as an external, objective consultant for companies that are grappling with AI ethical questions. When a company contacts the board with a request, it will determine the subject matter experts best suited to tackling that question. Those experts will form a smaller subcommittee that will be tasked with considering the question from all relevant perspectives and then resolving the case.

But the aim is not only to address the concerns of specific companies. Canca and the board members hope to answer broader questions about how AI can be implemented ethically in real-world settings.

The mindset is for truly solving questions, not just managing the question for the client but truly solving the question, and contributing to the progress of the practice Canca said. This is not a review board or a compliance board. Our approach is one, Lets figure the ethical issues and create better technologies. Lets enhance the technology with all these multidisciplinary capabilities that we have, that we can bring on board.'

Its an approach that Ricardo Baeza-Yates, co-chair of the board, director of research for the Institute for Experiential AI and professor of practice in Khoury College of Computer Science, said is necessary in order to tackle the privacy and discrimination issues that are most commonly seen in AI use. Baeza-Yates said the latter is especially concerning, since its not always a simple technical fix.

This sometimes comes from the data but also sometimes comes from the system, Baeza-Yates said. What you are trying to optimize can sometimes be the problem.

Baeza-Yates points to facial recognition programs and e-commerce AI that have profiled people of color and reinforced pre-existing biases and forms of discrimination. But the most well-known ethical problem in current AI use is the self-driving car, which Baeza-Yates likened to the trolley problem, a famous philosophical thought experiment.

We know that self-driving cars will kill less people [than human drivers], for sure, Baeza-Yates said. The problem is that we are saving a lot of people, but also we will kill some people who before were not in danger. Mostly, this will be vulnerable people, women, children, old people that, for example, didnt move so fast like the model expected or the kid moved too fast for the model to expect.

Conversations around the ethical implications of technology like the self-driving car are only starting in companies. For now, AI ethics seems very mysterious to a lot of companies, Canca said, which can lead to confusion and disinterest. With the board, Canca hopes to spark a more meaningful, engaged conversation and put an ethics-based approach at the core of how companies approach the technology moving forward.

We can help them understand the issues they are facing and figure out the problems that they need to solve through a proper knowledge exchange, Canca said. Through advising, We can help them ask the right questions and help them find novel and innovative solutions or mitigations. Companies are getting more and more interested in establishing a responsible AI practice, but its important that they do this efficiently and in a way that fits their organizational structure.

For media inquiries, please contact Shannon Nargi at s.nargi@northeastern.edu or 617-373-5718.

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Multiverse Collaborating with Bosch to Optimize Quality, Efficiency, and Performance in an Automotive Electronic Components Manufacturing Plant -…

July 30, 2022 | Author: admin

Multiverse Collaborating with Bosch to Optimize Quality, Efficiency, and Performance in an Automotive Electronic Components Manufacturing Plant

Multiverse and Bosch will be working to create a quantum computing model of the machinery and process flow in at one of Boschs manufacturing plants in a process known as digital twin. This is a technique where a model of the activities in the facility will be created inside the computer and then enable various simulations and optimizations to be performed which can predict how the plant will perform under different scenarios. The companies will be using both customized quantum and quantum inspired algorithms developed by Multiverse in order to model an automotive electronic components plants located in Madrid, Spain. The companies hope to have first results of this pilot implementation by the end of the year with a goal of finding ways to enhance quality control, improve overall efficiencies, minimize waste, and lower energy usage. Bosch has a total of 240 manufacturing plants that include over 120,000 machines and 250,000 devices which are connected together to provide them with digital control and sensing to optimize performance. So a successful implementation of this digital twin concept could be extended to many more factories and provide Bosch with a significant productivity advantage in the future. A news release from Multiverse about this collaboration can be accessed on their website here.

July 30, 2022

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Historically accurate Bitcoin metric exits buy zone in ‘unprecedented’ 2022 bear market – Cointelegraph

July 30, 2022 | Author: admin

Bitcoin (BTC) is enjoying what some are calling a "bear market rally" and has gained 20% in July, but price action is still confusing analysts.

As the July monthly close approaches, the Puell Multiple has left its bottom zone, leading to hopes that the worst of the losses may be in the past.

The Puell Multiple one of the best-known on-chain Bitcoin metrics. It measures the value of mined bitcoins on a given day compared to the value of those mined in the past 365 days.

The resulting multiple is used to determine whether a day's mined coins is particularly high or low relative to the year's average. From that, miner profitability can be inferred, along with more general conclusions about how overbought or oversold the market is.

After hitting levels which traditionally accompany macro price bottoms, the Puell Multiple is now aiming higher something traditionally seen at the start of macro price uptrends.

"Based on historical data, the breakout from this zone was accompanied by gaining bullish momentum in the price chart," Grizzly, a contributor at on-chain analytics platform CryptoQuant, wrote in one of the firm's "Quicktake" market updates on July 25.

The Multiple is not the only signal flashing green in current conditions. As Cointelegraph reported, accumulation trends among hodlers are also suggesting that the macro bottom is already in.

After its surprise relief bounce in the second half of this month, Bitcoin is now near its highest levels in six weeks and far from a new macro low.

Related:Bitcoin futures data shows 'improving' mood' despite -31% GBTC premium

As sentiment exits the "fear" zone, market watchers are pointing to unique phenomena which continue to make the 2022 bear market extremely difficult to predict with any certainty.

In another of its recent "Quicktake" research pieces, CryptoQuant noted that even price trendlines are not acting as normal this time around.

In particular, BTC/USD has crisscrossed its realized price level several times in recent weeks, something which did not occur in prior bear markets.

Realized price is the average at which the BTC supply last moved, and currently sits just below $22,000.

"The Realized Price has signaled the market bottoms in previous cycles," CryptoQuant explained.

Those conditions, as Cointelegraph reported, have come in the form of forty-year highs in inflation in the United States, rampant rate hikes by the Federal Reserve and most recently signals that the U.S. economy has entered a recession.

In addition to realized price, meanwhile, Bitcoin has formed an unusual relationship to its 200-week moving average (MA) this bear market.

While normally retaining it as support with brief dips below, BTC/USD managed to flip the 200-week MA to resistance for the first time in 2022. It currently sits at around $22,800, data from Cointelegraph Markets Pro and TradingView shows.

The views and opinions expressed here are solely those of the author and do not necessarily reflect the views of Cointelegraph.com. Every investment and trading move involves risk, you should conduct your own research when making a decision.

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New hardware offers faster computation for artificial intelligence, with much less energy – MIT News

July 30, 2022 | Author: admin

As scientists push the boundaries of machine learning, the amount of time, energy, and money required to train increasingly complex neural network models is skyrocketing. A new area of artificial intelligence called analog deep learning promises faster computation with a fraction of the energy usage.

Programmable resistors are the key building blocks in analog deep learning, just like transistors are the core elements for digital processors. By repeating arrays of programmable resistors in complex layers, researchers can create a network of analog artificial neurons and synapses that execute computations just like a digital neural network. This network can then be trained to achieve complex AI tasks like image recognition and natural language processing.

A multidisciplinary team of MIT researchers set out to push the speed limits of a type of human-made analog synapse that they had previously developed. They utilized a practical inorganic material in the fabrication process that enables their devices to run 1 million times faster than previous versions, which is also about 1 million times faster than the synapses in the human brain.

Moreover, this inorganic material also makes the resistor extremely energy-efficient. Unlike materials used in the earlier version of their device, the new material is compatible with silicon fabrication techniques. This change has enabled fabricating devices at the nanometer scale and could pave the way for integration into commercial computing hardware for deep-learning applications.

With that key insight, and the very powerful nanofabrication techniques we have at MIT.nano, we have been able to put these pieces together and demonstrate that these devices are intrinsically very fast and operate with reasonable voltages, says senior author Jess A. del Alamo, the Donner Professor in MITs Department of Electrical Engineering and Computer Science (EECS). This work has really put these devices at a point where they now look really promising for future applications.

The working mechanism of the device is electrochemical insertion of the smallest ion, the proton, into an insulating oxide to modulate its electronic conductivity. Because we are working with very thin devices, we could accelerate the motion of this ion by using a strong electric field, and push these ionic devices to the nanosecond operation regime, explains senior author Bilge Yildiz, the Breene M. Kerr Professor in the departments of Nuclear Science and Engineering and Materials Science and Engineering.

The action potential in biological cells rises and falls with a timescale of milliseconds, since the voltage difference of about 0.1 volt is constrained by the stability of water, says senior author Ju Li, the Battelle Energy Alliance Professor of Nuclear Science and Engineering and professor of materials science and engineering, Here we apply up to 10 volts across a special solid glass film of nanoscale thickness that conducts protons, without permanently damaging it. And the stronger the field, the faster the ionic devices.

These programmable resistors vastly increase the speed at which a neural network is trained, while drastically reducing the cost and energy to perform that training. This could help scientists develop deep learning models much more quickly, which could then be applied in uses like self-driving cars, fraud detection, or medical image analysis.

Once you have an analog processor, you will no longer be training networks everyone else is working on. You will be training networks with unprecedented complexities that no one else can afford to, and therefore vastly outperform them all. In other words, this is not a faster car, this is a spacecraft, adds lead author and MIT postdoc Murat Onen.

Co-authors include Frances M. Ross, the Ellen Swallow Richards Professor in the Department of Materials Science and Engineering; postdocs Nicolas Emond and Baoming Wang; and Difei Zhang, an EECS graduate student. The research is published today in Science.

Accelerating deep learning

Analog deep learning is faster and more energy-efficient than its digital counterpart for two main reasons. First, computation is performed in memory, so enormous loads of data are not transferred back and forth from memory to a processor. Analog processors also conduct operations in parallel. If the matrix size expands, an analog processor doesnt need more time to complete new operations because all computation occurs simultaneously.

The key element of MITs new analog processor technology is known as a protonic programmable resistor. These resistors, which are measured in nanometers (one nanometer is one billionth of a meter), are arranged in an array, like a chess board.

In the human brain, learning happens due to the strengthening and weakening of connections between neurons, called synapses. Deep neural networks have long adopted this strategy, where the network weights are programmed through training algorithms. In the case of this new processor, increasing and decreasing the electrical conductance of protonic resistors enables analog machine learning.

The conductance is controlled by the movement of protons. To increase the conductance, more protons are pushed into a channel in the resistor, while to decrease conductance protons are taken out. This is accomplished using an electrolyte (similar to that of a battery) that conducts protons but blocks electrons.

To develop a super-fast and highly energy efficient programmable protonic resistor, the researchers looked to different materials for the electrolyte. While other devices used organic compounds, Onen focused on inorganic phosphosilicate glass (PSG).

PSG is basically silicon dioxide, which is the powdery desiccant material found in tiny bags that come in the box with new furniture to remove moisture. It is studied as a proton conductor under humidified conditions for fuel cells. It is also the most well-known oxide used in silicon processing. To make PSG, a tiny bit of phosphorus is added to the silicon to give it special characteristics for proton conduction.

Onen hypothesized that an optimized PSG could have a high proton conductivity at room temperature without the need for water, which would make it an ideal solid electrolyte for this application. He was right.

Surprising speed

PSG enables ultrafast proton movement because it contains a multitude of nanometer-sized pores whose surfaces provide paths for proton diffusion. It can also withstand very strong, pulsed electric fields. This is critical, Onen explains, because applying more voltage to the device enables protons to move at blinding speeds.

The speed certainly was surprising. Normally, we would not apply such extreme fields across devices, in order to not turn them into ash. But instead, protons ended up shuttling at immense speeds across the device stack, specifically a million times faster compared to what we had before. And this movement doesnt damage anything, thanks to the small size and low mass of protons. It is almost like teleporting, he says.

The nanosecond timescale means we are close to the ballistic or even quantum tunneling regime for the proton, under such an extreme field, adds Li.

Because the protons dont damage the material, the resistor can run for millions of cycles without breaking down. This new electrolyte enabled a programmable protonic resistor that is a million times faster than their previous device and can operate effectively at room temperature, which is important for incorporating it into computing hardware.

Thanks to the insulating properties of PSG, almost no electric current passes through the material as protons move. This makes the device extremely energy efficient, Onen adds.

Now that they have demonstrated the effectiveness of these programmable resistors, the researchers plan to reengineer them for high-volume manufacturing, says del Alamo. Then they can study the properties of resistor arrays and scale them up so they can be embedded into systems.

At the same time, they plan to study the materials to remove bottlenecks that limit the voltage that is required to efficiently transfer the protons to, through, and from the electrolyte.

Another exciting direction that these ionic devices can enable is energy-efficient hardware to emulate the neural circuits and synaptic plasticity rules that are deduced in neuroscience, beyond analog deep neural networks. We have already started such a collaboration with neuroscience, supported by the MIT Quest for Intelligence, adds Yildiz.

The collaboration that we have is going to be essential to innovate in the future. The path forward is still going to be very challenging, but at the same time it is very exciting, del Alamo says.

Intercalation reactions such as those found in lithium-ion batteries have been explored extensively for memory devices. This work demonstrates that proton-based memory devices deliver impressive and surprising switching speed and endurance, says William Chueh, associate professor of materials science and engineering at Stanford University, who was not involved with this research. It lays the foundation for a new class of memory devices for powering deep learning algorithms.

This work demonstrates a significant breakthrough in biologically inspired resistive-memory devices. These all-solid-state protonic devices are based on exquisite atomic-scale control of protons, similar to biological synapses but at orders of magnitude faster rates, says Elizabeth Dickey, the Teddy & Wilton Hawkins Distinguished Professor and head of the Department of Materials Science and Engineering at Carnegie Mellon University, who was not involved with this work. I commend the interdisciplinary MIT team for this exciting development, which will enable future-generation computational devices.

This research is funded, in part, by the MIT-IBM Watson AI Lab.

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