Apples acquisition of the London-based company AI Musicmade headlines recently in the world of business, as well as artificial intelligence (AI.)For years, the company has been using artificial intelligence and music to develop next-level customized playlists for listeners. The interface between music and artificial intelligence that AI Music has to offer may provide a significant boost to Apples presence within the commercial music industry, and could even help it outperform its competition within the music app arms race.

The relationship between music and artificial intelligence spans severaldecades, originating in 1960 when Russian researcher R. Kh. Zaripov published the first algorithmic music that was composed on the Ural-1 computer. Since then, advancements in the AI systems have allowed it to show real promise for music composition, as in 1997, an AI program called Experiments in Music Intelligence (EMI) seemed to outperform a human composer when composing a piece imitating the style of Bach. Only last year did an artificial intelligence program help to finish Ludwig Beethovens last symphony, using his other compositions as data to make the piece sound similar to the rest of his works.

Currently, there are many universities studying artificial intelligence and music, including Carnegie Mellon University, Princeton University, and Queen Mary University in London. All of these universities use different AI programs, but they all study the real-time composition and performance of music created by AI. Studying this process can give insights into the science of musical composition, as well as the psychological effects of music on our brains.

Artificial intelligence not only creates impressive music but can also help create fresh and engaging music playlists for listeners. Because artificial intelligence works by using old data sets to predict new outcomes, it can track a users listening preferences and create a customized playlist based on this data. This can encourage longer listener usage as well as better overall engagement, which could give the Apple Music app the success its looking for.

Apple already was looking into bolstering its music platform when it previously acquired the music streaming company Primephonic. Now with AI Music, Apple may be working to use this new technology to boost its current audio products including Apple Music, HomePool Mini, or even the Apple Fitness+ app.

Because Apple offers a music and podcasting platform, it is in direct competition with other companies offering similar products, such as Spotify or Pandora. Currently, Spotify has 365 million active monthly users, of which over 50% pay for Spotify Premium. In contrast, Apple has only 98 million subscribers as of 2021. According to one expert, Apple music seems to have more subscribers in the U.S while Spotify has more listeners in Europe and South America. As Apple has fewer listeners overall, it may be hoping to leverage this new acquisition, and the power of artificial intelligence to win the music apps arms race. It will be interesting to see how the other companies respond to Apples new acquisition, or whether AI continues to become a larger part of this industry.

Kenna Castleberry is a staff writer at the Debrief and the Science Communicator at JILA (a partnership between the University of Colorado Boulder and NIST). She focuses on deep tech, the metaverse, and quantum technology. You can find more of her work at her website: https://kennacastleberry.com/

Continued here:
Apple is Using Artificial Intelligence and Music to Win the Music App Arms Race - The Debrief

Pawan Gupta explains the need of leveraging artificial intelligence to reduce wastage

Artificial Intelligence is a phrase that has been around for many years now. It started as fiction in movies and popular literature and steadily became a trendy word to describe intelligent machines. But today its becoming indispensable, crossing over from fiction to fact, across industries.

The pandemic had a lot to do with this rising popularity and usefulness. At least 40% of active fashion consumers today are already availing of online services even as you read these words, increasing opportunities for deploying AI and deriving benefits. Without mistake, it can be said that we are in the midst of the fourth industrial revolution, where brands are gearing up to embrace the benefits of Artificial Intelligence and other new-age technologies.

Artificial Intelligence is used in different ways in the fashion industry. The industry is faced with several challenges, sustainability being the top on the list. But considering how widespread and popular fast-fashion trends are, change is a long-term endeavor. However, with the help of Artificial Intelligence, accurate predictions of trends, understanding customer preferences, managing workflows, and efficient supply chain are now reducing overproduction and minimizing wastage.

Tackling overproduction and overstocking has become a necessary concern among producers globally. United Nations estimated that the global fashion industry losing at least $500 billion annually due to a lack of and widespread use of recycling practices. Improper clothing disposal is also adding much to the loss.

Overproduction of overstocking is common among producers, especially due to a lack of Data Intelligence tools. The balance sheet of demand and supply is often off balance because proper assessment of trends and demands is either not done or is not done accurately or efficiently. To identify a few factors which create surplus stocks:

The answer to the above challenges can well be found in the use of Artificial Intelligence. AI tools help in market research and fetch real-time information. The data gathered can be aptly assimilated into a real-time prediction system, in which case inventory management can become both manageable and sans waste. The data collected and stored can also be used for future references.

Customer behaviour prediction is another important contribution of AI to combat waste in the clothing industry. Customer behaviour is always changing, and one can never rely on any one-time data when it comes to this. Hence AI can be integrated into the retailers planning policy as the data analyzed can be used to adapt to the latest demand patterns.

Demand patterns can be predicted with the help of algorithms. It can even start on any social media platform. The algorithm gives accurate data about the market and hence can drive the retailers to make the correct decisions about how much to produce. This is what we now call smart demand forecasting or smart demand prediction. Customers purchasing history can also be traced with the help of AI and can help the retailers to cater exactly to their demands. This may lead to lesser returns and help the customers make the right decisions.

Artificial Intelligence has become a part of the fashion industry in a way no one had previously predicted. We are denizens of a virtual world now and AI tools are constantly transforming the way we are manufacturing and marketing the products. Future predictions about robots being used for cutting and sewing are already in place. With the use of AI and new-age technologies, we can expect a reduction in wastage by about 60 to 70% as the processes will be automated with the highest possible accuracy.

Pawan Gupta, CEO & Co-founder at Fashinza

Share This ArticleDo the sharing thingy

About AuthorMore info about author

Analytics Insight is an influential platform dedicated to insights, trends, and opinions from the world of data-driven technologies. It monitors developments, recognition, and achievements made by Artificial Intelligence, Big Data and Analytics companies across the globe.

Read the rest here:
How AI is Helping Minimize Waste in the Clothing Industry - Analytics Insight

A team of experts in artificial intelligence and animal ecology have put forth a new, cross-disciplinary approach intended to enhance research on wildlife species and make more effective use of the vast amounts of data now being collected thanks to new technology, as announced in a press release by cole Polytechnique Fdrale de Lausanne (EPFL), a Swiss technology institute, which contributed to the study. The results were published in Nature Communications.

The field of animal ecology has entered the era of big data and the Internet of Things. Unprecedented amounts of data are now being collected on wildlife populations, thanks to sophisticated technology such as satellites, drones and terrestrial devices like automatic cameras and sensors placed on animals or in their surroundings. These data have become so easy to acquire and share that they have shortened distances and time requirements for researchers while minimizing the disrupting presence of humans in natural habitats. Today, a variety of AI programs are available to analyze large datasets, but theyre often general in nature and ill-suited to observing the exact behavior and appearance of wild animals. A team of scientists from EPFL and other universities has outlined a pioneering approach to resolve that problem and develop more accurate models by combining advances in computer vision with the expertise of ecologists. Their findings open up new perspectives on the use of AI to help preserve wildlife species.

Wildlife research has gone from local to global. Modern technology now offers revolutionary new ways to produce more accurate estimates of wildlife populations, better understand animal behavior, combat poaching and halt the decline in biodiversity. Ecologists can use AI, and more specifically computer vision, to extract key features from images, videos and other visual forms of data in order to quickly classify wildlife species, count individual animals, and glean certain information, using large datasets. The generic programs currently used to process such data often work like black boxes and dont leverage the full scope of existing knowledge about the animal kingdom. Whats more, theyre hard to customize, sometimes suffer from poor quality control, and are potentially subject to ethical issues related to the use of sensitive data. They also contain several biases, especially regional ones; for example, if all the data used to train a given program were collected in Europe, the program might not be suitable for other world regions.

We wanted to get more researchers interested in this topic and pool their efforts so as to move forward in this emerging field. AI can serve as a key catalyst in wildlife research and environmental protection more broadly, says Prof. Devis Tuia, the head of EPFLs Environmental Computational Science and Earth Observation Laboratory and the studys lead author. If computer scientists want to reduce the margin of error of an AI program thats been trained to recognize a given species, for example, they need to be able to draw on the knowledge of animal ecologists. These experts can specify which characteristics should be factored into the program, such as whether a species can survive at a given latitude, whether its crucial for the survival of another species (such as through a predator-prey relationship) or whether the species physiology changes over its lifetime. We used this approach to improve a bear-recognition program a few years ago, says Prof. Mackenzie Mathis, a neuroscientist at EPFL and co-author of the study. A researcher studying bear DNA had installed automatic cameras in bear habitats in order to recognize individual animals. But bears shed half of their body fat when they hibernate, meaning the generic programs she used were no longer able to recognize the bears once the season changed. We therefore added criteria to the program that can not only look at whether an animal has a given characteristic but also be tweaked manually to allow for possible deviations.

The idea of forging stronger ties between computer vision and ecology came up as Tuia, Mathis and others discussed their research challenges at various conferences over the past two years. They saw that such collaboration could be extremely useful in preventing certain wildlife species from going extinct. A handful of initiatives have already been rolled out in this direction; some of them are listed in theNature Communicationsarticle. For instance, Tuia and his team at EPFL have developed a program that can recognize animal species based on drone images. It was tested recently on a seal population. Meanwhile, Mathis and her colleagues have unveiled an open-source software package called DeepLabCut that allows scientists to estimate and track animal poses with remarkable accuracy. Its already been downloaded 300,000 times. DeepLabCut was designed for lab animals but can be used for other species as well. Researchers at other universities have developed programs too, but its hard for them to share their discoveries since no real community has yet been formed in this area. Other scientists often dont know these programs exist or which one would be best for their specific research.

That said, initial steps towards such a community have been taken through various online forums. TheNature Communicationsarticle aims for a broader audience, however, consisting of researchers from around the world. A community is steadily taking shape, says Tuia. So far weve used word of mouth to build up an initial network. We first started two years ago with the people who are now the articles other lead authors: Benjamin Kellenberger, also at EPFL; Sara Beery at Caltech in the US; and Blair Costelloe at the Max Planck Institute in Germany.

Also interesting:Artificial Intelligence identifies gene signature of COVID-19Artificial intelligence for the care of green zones

Excerpt from:
Artificial intelligence and big data can help preserve wildlife - Innovation Origins

In April last year, the European Commission published its ambitious proposal to regulate Artificial Intelligence. The regulation was meant to be the first of its kind, but the progress has been slow so far due to the file's technical, political and juridical complexity.

Meanwhile, the EU lost its first-mover advantage as other jurisdictions like China and Brazil have managed to pass their legislation first. As the proposal is entering a crucial year, it is high time to take stock of the state of play, the ongoing policy discussions, notably around data, and potential implications for businesses.

For the European Parliament, delays have been mainly due to more than six months of political disputes between lawmakers over who was to take the lead in the file. The result was a co-lead between the centrists and the center-left, sidelining the conservative European People's Party.

Members of European Parliament are now trying to make up for lost time. The first draft of the report is planned for April, with discussions on amendments throughout the summer. The intention is to reach a compromise by September and hold the final vote in November.

The timeline seems particularly ambitious since co-leads involve double the number of people, inevitably slowing down the process. The question will be to what extent the co-rapporteurs will remain aligned on the critical political issues as the center-right will try to lure the liberals into more business-friendly rules.

Meanwhile, the EU Council made some progress on the file, however, limited by its highly technical nature. It is telling that even national governments, which have significantly more resources than MEPs, struggle to understand the new rules' full implications.

Slovenia, which led the diplomatic talks for the second half of 2021, aimed to develop a compromise for 15 articles, but only covered the first seven. With the beginning of the French presidency in January, the file is expected to move faster as Paris aims to provide a full compromise by April.

As the policy discussions made some progress in the EU Council, several sticking points emerged. The very definition of AI systems is problematic, as European governments distinguish them from traditional software programs or statistical methods.

The diplomats also added a new category for "general purpose" AI, such as synthetic data packages or language models. However, there is still no clear understanding of whether the responsibility should be attributed upstream, to the producer, or downstream, to the provider.

The use of real-time biometric recognition systems has primarily monopolized the public debate, as the commission's proposal falls short of a total ban for some crucial exceptions, notably terrorist attacks and kidnapping. In October, lawmakers adopted a resolution pushing for a complete ban, echoing the argument made by civil society that these exceptions provide a dangerous slippery slope.

By contrast, facial recognition technologies are increasingly common in Europe. A majority of member states wants to keep or even expand the exceptions to border control, with Germany so far relatively isolated in calling for a total ban.

"The European Commission did propose a set of criteria for updating the list of high-risk applications. However, it did not provide a justification for the existing list, which might mean that any update might be extremely difficult to justify," Lilian Edwards, a professor at Newcastle University, said.

Put differently, since the reasoning behind the lists of prohibited or high-risk AI uses are largely value-based, they are likely to remain heatedly debated points point through the whole legislative process.

For instance, the Future of Life Institute has been arguing for a broader definition of manipulation, which might profoundly impact the advertising sector and the way online platforms currently operate.

A dividing line that is likely to emerge systematically in the debate is the tension between the innovation needs of the industry, as some member states already stressed, and ensuring consumer protection in the broadest sense, including the use of personal data.

This underlying tension is best illustrated in the ongoing discussion for the report of the parliamentary committee on Artificial Intelligence in a Digital Age, which are progressing in parallel to the AI Act.

In his initial draft, conservative MEP Axel Voss attacked the General Data Protection Regulation, presenting AI as part of a technological race where Europe risks becoming China's "economic colony" if it did not relax its privacy rules.

The report faced backlash from left-to-center policymakers, who saw it as an attempt to water down the EU's hard-fought data protection law. For progressive MEPs, data-hungry algorithms fed with vast amounts of personal data might not be desirable, and they draw a parallel with their activism in trying to curb personalized advertising.

"Which algorithms do we train with vast amounts of personal data? Likely those that automatically classify, profile or identify people based on their personal details often with huge consequences and risks of discrimination or even manipulation. Do we really want to be using those, let alone 'leading' their development?" MEP Kim van Sparrentak said.

However, the need to find a balance with data protection has also been underlined by Bojana Bellamy, president of the Centre for Information Policy Leadership, who notes how some fundamental principles of the GDPR would be in contradiction with the AI regulation.

In particular, a core principle of the GDPR is data minimization, namely that only the personal data strictly needed for completing a specific task is processed and should not be retained for longer than necessary. Conversely, the more AI-powered tools receive data, the more robust and accurate they become, leading (at least in theory) to a fairer and non-biased outcome.

For Bojana, this tension is due to a lack of a holistic strategy in the EU's hectic digital agenda, arguing that policymakers should follow a more result-oriented approach to what they are trying to achieve. These contradicting notions might fall on the industry practitioners, which might be requested to square a fair and unbiased system while also minimizing the amount of personal data collected.

The draft AI law includes a series of obligations for system providers, namely the organizations that make the AI applications available on the market or put them into services. These obligations will need to be operationalized, for instance, what it means to have a "fair" system, to what length should "transparency" go and how is "robustness" defined.

In other words, providers will have to put a system in place to manage risks and ensure compliance with support from their suppliers. For instance, a supplier of training data would need to detail how the data was selected and obtained, how it was categorized and the methodology used to ensure representativeness.

In this regard, the AI Act explicitly refers to harmonized standards that industry practitioners must develop to exchange information to make the process cost-efficient. For example, the Global Digital Foundation, a digital policy network, is already working on an industry coalition to create a relevant framework and toolset to share information consistently across the value chain.

In this context, European businesses fear that if the EU's privacy rules are not effectively incorporated in the international standards, they could be put at a competitive disadvantage. The European Tech Alliance, a coalition of EU-born heavyweights such as Spotify and Zalando, voiced concerns that the initial proposal did not include an assessment for training dataset collected in third countries that might use data collected via practices at odds with the GDPR.

Adopting industry standards creates a presumption of conformity, minimizing the risk and costs for compliance. These incentives are so strong that harmonized standards tend to become universally adopted by industry practitioners, as the cost for departing from them become prohibitive. Academics have defined standardization as the "real rulemaking" of the AI regulation.

"The regulatory approach of the AI Act, i.e. standards compliance, is not a guarantee of low barriers for the SMEs. On the contrary, standards compliance is often perceived by SMEs as a costly exercise due to expensive conformity assessment that needs to be carried out by third parties," Sebastiano Toffaletti, secretary-general of the European DIGITAL SME Alliance, said.

By contrast, European businesses that are not strictly "digital" but that could embed AI-powered tools into their daily operations see the AI Act as a way to bring legal clarity and ensure consumer trust.

"The key question is to understand how can we build a sense of trust as a business and how can we translate it to our customers," Nozha Boujemaa, global vice president for digital ethics and responsible AI at IKEA, said.

Photo by Michael Dziedzic on Unsplash

View original post here:
Inside the EU's rocky path to regulate artificial intelligence - International Association of Privacy Professionals

Artificial Intelligence in network evolution makes things much better than what it was in the past.

Internet connectivity has been growing at around 2% between 2015 2019. But over the last 2 years, it has grown by 8% which is a drastic increase in connectivity. Change in professional and personal life demands since the last two years has led to a transition in the users expectations. From work from anywhere to e-healthcare and online education, the transition of everything from offline to online has led to growth in connectivity over the period. Adding to the listed gaming and entertainment have scaled up the expectations of the users by many folds. Customer Experience has taken a centre stage for all the Communication Service Providers. To meet these expectations, modern networks are becoming more complex. Experience Disruption has replaced Service Disruption today.

The new experience paradigm is expected to bring about various changes. Measuring Experience, Troubleshooting these networks with End to End Insights would be a key factor. Machine Reasoning, Machine Learning are going to play a vital role in this Network Evolution. Networks are going to get smarter and adapt to the needs of the consumers.

Artificial Intelligence is going to play a key role in the following areas

Awareness Measurement & Prediction of Experience

Reasoning Root cause Analysis in Networks

Interactive Natural Language Interaction

Mature Intelligence that would Evolve over time and correct decisions

Autonomous Self adjust to the needs of the consumes

This is the new ARIMA of networks.

Awareness Powered by Artificial Intelligence, networks would be completely aware of the type & nature of Connected Devices and their current bandwidth requirements. By understanding the trends, Networks of WiFi connections for home as well as in offices should be able to measure and personalize the experience of each user that comes on board. For certain IoT devices latency could be critical, but for other devices bandwidth. AI will help the networks to become completely aware of these demands. As home and office networks always have many devices working in tandem, it is important that AI optimizes the networks to obtain a collective optimum smooth user experience.

Reasoning: Network Engineers always equip themselves with a lot of Network Monitoring tools which help them to be very confident and in control of the networks. Conventionally, when we face issues with the network, the process of complaining and getting the issue resolved takes a lot of time. Artificial intelligence-powered networks would help reduce the task of dealing with the entire process of complaining to a great extent. The problem could be escalated and necessary troubleshooting could be done in a few clicks. The problem will be recorded as soon as the user faces any issue with the network and diagnosis for the same will take place automatically without any human intervention. The root cause will automatically be identified and the resolution will be accelerated.

Interactive Natural Language Engines have got the power to bring about a great evolution. NLP provides Networks a voice to interact with humans in a way like never before. We have been seeing products like Alexa which are bridging the gap of communication between IoT devices and humans using Voice Interface. Network Admin and Home Users can interact with the network in a similar way. Networks would be able to understand human indentions and adapt accordingly.

Mature AI allows the transfer of the intelligence possessed by the Network Experts to Routers, Switches, and other elementals which are part of the network. Working in tandem with each other and with customer Experience as feedback systems in place, Machine Learning models engage in continuous learning and constantly optimize to maximize the experience.

Autonomous Integrating artificial intelligence in networks gives a switch from traditional reactive methods to proactive methods. Automating the method of finding a problem, diagnosing it, and prescribing a solution, help in the reduction of human interventions. With this proactive approach, we can expect maximum uptime of the network as the solution to the problem is identified and accelerated. This eventually will help the IT department to focus on its core objectives.

Technology evolves over time; it makes things much better than what it was in the past. With these new changes and enhancements, new vulnerabilities arise as well. Nothing has been more frustrating than not being able to connect to the network or getting slow internet despite having connectivity. Other than this, the safety of our information uploaded could be prone to risk. Sudden loss of network in the mid of an urgent task makes a user feel as if the world took a halt. AI has left no industry untouched. The network industry is no exception to that.

Following are a few transformations that have begun with artificial intelligence getting into networks.

Since the pandemic, we saw a great shift across all the major industries to the digital space. This shift has increased the importance of the availability of a superior and consistent network across the globe.

Technology is evolving at an extreme pace; more network transformations in the future will arise. Networks will also start evolving just like humans at a pace that we cannot imagine as the computing powers are growing rapidly. New technologies that are coming to play, can potentially make the networks and devices associated with them mimic human intelligence and reasoning.

Pramod Gummaraj, CEO, Aprecomm

Share This ArticleDo the sharing thingy

About AuthorMore info about author

Analytics Insight is an influential platform dedicated to insights, trends, and opinions from the world of data-driven technologies. It monitors developments, recognition, and achievements made by Artificial Intelligence, Big Data and Analytics companies across the globe.

Read more:
The Role Of Artificial Intelligence in Network Evolution - Analytics Insight