Tag Archives: Artificial

How AI Revolutionizes Music Streaming

January 20, 2021Artificial Intelligence & Machine LearningAI, Artificial, Audio, Blockchain, Collaborative Filtering, Intelligence, Machine Learning, Models, Music, Natural Language Processing, NLP, Streamingadmin

In 2020, worldwide music streaming revenue hit 11.4 billion dollars, a 2800% growth over the course of a decade. Three hundred forty-one million paid online streaming subscribers get their music from top services like Apple Music, Spotify, and Tidal. The competition for listeners is fierce. Each company looks to leverage every advantage they can in pursuit of higher market share.

Like all major tech conglomerates, music streaming services collect an exceptional amount of user data through their platforms and are creating elaborate AI algorithms designed to improve user experience on a number of levels. Spotify has emerged as the largest on-demand music service active today and bolstered its success through the innovative use of AI.

Here are the top ways in which AI has changed music streaming:

COLLABORATIVE FILTERING

AI has the ability to sift through a plenitude of implicit consumer data, including:

Song preferences
Keyword preferences
Playlist data
Geographic location of listeners
Most used devices

AI algorithms can analyze user trends and identify users with similar tastes. For example, if AI deduces that User 1 and User 2 have similar tastes, then it can infer that songs User 1 has liked will also be enjoyed by User 2. Spotify’s algorithms will leverage this information to provide recommendations for User 2 based on what User 1 likes, but User 2 has yet to hear.

The result is not only improved recommendations, but greater exposure for artists that otherwise may not have been organically found by User 2.

NATURAL LANGUAGE PROCESSING

Natural Language Processing is a burgeoning field in AI. Previously in our blog, we covered GPT-3, the latest Natural Language Processing (NLP) technology developed by OpenAI. Music streaming services are well-versed in the technology and leverage it in a variety of ways to enhance UI.

Algorithms scan a track’s metadata, in addition to blog posts, discussions, and news articles about artists or songs on the internet to determine connections. When artists/songs are mentioned alongside artists/songs the user likes, algorithms make connections that fuel future recommendations.

GPT-3 is not perfect; its ability to track sentiments lacks nuance. As Sonos Radio general manager Ryan Taylor recently said to Fortune Magazine: “The truth is music is entirely subjective… There’s a reason why you listen to Anderson .Paak instead of a song that sounds exactly like Anderson .Paak.”

As NLP technology evolves and algorithms extend their grasp of the nuances of language, so will the recommendations provided to you by music streaming services.

AUDIO MODELS

AI can study audio models to categorize songs exclusively based on their waveforms. This scientific, binary approach to analyzing creative work enables streaming services to categorize songs and create recommendations regardless of the amount of coverage a song or artist has received.

BLOCKCHAIN

Artist payment of royalties on streaming services poses its own challenges, problems, and short-comings. Royalties are deduced from trillions of data points. Luckily, blockchain is helping to facilitate a smoother artist’s payment process. Blockchain technology can not only make the process more transparent but also more efficient. Spotify recently acquired blockchain company Mediachain Labs, which will, many pundits are saying, change royalty payments in streaming forever.

MORE TO COME

While AI has vastly improved streaming ability to keep their subscribers compelled, a long road of evolution lies ahead before it can come to a deep understanding of what motivates our musical tastes and interests. Today’s NLP capabilities provided by GPT-3 will probably become fairly archaic within three years as the technology is pushed further. One thing is clear: as streaming companies amass decades’ worth of user data, they won’t hesitate to leverage it in their pursuit of market dominance.

How Artificial Intuition Will Pave the Way for the Future of AI

September 28, 2020Artificial Intelligence & Machine LearningAI, Alphago, Analytics, Artificial, Data, Deepmind, Future, Intelligence, Intuition, Learning, Machine, Reinforcement, Selfadmin

Artificial intelligence is one of the most powerful technologies in history, and a sector defined by rapid growth. While numerous major advances in AI have occurred over the past decade, in order for AI to be truly intelligent, it must learn to think on its own when faced with unfamiliar situations to predict both positive and negative potential outcomes.

One of the major gifts of human consciousness is intuition. Intuition differs from other cognitive processes because it has more to do with a gut feeling than intellectually driven decision-making. AI researchers around the globe have long thought that artificial intuition was impossible, but now major tech titans like Google, Amazon, and IBM are all working to develop solutions and incorporate it into their operational flow.

WHAT IS ARTIFICIAL INTUITION?

Descriptive analytics inform the user of what happened, while diagnostic analytics address why it happened. Artificial intuition can be described as “predictive analytics,” an attempt to determine what may happen in the future based on what occurred in the past.

For example, Ronald Coifman, Phillips Professor of Mathematics at Yale University, and an innovator in the AI space, used artificial intuition to analyze millions of bank accounts in different countries to identify $1 billion worth of nominal money transfers that funded a well-known terrorist group.

Coifman deemed “computational intuition” the more accurate term for artificial intuition, since it analyzes relationships in data instead of merely analyzing data values. His team creates algorithms which identify previously undetected patterns, such as cybercrime. Artificial intuition has made waves in the financial services sector where global banks are increasingly using it to detect sophisticated financial cybercrime schemes, including: money laundering, fraud, and ATM hacking.

ALPHAGO

One of the major insights into artificial intuition was born out of Google’s DeepMind research in which a super computer used AI, called AlphaGo, to become a master in playing GO, an ancient Chinese board game that requires intuitive thinking as part of its strategy. AlphaGo evolved to beat the best human players in the world. Researchers then created a successor called AlphaGo Zero which defeated AlphaGo, developing its own strategy based on intuitive thinking. Within three days, AlphaGo Zero beat the 18—time world champion Lee Se-dol, 100 games to nil. After 40 days, it won 90% of matches against AlphaGo, making it arguably the best Go player in history at the time.

AlphaGo Zero represents a major advancement in the field of Reinforcement Learning or “Self Learning,” a subset of Deep Learning which is a subset of Machine Learning. Reinforcement learning uses advanced neural networks to leverage data into making decisions. AlphaGo Zero achieved “Self Play Reinforcement Learning,” playing Go millions of times without human intervention, creating a neural network of “artificial knowledge” reinforced by a sequence of actions that had both consequences and inception. AlphaGo Zero created knowledge itself from a blank slate without the constraints of human expertise.

ENHANCING RATHER THAN REPLACING HUMAN INTUITION

The goal of artificial intuition is not to replace human instinct, but as an additional tool to help improve performance. Rather than giving machines a mind of their own, these techniques enable them to acquire knowledge without proof or conscious reasoning, and identify opportunities or potential disasters, for seasoned analysts who will ultimately make decisions.

Many potential applications remain in development for Artificial Intuition. We expect to see autonomous cars harness it, processing vast amounts of data and coming to intuitive decisions designed to keep humans safe. Although its ultimate effects remain to be seen, many researchers anticipate Artificial Intuition will be the future of AI.

A Smarter World Part 4: Securing the Smart City and the Technology Within

January 24, 2020Internet of ThingsAI, Artificial, City, Disaster, Disaster Prevention, Future, IIoT, Intelligence, Internet of Things, IoT, Police, Prevention, security, Smart, Surveillance, Technology, Urbanadmin

In the last installment of our blog series on smart cities, we examined how smart transportation will make for a more efficient society. This week, we’ll examine how urban security stands to evolve with the implementation of smart technology.

Smart security in the modern era is a controversial issue for informed citizens. Many science fiction stories have dramatized the evolution of technology, and how every advance increases the danger of reaching a totalitarian state—particularly when it comes to surveillance. However, as a society, it would be foolish to refrain from using the technical power afforded to us to protect our cities.

Here are the top applications for smart security in the smart cities of the future:

Surveillance

Surveillance has been a political point of contention and paranoia since the Watergate scandal in the early 1970s. Whistleblower Edward Snowden became a martyr or traitor depending on your point of view when he exposed vast surveillance powers used by the NSA. As technology has rapidly evolved, the potential for governments to abuse their technological power has evolved with it.

Camera technology has evolved to the point where everyone has a tiny camera on them at all time via their phones. While monitoring entire cities with surveillance feeds is feasible, the amount of manpower necessary to monitor the footage and act in a timely manner rendered this mass surveillance ineffective. However, deep learning-driven AI video analytics tools can analyze real-time footage and identify anomalies, such as foreboding indicators of violence, and notify nearby law enforcement instantly.

In China, police forces use smart devices allied to a private broadband network to discover crimes. Huawei’s eLTE system allows officers to swap incident details securely and coordinate responses between central command and local patrols. In Shanghai, sophisticated security systems have seen crime rates drop by 30% and the amount of time for police to arrive at crime scenes drop to 3 minutes.

In Boston, to curb gun violence, the Boston police force has deployed an IoT sensor-based gunfire detection system that notifies officers to crime scenes within seconds.

Disaster Prevention

One of the major applications of IoT-based security system involves disaster prevention and effective use of smart communication and alert systems.

When disasters strike, governments require a streamlined method of coordinating strategy, accessing data, and managing a skilled workforce to enact the response. IoT devices and smart alert systems work together to sense impending disasters and give advance warning to the public about evacuations and security lockdown alerts.

Cybersecurity

The more smart applications present in city infrastructure, the more a city becomes susceptible to cyber attack. Unsecured devices, gateways, and networks each represent a potential vulnerability for a data breach. The average cost of a data breach according to IBM and the Poneman Institute is estimated at $3.86 million dollars. Thus, one of the major components of securing the smart city is the ramping up of cybersecurity to prevent hacking.

The Industrial Internet Consortium are helping establish frameworks across technologies to safely accelerate the Industrial Internet of Things (IIot) for transformational outcomes. GlobalSign works to move secure IoT deployments forward on a world-wide basis.

One of the first and most important steps toward cybersecurity is adopting standards and recommended guidelines to help address the smart city challenges of today. The Cybersecurity Framework is a voluntary framework consisting of standards, guidelines, and best practices to manage cybersecurity-related risk published by the National Institute of Standards and Technology (NIST), a non-regulatory agency in the US Department of Commerce. Gartner projects that 50% of U.S. businesses, critical infrastructure operators, and countries around the globe will use the framework as they develop and deploy smart city technology.

Conclusion

The Smart City will yield a technological revolution, begetting a bevy of potential applications in different fields, and with every application comes potential for hacker exploitation. Deployment of new technologies will require not only data standardization, but new security standardizations to ensure that these vulnerabilities are protected from cybersecurity threats. However, don’t expect cybersecurity to slow the evolution of the smart city too much as it’s expected to grow into a $135 billion dollar industry by 2021 according to TechRepublic.

This concludes our blog series on Smart Cities, we hope you enjoyed and learned from it! In case you missed it, check out our past entries for a full picture of the future of smart cities:

A Smarter World Part 1: How the Future of Smart Cities Will Change the World

A Smarter World Part 2: How Smart Infrastructure Will Reshape Your City

A Smarter World Part 3: How Smart Transportation Will Accelerate Your Business