Category Archives: Artificial Intelligence
Can artificial intelligence really help us talk to the animals? – The Guardian
A dolphin handler makes the signal for together with her hands, followed by create. The two trained dolphins disappear underwater, exchange sounds and then emerge, flip on to their backs and lift their tails. They have devised a new trick of their own and performed it in tandem, just as requested. It doesnt prove that theres language, says Aza Raskin. But it certainly makes a lot of sense that, if they had access to a rich, symbolic way of communicating, that would make this task much easier.
Raskin is the co-founder and president of Earth Species Project (ESP), a California non-profit group with a bold ambition: to decode non-human communication using a form of artificial intelligence (AI) called machine learning, and make all the knowhow publicly available, thereby deepening our connection with other living species and helping to protect them. A 1970 album of whale song galvanised the movement that led to commercial whaling being banned. What could a Google Translate for the animal kingdom spawn?
The organisation, founded in 2017 with the help of major donors such as LinkedIn co-founder Reid Hoffman, published its first scientific paper last December. The goal is to unlock communication within our lifetimes. The end we are working towards is, can we decode animal communication, discover non-human language, says Raskin. Along the way and equally important is that we are developing technology that supports biologists and conservation now.
Understanding animal vocalisations has long been the subject of human fascination and study. Various primates give alarm calls that differ according to predator; dolphins address one another with signature whistles; and some songbirds can take elements of their calls and rearrange them to communicate different messages. But most experts stop short of calling it a language, as no animal communication meets all the criteria.
Until recently, decoding has mostly relied on painstaking observation. But interest has burgeoned in applying machine learning to deal with the huge amounts of data that can now be collected by modern animal-borne sensors. People are starting to use it, says Elodie Briefer, an associate professor at the University of Copenhagen who studies vocal communication in mammals and birds. But we dont really understand yet how much we can do.
Briefer co-developed an algorithm that analyses pig grunts to tell whether the animal is experiencing a positive or negative emotion. Another, called DeepSqueak, judges whether rodents are in a stressed state based on their ultrasonic calls. A further initiative Project CETI (which stands for the Cetacean Translation Initiative) plans to use machine learning to translate the communication of sperm whales.
Yet ESP says its approach is different, because it is not focused on decoding the communication of one species, but all of them. While Raskin acknowledges there will be a higher likelihood of rich, symbolic communication among social animals for example primates, whales and dolphins the goal is to develop tools that could be applied to the entire animal kingdom. Were species agnostic, says Raskin. The tools we develop can work across all of biology, from worms to whales.
The motivating intuition for ESP, says Raskin, is work that has shown that machine learning can be used to translate between different, sometimes distant human languages without the need for any prior knowledge.
This process starts with the development of an algorithm to represent words in a physical space. In this many-dimensional geometric representation, the distance and direction between points (words) describes how they meaningfully relate to each other (their semantic relationship). For example, king has a relationship to man with the same distance and direction that woman has to queen. (The mapping is not done by knowing what the words mean but by looking, for example, at how often they occur near each other.)
It was later noticed that these shapes are similar for different languages. And then, in 2017, two groups of researchers working independently found a technique that made it possible to achieve translation by aligning the shapes. To get from English to Urdu, align their shapes and find the point in Urdu closest to the words point in English. You can translate most words decently well, says Raskin.
ESPs aspiration is to create these kinds of representations of animal communication working on both individual species and many species at once and then explore questions such as whether there is overlap with the universal human shape. We dont know how animals experience the world, says Raskin, but there are emotions, for example grief and joy, it seems some share with us and may well communicate about with others in their species. I dont know which will be the more incredible the parts where the shapes overlap and we can directly communicate or translate, or the parts where we cant.
He adds that animals dont only communicate vocally. Bees, for example, let others know of a flowers location via a waggle dance. There will be a need to translate across different modes of communication too.
The goal is like going to the moon, acknowledges Raskin, but the idea also isnt to get there all at once. Rather, ESPs roadmap involves solving a series of smaller problems necessary for the bigger picture to be realised. This should see the development of general tools that can help researchers trying to apply AI to unlock the secrets of species under study.
For example, ESP recently published a paper (and shared its code) on the so called cocktail party problem in animal communication, in which it is difficult to discern which individual in a group of the same animals is vocalising in a noisy social environment.
To our knowledge, no one has done this end-to-end detangling [of animal sound] before, says Raskin. The AI-based model developed by ESP, which was tried on dolphin signature whistles, macaque coo calls and bat vocalisations, worked best when the calls came from individuals that the model had been trained on; but with larger datasets it was able to disentangle mixtures of calls from animals not in the training cohort.
Another project involves using AI to generate novel animal calls, with humpback whales as a test species. The novel calls made by splitting vocalisations into micro-phonemes (distinct units of sound lasting a hundredth of a second) and using a language model to speak something whale-like can then be played back to the animals to see how they respond. If the AI can identify what makes a random change versus a semantically meaningful one, it brings us closer to meaningful communication, explains Raskin. It is having the AI speak the language, even though we dont know what it means yet.
A further project aims to develop an algorithm that ascertains how many call types a species has at its command by applying self-supervised machine learning, which does not require any labelling of data by human experts to learn patterns. In an early test case, it will mine audio recordings made by a team led by Christian Rutz, a professor of biology at the University of St Andrews, to produce an inventory of the vocal repertoire of the Hawaiian crow a species that, Rutz discovered, has the ability to make and use tools for foraging and is believed to have a significantly more complex set of vocalisations than other crow species.
Rutz is particularly excited about the projects conservation value. The Hawaiian crow is critically endangered and only exists in captivity, where it is being bred for reintroduction to the wild. It is hoped that, by taking recordings made at different times, it will be possible to track whether the speciess call repertoire is being eroded in captivity specific alarm calls may have been lost, for example which could have consequences for its reintroduction; that loss might be addressed with intervention. It could produce a step change in our ability to help these birds come back from the brink, says Rutz, adding that detecting and classifying the calls manually would be labour intensive and error prone.
Meanwhile, another project seeks to understand automatically the functional meanings of vocalisations. It is being pursued with the laboratory of Ari Friedlaender, a professor of ocean sciences at the University of California, Santa Cruz. The lab studies how wild marine mammals, which are difficult to observe directly, behave underwater and runs one of the worlds largest tagging programmes. Small electronic biologging devices attached to the animals capture their location, type of motion and even what they see (the devices can incorporate video cameras). The lab also has data from strategically placed sound recorders in the ocean.
ESP aims to first apply self-supervised machine learning to the tag data to automatically gauge what an animal is doing (for example whether it is feeding, resting, travelling or socialising) and then add the audio data to see whether functional meaning can be given to calls tied to that behaviour. (Playback experiments could then be used to validate any findings, along with calls that have been decoded previously.) This technique will be applied to humpback whale data initially the lab has tagged several animals in the same group so it is possible to see how signals are given and received. Friedlaender says he was hitting the ceiling in terms of what currently available tools could tease out of the data. Our hope is that the work ESP can do will provide new insights, he says.
But not everyone is as gung ho about the power of AI to achieve such grand aims. Robert Seyfarth is a professor emeritus of psychology at University of Pennsylvania who has studied social behaviour and vocal communication in primates in their natural habitat for more than 40 years. While he believes machine learning can be useful for some problems, such as identifying an animals vocal repertoire, there are other areas, including the discovery of the meaning and function of vocalisations, where he is sceptical it will add much.
The problem, he explains, is that while many animals can have sophisticated, complex societies, they have a much smaller repertoire of sounds than humans. The result is that the exact same sound can be used to mean different things in different contexts and it is only by studying the context who the individual calling is, how are they related to others, where they fall in the hierarchy, who they have interacted with that meaning can hope to be established. I just think these AI methods are insufficient, says Seyfarth. Youve got to go out there and watch the animals.
There is also doubt about the concept that the shape of animal communication will overlap in a meaningful way with human communication. Applying computer-based analyses to human language, with which we are so intimately familiar, is one thing, says Seyfarth. But it can be quite different doing it to other species. It is an exciting idea, but it is a big stretch, says Kevin Coffey, a neuroscientist at the University of Washington who co-created the DeepSqueak algorithm.
Raskin acknowledges that AI alone may not be enough to unlock communication with other species. But he refers to research that has shown many species communicate in ways more complex than humans have ever imagined. The stumbling blocks have been our ability to gather sufficient data and analyse it at scale, and our own limited perception. These are the tools that let us take off the human glasses and understand entire communication systems, he says.
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Can artificial intelligence really help us talk to the animals? - The Guardian
Artificial Intelligence Has a ‘Last Mile’ Problem, and Machine Learning Operations Can Solve It – Built In
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.
Read More on Built Ins Expert Contributors NetworkRage Against the Machine Learning: My War With Recommendation Engines
Artificial Intelligence Act in the European Union (EU): Risks and regulations – MediaNama.com
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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Artificial Intelligence Act in the European Union (EU): Risks and regulations - MediaNama.com
How is the healthcare sector inclining toward artificial intelligence worldwide? – The Financial Express
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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How is the healthcare sector inclining toward artificial intelligence worldwide? - The Financial Express
The Impact of AI on the Future of VPN Technology – IoT For All
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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The Impact of AI on the Future of VPN Technology - IoT For All
Artificial Intelligence in Aviation Market to be Worth $9995.83 Billion by 2029 | Global Market Vision – Digital Journal
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.
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Artificial Intelligence in Aviation Market to be Worth $9995.83 Billion by 2029 | Global Market Vision - Digital Journal
DALL-E Proves the Unbounded Abilities of Artificial Intelligence – Study Breaks
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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DALL-E Proves the Unbounded Abilities of Artificial Intelligence - Study Breaks
Northeastern Launches AI Ethics Board to Chart a Responsible Future in AI – Northeastern University
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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Northeastern Launches AI Ethics Board to Chart a Responsible Future in AI - Northeastern University
Artificial Intelligence in Healthcare Market worth $67.4 billion by 2027 – Exclusive Report by MarketsandMarkets – PR Newswire UK
Browse in-depth TOC on"AI in healthcare Market"
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The services segment is projected to foresee highest CAGR during the forecast period
AI is a complex method as it requires the implementation of sophisticated algorithms for a wide range of applications in patient data and risk analysis, lifestyle management and monitoring, precision medicine, inpatient care and hospital management, medical imaging and diagnostics, drug discovery, and virtual assistants, among others. Hence, for the successful deployment of AI, there is a need for deployment and integration, and support and maintenance services. Big technology companies such as Microsoft (US), and Google (US) are providing cloud services for AI in healthcare applications.
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Machine learning technology to hold largest size of AIin healthcare market during the forecast period
ML is being implemented in healthcare to deal with large volumes of data, where the time previously dedicated to poring over charts and spreadsheets is now being used to seek intelligent ways to automate data analysis. It is used to streamline administrative processes in hospitals, map and treat infectious diseases, and personalize medical treatments. Machine learning includes various technologies, such as deep learning, supervised learning, unsupervised learning, and reinforcement learning.
The key players operating in the artificial intelligence in healthcare market
Europe region is expected to create high market opportunity in artificial intelligence in healthcare market during the forecast period.
The major factors driving the growth of the market in the region include the surging adoption of AI-based tools in R&D for drug discovery, favorable government initiatives to encourage technological developments in the field of AI and robotics, growing EMR adoption leading to the generation of large volumes of patient data, increasing venture capital funding, rising healthcare expenditure, and growing geriatric population.
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Artificial Intelligence in Healthcare Market worth $67.4 billion by 2027 - Exclusive Report by MarketsandMarkets - PR Newswire UK
What is Artificial Intelligence? Guide to AI | eWEEK – eWeek
By any measure, artificial intelligence (AI) has become big business.
According to Gartner, customers worldwide will spend $62.5 billion on AI software in 2022. And it notes that 48 percent of CIOs have either already deployed some sort of AI software or plan to do so within the next twelve months.
All that spending has attracted a huge crop of startups focused on AI-based products. CB Insights reported that AI funding hit $15.1 billion in the first quarter of 2022 alone. And that came right after a quarter that saw investors pour $17.1 billion into AI startups. Given that data drives AI, its no surprise that related fields like data analytics, machine learning and business intelligence are all seeing rapid growth.
But what exactly is artificial intelligence? And why has it become such an important and lucrative part of the technology industry?
Also see: Top AI Software
In some ways, artificial intelligence is the opposite of natural intelligence. If living creatures can be said to be born with natural intelligence, man-made machines can be said to possess artificial intelligence. So from a certain point of view, any thinking machine has artificial intelligence.
And in fact, one of the early pioneers of AI, John McCarthy, defined artificial intelligence as the science and engineering of making intelligent machines.
In practice, however, computer scientists use the term artificial intelligence to refer to machines doing the kind of thinking that humans have taken to a very high level.
Computers are very good at making calculations at taking inputs, manipulating them, and generating outputs as a result. But in the past they have not been capable of other types of work that humans excel at, such as understanding and generating language, identifying objects by sight, creating art, or learning from past experience.
But thats all changing.
Today, many computer systems have the ability to communicate with humans using ordinary speech. They can recognize faces and other objects. They use machine learning techniques, especially deep learning, in ways that allow them to learn from the past and make predictions about the future.
So how did we get here?
Also see: How AI is Altering Software Development with AI-Augmentation
Many people trace the history of artificial intelligence back to 1950, when Alan Turing published Computing Machinery and Intelligence. Turings essay began, I propose to consider the question, Can machines think?' It then laid out a scenario that came to be known as a Turing Test. Turing proposed that a computer could be considered intelligent if a person could not distinguish the machine from a human being.
In 1956, John McCarthy and Marvin Minsky hosted the first artificial intelligence conference, the Dartmouth Summer Research Project on Artificial Intelligence (DSRPAI). It convinced computer scientists that artificial intelligence was an achievable goal, setting the foundation for several decades of further research. And early forays into AI technology developed bots that could play checkers and chess.
The 1960s saw the development of robots and several problem-solving programs. One notable highlight was the creation of ELIZA, a program that simulated psychotherapy and provided an early example of human-machine communication.
In the 1970s and 80s, AI development continued but at a slower pace. The field of robotics in particular saw significant advances, such as robots that could see and walk. And Mercedes-Benz introduced the first (extremely limited) autonomous vehicle. However, government funding for AI research decreased dramatically, leading to a period some refer to as the AI winter.
Interest in AI surged again in the 1990s. The Artificial Linguistic Internet Computer Entity (ALICE) chatbot demonstrated that natural language processing could lead to human-computer communication that felt far more natural than what had been possible with ELIZA. The decade also saw a surge in analytic techniques that would form the basis of later AI development, as well as the development of the first recurrent neural network architecture. This was also the decade when IBM rolled out its Deep Blue chess AI, the first to win against the current world champion.
The first decade of the 2000s saw rapid innovation in robotics. The first Roombas began vacuuming rugs, and robots launched by NASA explored Mars. Closer to home, Google was working on a driverless car.
The years since 2010 have been marked by unprecedented increases in AI technology. Both hardware and software developed to a point where object recognition, natural language processing, and voice assistants became possible. IBMs Watson won Jeopardy. Siri, Alexa, and Cortana came into being, and chatbots became a fixture of modern retail. Google DeepMinds AlphaGo beat human Go champions. And enterprises in all industries have begun deploying AI tools to help them analyze their data and become more successful.
Now AI is truly beginning to evolve past some of the narrow and limited types into more advanced implementations.
Also see:The History of Artificial Intelligence
Different groups of computer scientists have proposed different ways of classifying the types of AI. One popular classification uses three categories:
Another popular classification uses four different categories:
While these classifications are interesting from a theoretical standpoint, most organizations are far more interested in what they can do with AI. And that brings us to the aspect of AI that is generating a lot of revenue the AI use cases.
Also see: Three Ways to Get Started with AI
The possible AI use cases and applications for artificial intelligence are limitless. Some of todays most common AI use cases include the following:
Of course, these are just some of the more widely known use cases for AI. The technology is seeping into daily life in so many ways that we often arent fully aware of them.
Also see: Best Machine Learning Platforms
So where is the future of AI? Clearly it is reshaping consumer and business markets.
The technology that powers AI continues to progress at a steady rate. Future advances like quantum computing may eventually enable major new innovations, but for the near term, it seems likely that the technology itself will continue along a predictable path of constant improvement.
Whats less clear is how humans will adapt to AI. This question poses questions that loom large over human life in the decades ahead.
Many early AI implementations have run into major challenges. In some cases, the data used to train models has allowed bias to infect AI systems, rendering them unusable.
In many other cases, business have not seen the financial results they hoped for after deploying AI. The technology may be mature, but the business processes surrounding it are not.
The AI software market is picking up speed, but its long-term trajectory will depend on enterprises advancing their AI maturity, said Alys Woodward, senior research director at Gartner.
Successful AI business outcomes will depend on the careful selection of use cases, Woodware added. Use cases that deliver significant business value, yet can be scaled to reduce risk, are critical to demonstrate the impact of AI investment to business stakeholders.
Organizations are turning to approaches like AIOps to help them better manage their AI deployments. And they are increasingly looking for human-centered AI that harnesses artificial intelligence to augment rather than to replace human workers.
In a very real sense, the future of AI may be more about people than about machines.
Also see: The Future of Artificial Intelligence
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What is Artificial Intelligence? Guide to AI | eWEEK - eWeek