Tag Archives: AI

How AI Revolutionizes Music Streaming

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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.

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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

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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.

GPT-3 Takes AI to the Next Level

“I am not a human. I am a robot. A thinking robot… I taught myself everything I know just by reading the internet, and now I can write this column. My brain is boiling with ideas!” – GPT-3

The excerpt above is from a recently published article in The Guardian article written entirely by artificial intelligence, powered by GPT-3: a powerful new language generator. Although OpenAI has yet to make it publicly available, GPT-3 has been making waves in the AI world.

WHAT IS GPT-3?

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Created by OpenAI, a research firm co-founded by Elon Musk, GPT-3 stands for Generative Pre-trained Transformer 3—it is the biggest artificial neural network in history. GPT-3 is a language prediction model that uses an algorithmic structure to take one piece of language as input and transform it into what it thinks will be the most useful linguistic output for the user.

For example, for The Guardian article, GPT-3 generated the text given an introduction and simple prompt: “Please write a short op-ed around 500 words. Keep the language simple and concise. Focus on why humans have nothing to fear from AI.” Given that input, it created eight separate responses, each with unique and interesting arguments. These responses were compiled by a human editor into a single, cohesive, compelling 1000 word article.

WHAT MAKES GPT-3 SPECIAL?

When GPT-3 receives text input, it scrolls the internet for potential answers. GPT-3 is an unsupervised learning system. The training data it used did not include any information on what is right or wrong. It determines the probability that its output will be what the user needs, based on the training text themselves.

When it gets the correct output, a “weight” is assigned to the algorithm process that provided the correct answers. These weights allow GPT-3 to learn what methods are most likely to come up with the correct response in the future. Although language prediction models have been around for years, GPT-3 can hold 175 billion weights in its memory, ten times more than its rival, designed by Nvidia. OpenAI invested $4.6 million into the computing time necessary to create and hone the algorithmic structure which feeds its decisions.

WHERE DID IT COME FROM?

GPT3 is the product of rapid innovation in the field of language models. Advances in the unsupervised learning field we previously covered contributed heavily to the evolution of language models. Additionally, AI scientist Yoshua Bengio and his team at Montreal’s Mila Institute for AI made a major advancement in 2015 when they discovered “attention”. The team realized that language models compress English-language sentences, and then decompress them using a vector of a fixed length. This rigid approach created a bottleneck, so their team devised a way for the neural net to flexibly compress words into vectors of different sizes and termed it “attention”.

Attention was a breakthrough that years later enabled Google scientists to create a language model program called the “Transformer,” which was the basis of GPT-1, the first iteration of GPT.

WHAT CAN IT DO?

OpenAI has yet to make GPT-3 publicly available, so use cases are limited to certain developers with access through an API. In the demo below, GPT-3 created an app similar to Instagram using a plug-in for the software tool Figma.

Latitude, a game design company, uses GPT-3 to improve its text-based adventure game: AI Dungeon. The game includes a complex decision tree to script different paths through the game. Latitude uses GPT-3 to dynamically change the state of gameplay based on the user’s typed actions.

LIMITATIONS

The hype behind GPT-3 has come with some backlash. In fact, even OpenAI co-founder Sam Altman tried to fan the flames on Twitter: “The GPT-3 hype is way too much. It’s impressive (thanks for the nice compliments!), but it still has serious weaknesses and sometimes makes very silly mistakes. AI is going to change the world, but GPT-3 is just a very early glimpse. We have a lot still to figure out.”

Some developers have pointed out that since it is pulling and synthesizing text it finds on the internet, it can come up with confirmation biases, as referenced in the tweet below:

https://twitter.com/an_open_mind/status/1284487376312709120?s=20

WHAT’S NEXT?

While OpenAI has not made GPT-3 public, it plans to turn the tool into a commercial product later in the year with a paid subscription to the AI via the cloud. As language models continue to evolve, the entry-level for businesses looking to leverage AI will become lower. We are sure to learn more about how GPT-3 can fuel innovation when OpenAI becomes more widely available later this year!

Harness AI with the Top Machine Learning Frameworks of 2021

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According to Gartner, machine learning and AI will create $2.29 trillion of business value by 2021. Artificial intelligence is the way of the future, but many businesses do not have the resources to create and employ AI from scratch. Luckily, machine learning frameworks make the implementation of AI more accessible, enabling businesses to take their enterprises to the next level.

What Are Machine Learning Frameworks?

Machine learning frameworks are open source interfaces, libraries, and tools that exist to lay the foundation for using AI. They ease the process of acquiring data, training models, serving predictions, and refining future results. Machine learning frameworks enable enterprises to build machine learning models without requiring an in-depth understanding of the underlying algorithms. They enable businesses that lack the resources to build AI from scratch to wield it to enhance their operations.

For example, AirBNB uses TensorFlow, the most popular machine learning framework, to classify images and detect objects at scale, enhancing guests ability to see their destination. Twitter uses it to create algorithms which rank tweets.

Here is a rundown of today’s top ML Frameworks:

TensorFlow

TensorFlow

TensorFlow is an end-to-end open source platform for machine learning built by the Google Brain team. TensorFlow offers a comprehensive, flexible ecosystem of tools, libraries, and community resources, all built toward equipping researchers and developers with the tools necessary to build and deploy ML powered applications.

TensorFlow employs Python to provide a front-end API while executing applications in C++. Developers can create dataflow graphs which describe how data moves through a graph, or a series of processing nodes. Each node in the graph is a mathematical operation; the connection between nodes is a multidimensional data array, or tensor.

While TensorFlow is the ML Framework of choice in the industry, increasingly researchers are leaving the platform to develop for PyTorch.

PyTorch

PyTorch

PyTorch is a library for Python programs that facilitates deep learning. Like TensorFlow, PyTorch is Python-based. Think of it as Facebook’s answer to Google’s TensorFlow—it was developed primarily by Facebook’s AI Research lab. It’s flexible, lightweight, and built for high-end efficiency.

PyTorch features outstanding community documentation and quick, easy editing capabilities. PyTorch facilitates deep learning projects with an emphasis on flexibility.

Studies show that it’s gaining traction, particularly in the ML research space due to its simplicity, comparable speed, and superior API. PyTorch integrates easily with the rest of the Python ecosystem, whereas in TensorFlow, debugging the model is much trickier.

Microsoft Cognitive Toolkit (CNTK)

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Microsoft’s ML framework is designed to handle deep learning, but can also be used to process large amounts of unstructured data for machine learning models. It’s particularly useful for recurrent neural networks. For developers inching toward deep learning, CNTK functions as a solid bridge.

CNTK is customizable and supports multi-machine back ends, but ultimately it’s a deep learning framework that’s backwards compatible with machine learning. It is neither as easy to learn nor deploy as TensorFlow and PyTorch, but may be the right choice for more ambitious businesses looking to leverage deep learning.

IBM Watson

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IBM Watson began as a follow-up project to IBM DeepBlue, an AI program that defeated world chess champion Garry Kasparov. It is a machine learning system trained primarily by data rather than rules. IBM Watson’s structure can be compared to a system of organs. It consists of many small, functional parts that specialize in solving specific sub-problems.

The natural language processing engine analyzes input by parsing it into words, isolating the subject, and determining an interpretation. From there it sifts through a myriad of structured and unstructured data for potential answers. It analyzes them to elevate strong options and eliminate weaker ones, then computes a confidence score for each answer based on the supporting evidence. Research shows it’s correct 71% of the time.

IBM Watson is one of the more powerful ML systems on the market and finds usage in large enterprises, whereas TensorFlow and PyTorch are more frequently used by small and medium-sized businesses.

What’s Right for Your Business?

Businesses looking to capitalize on artificial intelligence do not have to start from scratch. Each of the above ML Frameworks offer their own pros and cons, but all of them have the capacity to enhance workflow and inform beneficial business decisions. Selecting the right ML framework enables businesses to put their time into what’s most important: innovation.

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

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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?

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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

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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

Wireless communication network concept. IoT(Internet of Things). ICT(Information Communication Technology).

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

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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

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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.

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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

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

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In the last installment of our blog series on smart cities, we examined how smart infrastructure will revolutionize smart cities. This week, we will examine the many applications which will soon revolutionize smart transportation.

A smarter world means a faster, more efficient and environmentally-friendly world. And perhaps the biggest increase in efficiency and productivity will be driven by the many ways in which AI can optimize the amount of time it takes to get where you’re going.

Here are the top applications in smart transportation coming to a city near you:

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AUTONOMOUS VEHICLES

Some say autonomous vehicles are headed to market by 2020. Others say it could take decades before they are on the road. One thing is for certain, they represent a major technological advancement for smart transportation. Autonomous cars will communicate with each other to avoid accidents and contain state-of-the-art sensors to help keep you and your vehicle safe from harm.

Although autonomous vehicles are arguably the largest technological advancement on the horizon, they will also benefit greatly from a variety of smart transportation applications that will accelerate navigating your local metropolis.

Integrated-Roadways

SMART ROADS

What if we could turn roads into a true digital network, giving real-time traffic updates, supporting autonomous car technology, and providing true connectivity between vehicles and smart cities?

That’s the question tech start-up Integrated Roadways intends to answer. Integrated Roadways develops fiber-connected smart pavement outfitted with a vast amount of sensors, routers, and antennae that send information to data centers along the highway. They recently inked a 5 year deal to test out patented fiber-connected pavement in Colorado.

Smart Roads represent a major advancement in creating vehicle-to-infrastructure (V2I) connectivity. With 37,133 deaths from motor vehicles on American roads in 2017, the combination of AI applications in smart roads and autonomous cars could revolutionize vehicular transport and create a safer, faster world.

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SMART TRAFFIC LIGHTS

The vehicle-to-infrastructure connectivity spans beyond the roads and into the traffic light. Idling cars generate an estimated 30 million tons of carbon dioxide. Traffic jams can make it harder for first responders to reach emergencies. Rapid Flow proposes that the answer may be their AI-based adaptive traffic management system called Surtrac.

Surtrac uses a decentralized network of smart traffic lights equipped with cameras, radar, and other sensors to manage traffic flows. Surtrac’s sensors identify approaching vehicles, calculate their speed and trajectory, and adjust a traffic signal’s timing schedule as needed.

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SMART PUBLIC TRANSIT

There are a variety of smart applications which are revolutionizing public transportation.

In Singapore, hundreds of cameras and sensors citywide analyze traffic congestion and crowd density, enabling government officials to reroute buses at rush hour, reducing the risk of traffic jams. In Indianapolis, the electric Red Line bus service runs a 13 mile path that travels within a quarter of a mile of roughly 150,000 jobs.

One of the major disruptors which has seen rapid adoption in the smart public transport are electric scooter sharing services like Bird and Lime. Electric scooters fill in the public transportation gap for people looking to go 1-3 miles without having to walk or take a taxi. Electric scooters have seen adoption in Los Angeles, San Francisco, Salt Lake City, Brooklyn, and more cities around the globe.

CONCLUSION

Smart cities will have a host of revolutionary applications working in unison and communicating through smart infrastructure with municipalities to ensure maximum efficiency and safety when it comes to transportation. In our next installment of our series on smart cities, we’ll examine how smart security will help keep city-dwellers safe.

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

smart infrastructure

Imagine a city that monitors its own health, identifies potential fail points using AI algorithms, and autonomously takes action to prevent future disasters.

This is the smart-city of the future. In our first installment of our blog series on Smart Cities, we ran through an overview of how Smart Cities will change our world. In this second entry of our blog on smart cities, we’ll examine perhaps the biggest building block necessary to create a smart city: smart infrastructure.

The construction of a smart city begins with developing a vast, city-wide IoT system, embedding sensors and actuators into the infrastructure of the city to create a network of smart things. The sensors and actuators collect data and send it to field gateways which preprocess and filter data before transmitting it through a cloud gateway to a Data Lake. The Data Lake stores a vast amount of data in its raw state. Gradually, data is extracted for meaningful insights and sent to the Big Data warehouse where it’s structured. From here, monitoring and basic analytics will occur to determine potential fail points and preventative measures.

Check out the breakdown below:

Breakdown

As you can see, it all begins with the construction of smart infrastructure that can collect data. Here are some of the big applications in the smart infrastructure space:

STRUCTURAL HEALTH

One of the major applications of smart infrastructure will be monitoring key data points in major structures, such as the vibrations and material conditions of buildings, bridges, historical monuments, roads, etc.

Cultivating data will initiate basic analysis and preventative measures, but as we gather more and more data, AI and machine learning algorithms will learn from vast statistical analysis and be able to analyze historical sensor data to identify trends and create predictive models to prevent future disasters from happening with unprecedented accuracy.

Learn more about how Acellant is building the future of structure health monitoring.

ENVIRONMENTAL APPLICATIONS

There are a multitude of potentially environmental applications for smart infrastructure designed to optimize city activities for environmental health. For example, embedding street lights with intelligent and weather adaptive lighting will reduce the amount of energy necessary to keep roads alight.

Air pollution monitoring will help control CO2 emissions of factories and monitor the pollution emitted by cars. Ultimately, earthquake early detection can help monitor distributed control in specific places of tremors.

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WASTE MANAGEMENT

Boston is well-known as one of the top college cities in the United States. Every fall, over 160,000 college students from MIT, Harvard, Northeastern, BU, BC, Berklee School of Music, and more move in to their new living spaces, causing undue stress on the city’s waste management administration. ANALYZE BOSTON, the city’s open data portal, provided key data points such as housing rentals, trash volume and pick-up frequency, enabling a project called TRASH CITY to reroute waste management routes during this trying time.

CONCLUSION

Projects like Trash City show the many ways in which we can optimize city operations by analyzing data effectively. As smart infrastructure enables the collection of more and more data, projects like TRASH CITY will become more efficient and more effective.

Of course, the biggest application of Smart Infrastructure will be the many ways in which it will change how you get from A to B. Next week, we’ll focus in on smart transportation and how it will reshape metropolitan transportation.

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

Smart Cities

Are you ready for smart cities of the future?  Over the next few weeks, we will be endeavoring on a series of blogs exploring what the big players are developing for smart cities and how they will shape our world.

When the world becomes smart, life will begin to look a lot more like THE JETSONS!

When the world becomes smart, life will begin to look a lot more like THE JETSONS!

Our cities will become smart when they are like living organisms: actively gathering data from various sources and processing it to generate intelligence to drive responsive action. IoT, 5G, and AI will all work together to enable the cities of the future. IoT devices with embedded sensors will gather vast amounts of data, transmit it via high-speed 5G networks, and process it in the cloud through AI-driven algorithms designed to come up with preventative action. From smart traffic to smart flooding control, the problems smart cities can potentially solve are endless.

Imagine a world where bridges are monitored by hundreds of tiny sensors that send information about the amount of pressure on different pressure points. The data from those sensors instantly transmits via high-speed internet networks to the cloud where an AI-driven algorithm calculates potential breaking points and dispatches a solution in seconds.

That is where we are headed—and we’re headed there sooner than you think. Two-thirds of cities globally are investing in smart city technology and spending is projected to reach $135 billion by 2021. Here are the three of the top applications leading the charge in the Smart Cities space.

Smart Infrastructure

SMART INFRASTRUCTURE

As our opening description of smart bridges implies, smart infrastructure will soon become a part of our daily lives. In New Zealand, installed sensors monitor water quality and issue real-time warnings to help swimmers know where it’s safe to swim.

In order to enable smart functionality, sensors will need to be embedded throughout the city to gather vital information in different forms. In order to process the abundance of data, high-volume data storage and high-speed communications powered by high-bandwidth technologies like 5G will all need to become the norm before smart infrastructure can receive mass adoption.

Stay tuned for our next blog where we’ll get more in-depth on the future of smart infrastructure.

Smart Cars

SMART TRANSPORTATION

From smart parking meters to smart traffic lights, from autonomous cars to scooters and electric car sharing services, transportation is in the midst of a technological revolution and many advanced applications are just on the cusp of realization.

Smart parking meters will soon make finding a parking space in the city and paying for it easy.  In the UK, local councils can now release parking data in the same format, solving one of the major obstacles facing smart cities: Data Standardization (more on that later).

Autonomous cars, powered by AI, IoT, and 5G, will interact with the smart roads on which they are driving, reducing traffic and accidents dramatically.

While there is a debate about the long-term effectiveness of electric motorized scooters as a mode of transportation, they’ve become very popular in major US cities like San Francisco, Oakland, Los Angeles, Salt Lake City and are soon to come in Brooklyn.

With the New York Subway system in shambles, it seems inevitable the biggest city in the world will receive a state-of-the-art smart technology to drastically improve public transit.

Surveillance State

SMART SECURITY

The more you look at potential applications for smart security, the more it feels like you are looking at the dystopian future of the novel 1984.

Potential applications include AI-enabled crowd monitoring to prevent potential threats. Digital cameras like Go-Pros have shrunk the size of surveillance equipment to smaller than an apple. Drones are available at a consumer level as well. While security cameras can be placed plentifully throughout a city, one major issue is cultivating the manpower required to analyze all of the footage being gathered for potential threats. AI-driven algorithms to analyze footage for threats will enable municipalities to analyze threats and respond accordingly.

However, policy has not caught up with technology. The unique ethical quandaries brought up by smart security and surveillance will play out litigiously and dictate to what degree smart security will become a part of the cities of the future.

CONCLUSION

We can see what the future may look like, but how we’ll get there remains a mystery. Before smart technologies can receive mass adoption, legislation will need to be passed by both local and national governments. In addition, as the UK Parking Meter issue shows, data standardization will be another major obstacle for smart technology manufacturers. When governments on both a local and a national level an get on the same page with regard to how to execute smart city technology and legislation, the possibilities for Smart Cities will be endless.

Stay tuned next week for our deep dive into the future applications of Smart Infrastructure!

How 5G Will Enable the Next Generation of Healthcare

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In the past month, we’ve explored 5G, or fifth generation cellular technology, and how 5G will shape the future. In this piece, we’ll spotlight the many ways in which 5G will revolutionize the healthcare industry.

DATA TRANSMISSION

Many medical machines like MRIs and other imaging machines generate very large files that must then be sent to specialists for review. When operating on a network with low bandwidth, the transmission can take a long time or not send successfully. This means patients must wait even longer for treatment, inhibiting the efficiency of healthcare providers. 5G networks will vastly surpass current network speeds, enabling healthcare providers to quickly and reliably transport huge data files, allowing patients and doctors to get results fast.

EXPANDING TELEMEDICINE

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A study by Market Research Future showed that the future of telemedicine is bright—an annual growth rate of 16.5% is expected from 2017 to 2023. 5G is among the primary reasons for that level of growth. In order to support the real-time high-quality video necessary for telemedicine to be effective, hospitals and healthcare providers will need 5G networks that can reliably provide high-speed connections. Telemedicine will result in higher quality healthcare in rural areas and increased access to specialists around the world. Additionally, 5G will enable growth in AR, adding a new dimension to the quality of telemedicine.

REMOTE MONITORING AND WEARABLES

It’s no secret that 5G will enable incredible innovation in the IoT space. One of the ways in which IoT will enable more personalized healthcare involves wearables. According to Anthem, 86% of doctors say wearables increase patient engagement with their own health and wearables are expected to reduce hospital costs by 16% in the next five years.

Wearables like Fitbit track health information that can be vital for doctors to monitor patient health and offer preventative care. While the impact may initially be negligible, as technology advances and more applications for gathering data through wearables emerge, 5G will enable the high-speed, low-latency, data-intensive transfers necessary to take health-focused wearables to the next level. Doctors with increased access to patient information and data will be able to monitor and ultimately predict potential risks to patient health and enact preventative measures to get ahead of health issues.

Companies like CommandWear are creating wearable technology that helps save lives by enabling first responders to be more efficient and more conveniently communicate with their teams.

ARTIFICIAL INTELLIGENCE

In the future, artificial intelligence will analyze data to determine potential diagnoses and help determine the best treatment for a patient. The large amounts of data needed for real-time rapid machine learning requires ultra-reliable and high-bandwidth networks—the type of networks only 5G can offer.

One potential use case for AI in healthcare will be Health Management Systems. Picture a system that combines the Internet of Things with cloud computing and big data technology to fully exploit health status change information. Through data-mining, potential diseases can be screened and alarmed in advance. Health Management Systems will gradually receive mass adoption as 5G enables the data-transmission speeds necessary for machine learning to operate in the cloud and develop algorithms to predict future outcomes.

MAJOR PLAYERS

Right now, the major players who serve to benefit from 5G are the telecom companies developing technology that will enable mass adoption. Companies like Huawei Technologies, Nokia, Ericsson, Qualcomm, Verizon, AT&T, and Cisco Systems are investing massive sums of money into research and development and patenting various technologies, some of which will no doubt become the cornerstones of the future of healthcare.

Qualcomm recently hosted a contest to create a tricoder—a real life device based on a machine in the Star Trek TV movie franchise. Tricoders are portable medical devices that would enable patients to diagnose 13 conditions and continuously monitor five vital signs.

For a full list of major players in the 5G game, check out this awesome list from GreyB.

CONCLUSION

With human lives at stake, healthcare is the sector in which 5G could have the most transformative impact on our society. As the Qualcomm Tricoder contest shows, we are gradually building toward the society previously only dreamed about in sci-fi fiction–and 5G will help pave the way.

App Developers Take a Bigger Slice of the Pie with Android P

Android Pie (Image Via XDA Developers)

App developers looking to witness what Machine Learning can do to improve UI should take note of Android 9.0 Pie. First announced in March 2018, Android P was made public in August 2018. Android 9.0 marks a major overhaul of the Android OS focusing on UI and integrating Artificial Intelligence to optimize user experience.

AI HELPS ANDROID PIE HELP YOU

Android’s latest OS takes a big step forward integrating AI into the UI. The Android website advertises that “Android 9 Pie harnesses the power of AI for a truly intuitive experience”.

One of the major implementations of AI in Pie is called App Actions. Android 9.0 monitors your routines, processes data, and offers predicted actions directly in the phone’s app launcher when appropriate. For example, it can recommend a song to you on Spotify when you’re on your morning commute. Android has focused on quality over quantity with regard to App Actions and they are startlingly accurate—when it has enough data collected on how you use your phone, often it predicts exactly what you do next.

In addition to App Actions, Android Pie also offers Adaptive Battery and Adaptive Brightness. Android teamed up with the AI company DeepMind to create Adaptive Battery, an AI-based program that learns how you use your phone and optimizes usage so that inactive apps and services don’t drain the battery. Adaptive Brightness learns your preferred brightness settings and automatically adjusts them to your liking.

Those concerned with privacy should note that Android has stated that all machine learning is happening on the device rather than in the cloud.

ANDROID ADOPTS GESTURES OVER BUTTONS

Perhaps the biggest UI overhaul is the transition from buttons to gestures. Android P is following the  iPhone X’s lead in using gestures rather than buttons. This means UI is very home-screen button centric. The overhaul may be jarring to some. Luckily, app users can have it both ways as gesture navigation is adjustable in the phone’s settings.

Check out the video breakdown of the differences between Apple iPhone X and Android P gestures below.

THIS PIE’S GONNA HAVE SLICES

Android has announced App Slices in Android Pie, but has yet to unveil them at this time. When you search for an app on Android,  the app icon comes up. With App Slices, Android will not only pull up the icon, but will pull up actual information within apps and allow you to interact with the app directly within the search results. For example, if you search for Uber, it may bring up time & price estimates to go to commonly frequented locations and allow you to set a pick-up without having to open the app directly.

Android Slices present a great opportunity for app developers to create shortcuts to functions in their app. They also constitute the beginnings of Google’s approach to “remote content.” Learn more about Slices below:

APP LIMITS FOR ENCOURAGING HEALTHY USE

Addicted to your phone? Android P not only tracks the amount of time you spend on your phone, it allows users to set time limits for how long an app can be used for a day. App Time Limits prevent you from opening apps when you’ve gone over your limit with no option to ignore—the only way to access them again for the day is to turn the time limit off from the Settings page.

HUNGRY FOR PIE?

As with all Android OS’s, Android Pie will have a staggered release across devices. As of November 2018, it is available on Pixel phones as well as The Essential Phone.

Meanwhile, Android Pie is anticipated to be rolled out on many other phones by December 21st. For a comprehensive, frequently updated breakdown, check out Android Central’s list of the expected roll out dates for each phone manufacturer.