Category Archives: Artificial Intelligence & Machine Learning

Machine Learning Unlocks Quantum Potential: A Paradigm-Shifting Partnership

Three Dimensional Qubit
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In the modern world, technology has revolutionized the way we work, carry out our tasks, and interact with one another. These technological transformations have come into existence due to the application of various scientific discoveries and computing power advancements. In recent years, Machine Learning and Quantum Computing have both evolved to become game-changers, taking their place in the revolutionary field of computer science. This blog will discuss the effects of machine learning on Quantum Computing, and how the models and algorithms derived in machine learning can be applied to enhance the power of quantum computing.

Machine learning has been a hot topic in the world of computer science, with its ability to analyze and make predictions from vast amounts of data. This has led to significant advancements in various fields such as healthcare, finance, and transportation. On the other hand, quantum computing has sparked excitement with its potential to solve complex problems that are impossible for traditional computers.

The Impact of Machine Learning on Quantum Computing

Machine learning and quantum computing are two powerful technologies that have the potential to complement each other. The combination of these two fields can create a cutting-edge technology that can solve some of the most complex problems known to humankind. One of the key areas where machine learning has shown its impact on quantum computing is in the optimization of quantum algorithms.

Quantum computers are known for their ability to process large amounts of data in a fraction of the time it would take traditional computers. However, implementing quantum algorithms can be challenging due to the complexity involved. This is where machine learning comes into play. By using machine learning models and algorithms, scientists and researchers can optimize these quantum algorithms to work more efficiently and accurately. This not only saves time and resources but also improves the overall performance of quantum computers.

Another area where machine learning has shown its potential in enhancing quantum computing is in error correction. As with any technology, errors are inevitable. In quantum computing, these errors can significantly impact the accuracy and reliability of calculations. By utilizing machine learning techniques, researchers have been able to develop algorithms that can detect and correct errors in quantum systems. This has greatly improved the stability and efficiency of quantum computers, making them more viable for practical use.

Difference between a Bit and Qubit

Exactly How is Machine Learning Impacting Quantum Computing?

Quantum computing, on the other hand, is a unique form of computing that employs quantum-mechanical phenomena such as superposition and entanglement to manipulate information. Unlike classical computers, where information is represented in bits (0s and 1s), quantum computers use qubits to represent information. This allows them to handle and process multiple calculations simultaneously, making them incredibly powerful.

The integration of machine learning with quantum computing has opened new avenues for the development of more sophisticated algorithms and models that can solve complex problems. Machine learning techniques such as neural networks and deep learning are being applied to quantum computing, allowing for enhanced data processing and analysis. This has led to a better understanding and utilization of quantum properties, resulting in improved performance and accuracy in solving complex problems. The potential of this partnership is immense, and it has the potential to shape the future of computing.

Neural Network

Challenges and Opportunities

While the partnership between machine learning and quantum computing offers many opportunities, there are also some challenges that need to be addressed. One major challenge is the limited availability of quantum hardware. Quantum computers are still in their early stages of development, and only a few companies and research institutions have access to them. This can hinder the progress of using machine learning techniques in quantum computing.

Additionally, there is a shortage of experts who possess both machine learning and quantum computing knowledge. Both fields require a deep understanding of complex mathematical concepts, making it challenging to find individuals with expertise in both areas. As such, there is a need for more interdisciplinary training and collaboration between these fields to bridge this gap.

Machine Learning and Quantum Computing Effects

Machine learning and quantum computing have significant positive effects when used together. Machine learning can help quantum computing to identify, react, and handle large volumes of data quickly and efficiently. Both technologies rely on deep mathematical connections, and when combined, they can improve the precision and accuracy of quantum computations. This will enable quantum computers to solve complex problems much quicker than before. Additionally, machine learning can help in reducing the sensitivity of quantum computers to errors and noise, which are common in these systems. This will lead to improved stability and reliability of quantum computers, making them more practical for solving real-world problems.

Quantum Circuit

Moreover, the integration of machine learning with quantum computing can also aid in the development of new quantum algorithms. These algorithms can be used in various applications such as optimization problems, simulation, and machine learning. The combination of these two technologies has the potential to transform various fields, including finance, drug discovery, and climate modeling.

Some Examples of Companies using Machine Learning for Quantum Computing

Several companies use machine learning and quantum computing to improve their processes and services such as: IBM, Google, Microsoft, Rigetti and Anyon Systems.

IBM: IBM Quantum is at the forefront of research and development in quantum machine learning algorithms. They’ve launched the Qiskit Machine Learning library, enabling users to implement quantum machine learning models on IBM’s quantum computers.

Google: Known for its Quantum AI lab, has been exploring the acceleration of machine learning tasks using quantum processors, particularly in the development of quantum neural networks.

Rigetti: Rigetti has been actively using quantum computers for machine learning applications. They offer the Quantum Machine Learning (QML) toolkit, which implements machine learning algorithms on quantum hardware.

Microsoft: Microsoft has been actively researching quantum machine learning and has integrated quantum computing capabilities into their Azure cloud platform, providing resources for quantum machine learning research.

Anyon Systems: Anyon Systems, a quantum software company, explores the application of quantum computing to machine learning and optimization problems, providing software tools for quantum machine learning research.

It’s worth noting that the field of quantum computing is rapidly evolving, and new companies and developments are emerging continually.

Future Possibilities

Quantum Mechanics and Drug Discovery

The combination of machine learning and quantum computing holds immense potential for the future. As both technologies continue to advance and evolve, their integration will lead to groundbreaking innovations in fields such as drug discovery, finance, materials science, and more. With the ability to process vast amounts of data quickly and efficiently, quantum computers powered by machine learning will revolutionize problem-solving and decision-making processes. This will have a profound impact on various industries, leading to the development of new products and services that were previously unimaginable.

Here are some future possibilities and effects of the synergy between machine learning and quantum computing:

Faster Optimization: Quantum computers excel at solving optimization problems, which are prevalent in machine learning. They can significantly speed up tasks like hyperparameter tuning, portfolio optimization, and feature selection, making machine-learning models more efficient and accurate.

Quantum Machine Learning Models: Quantum machine learning algorithms may become a reality, utilizing the inherent properties of quantum systems to create novel models capable of solving complex problems.

Improved Data Processing: Quantum computing can enhance data preprocessing tasks like dimensionality reduction, clustering, and pattern recognition. Quantum algorithms can efficiently handle large datasets, potentially reducing the need for extensive data cleaning and preparation.

Enhanced AI Training: Quantum computers could expedite the training of deep learning models, which is a computationally intensive task. This could lead to faster model training and the ability to tackle more complex neural network architectures.

Quantum Data Analysis: Quantum computing can facilitate the analysis of quantum data, which is generated by quantum sensors and experiments. Quantum machine learning can help in extracting meaningful insights from this data, leading to advancements in physics, chemistry, and materials science.

Drug Discovery and Material Science: Quantum computing combined with machine learning can accelerate drug discovery and materials research. Quantum simulations can accurately model molecular structures and properties, leading to the development of new drugs and materials.

Quantum-Assisted AI Services: Cloud providers may offer quantum-assisted AI services, allowing businesses and researchers to harness the power of quantum computing for machine learning tasks via the cloud, similar to how cloud-based GPUs are used today.

Improved Security: Quantum machine learning can contribute to enhancing cybersecurity by developing more robust encryption and security protocols. Quantum-resistant encryption algorithms are being explored to safeguard data against quantum attacks.

It’s important to note that the full realization of these possibilities depends on advancements in both quantum hardware and quantum algorithms, as well as the integration of quantum computing into existing machine learning workflows. While quantum computing is a promising technology, it is still in its early stages, and practical applications may take several years to become widespread.

Additional Benefits of Machine Learning on Quantum Computing

With machine learning, quantum computing can quickly recognize patterns and anomalies, which can lead to improvements in supply chain logistics and customer service. Additionally, it has the potential to aid breakthrough research in cancer treatments and other scientific issues that currently require significant amounts of time and effort. Using machine learning with quantum computing could generate the solutions more efficiently. Moreover, as quantum computers continue to scale, the applications and potential benefits will only increase. It’s an exciting time for both fields, and the future possibilities are limitless. Combining these two technologies will pave the way for groundbreaking discoveries and advancements that will shape our society in unimaginable ways.

Qubit

Machine Learning has led to significant improvements in many sectors, and in recent years, Quantum Computing has begun to change how various industries process and analyze data. The effects of machine learning on Quantum Computing can enhance computing efficiency and precision and lead to groundbreaking research. As we continue to explore the possibilities of machine learning and quantum computing, the future is looking increasingly bright for the integration of these two innovative technologies. The application of machine learning to quantum computing has the potential to transform how we conduct research, and it is exciting to think about what changes will come about in the not-too-distant future. The possibilities are endless, and the integration of these two fields is just beginning. We can only imagine the advancements that will be made through this synergy and eagerly await what’s to come. So, it is essential to continue learning about both machine learning and quantum computing, staying updated on new developments, and exploring potential applications in various industries. By doing so, we can fully embrace and harness the power of machine learning and quantum computing, leading to a more advanced and innovative future. So, let’s keep learning and exploring the possibilities together!

In conclusion, machine learning and quantum computing are powerful technologies on their own, but when combined, their potential becomes even greater. As we continue to make advancements in both fields, it is crucial to explore and embrace the possibilities of their integration.

The many ways machine learning has revolutionized the aviation industry

Augmented Reality and Aviation
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The aviation industry has experienced tremendous growth in recent years, thanks to technological advancements that have made flying safer, more efficient, and cost-effective. One of the most exciting impactful advances in aviation technology is machine learning. By harnessing the power of machine learning, airlines can efficiently analyze massive volumes of data, enabling them to make well-informed decisions and enhance safety measures. In this blog post, we will delve into the transformative power of machine learning in revolutionizing the aviation industry and examine its profound implications for the future.

Safety First!

Safety is of utmost importance in the aviation industry, and the utilization of machine learning holds the potential to further enhance the safety of air travel. With access to vast amounts of data, machine learning algorithms can detect patterns and anomalies that humans may overlook. This technology can be used to predict and prevent potential safety hazards, such as mechanical failures or adverse weather conditions. Machine learning can also analyze pilot and crew performance data to identify areas for improvement, leading to better training programs and ultimately safer flights. As a result, passengers can have peace of mind knowing that their safety is being prioritized in every aspect of air travel.

Flight Operations

AR increases aviation efficiency

In addition to enhancing safety, machine learning is also revolutionizing flight operations. With real-time data analysis, airlines can optimize flight routes to reduce fuel consumption and decrease flight times. Machine learning algorithms can also analyze historical data to predict demand for flights and adjust schedules accordingly, reducing delays and cancellations. This technology can also assist with flight planning and decision-making processes, such as determining the most efficient altitude for a flight based on weather conditions. By improving operational efficiency, machine learning is saving airlines time and money while also reducing their impact on the environment. These improvements not only benefit the airlines but also provide a better travel experience for passengers.

Efficiency at its Best

Another area where machine learning has great potential to revolutionize the aviation industry is in streamlining operations and improving efficiency. Airline companies deal with immense amounts of data on a daily basis, ranging from passenger bookings and flight schedules to maintenance and crew schedules. By implementing machine learning algorithms, airlines can quickly analyze this data and make predictions on potential delays or cancellations, allowing them to take proactive measures. This not only saves time and resources but also enhances the overall travel experience for passengers. Moreover, by optimizing flight routes and fuel consumption through machine learning, airlines can significantly reduce their operational costs.

Airlines are under constant pressure to improve efficiency, and machine learning algorithms can help them achieve this goal. By analyzing data from flight operations, airlines can optimize fuel consumption, reduce turnaround times, and improve on-time arrivals. Additionally, airlines can use machine learning algorithms to predict delays and identify opportunities to improve operational efficiency. This can result in significant time and cost savings for airlines, making air travel more efficient for both passengers and the industry as a whole.

Personalization and Customer Experience

Increase customer experience

Machine learning algorithms are being used by airlines to understand passenger behavior and preferences. By analyzing data from past bookings and interactions with customers, airlines can predict what customers want and provide personalized services and offers. For example, airlines can use machine learning to personalize in-flight entertainment options, recommend travel destinations, and offer relevant upgrades or travel packages. As a result, airlines can improve the customer experience and build stronger relationships with their passengers.

Predictive Maintenance

By using data from sensors and other sources, machine learning algorithms can detect potential equipment failures before they happen, allowing for proactive maintenance rather than reactive repairs. This predictive maintenance approach not only reduces the risk of in-flight malfunctions but also decreases maintenance costs for airlines. By identifying potential issues early on, airlines can schedule maintenance during off-peak times, reducing the impact on flight schedules and passenger experience. This not only improves the overall safety of flights but also helps airlines save money and operate more efficiently.

In addition to improving safety, flight operations, and maintenance, machine learning is also making a significant impact in the field of air traffic control. By analyzing real-time data from multiple sources, including radar and weather systems, machine learning algorithms can help optimize air traffic flow and reduce congestion. This not only saves time and fuel but also improves safety by reducing the risk of mid-air collisions.

Reduced Costs

In recent years, there has been a noticeable surge in ticket prices, reaching unprecedented heights across the airline industry. As a solution, leveraging advanced machine learning algorithms for predictive maintenance can prove to be highly advantageous for airlines. By accurately predicting maintenance needs, airlines can significantly cut down on expensive repairs and replacements, thereby saving substantial costs.

Moreover, enhancing safety measures plays a crucial role in preventing costly accidents and delays, which can potentially result in lost revenue. By prioritizing safety and implementing effective strategies, airlines can not only safeguard their passengers but also maintain a consistent and reliable service, further boosting customer satisfaction.

Additionally, optimizing flight routes and schedules can yield significant cost-saving benefits. Through careful analysis and adjustments, airlines can minimize fuel consumption, leading to substantial savings in fuel costs. This, in turn, directly impacts the profitability of airlines, allowing for potential reductions in ticket prices for passengers.

By implementing these comprehensive measures, airlines can not only enhance their operational efficiency but also make air travel more affordable and accessible, ultimately benefiting both the industry and the passengers alike.

Fraud Prevention

Machine learning algorithms can be used by airlines to detect and prevent fraud. By analyzing booking and payment data, airlines can identify fraudulent transactions and take action before they result in any loss. Additionally, machine learning algorithms can be used to identify patterns of fraud and prevent future incidents. By using machine learning for fraud prevention, airlines can save millions of dollars and protect their reputation.

Here are a few illustrations of the machine learning initiatives being implemented by some of the leading airlines.

Delta Airlines – Delta Airlines leverages the power of machine learning algorithms to meticulously analyze vast amounts of data collected from aircraft sensors. By scrutinizing this data, they are able to continually monitor and fine-tune aircraft performance, diminish maintenance duration, and enhance fuel efficiency to a remarkable degree. Moreover, Delta Airlines employs machine learning techniques to personalize its esteemed SkyMiles rewards program, tailoring exclusive and targeted promotions to its valued customers, ensuring an unparalleled travel experience.

American Airlines– American Airlines leverages the power of machine learning algorithms to analyze vast amounts of data from various operational systems, such as flight planning and crew scheduling. By conducting such comprehensive analysis, American Airlines can uncover valuable insights and identify numerous opportunities for optimization, thereby enhancing overall operational efficiency to unprecedented levels. Moreover, through the utilization of cutting-edge machine learning techniques, American Airlines goes beyond the realm of operational data and delves into customer-centric insights. This enables them to provide personalized recommendations for travel options and upgrades, ensuring that each customer’s journey is tailored to their unique preferences and needs. With a commitment to innovation and utilizing advanced technologies, American Airlines continues to redefine the travel experience, setting new benchmarks in the industry.

United Airlines

United Airlines – United Airlines leverages advanced machine learning algorithms to thoroughly analyze a wide range of customer data, taking into account individual preferences, travel history, and even previous interactions. This comprehensive analysis enables the airline to create highly personalized offers and tailor the customer experience to unparalleled levels of satisfaction. Moreover, through the power of machine learning, United Airlines optimizes flight schedules with precision, ensuring enhanced on-time performance and delivering an even smoother travel experience for passengers. By embracing cutting-edge technological advancements, United Airlines remains at the forefront of innovation, consistently striving to exceed customer expectations and set new standards in the aviation industry.

Southwest Airlines – Utilizing advanced machine learning algorithms, Southwest Airlines leverages the power of data analysis to thoroughly examine safety data, encompassing flight data recorders and cockpit voice recorders. By conducting meticulous analysis, potential safety risks can be promptly identified, enabling proactive measures to be taken before they manifest into larger issues. Furthermore, Southwest Airlines harnesses the capabilities of machine learning to optimize fuel consumption, resulting in significant cost reductions and enhanced operational efficiency.

Virgin Atlantic – Virgin Atlantic uses machine learning algorithms to analyze data from aircraft sensors and engines. This analysis is used for predictive maintenance, identifying potential issues before they result in delays or cancellations. Additionally, Virgin Atlantic uses machine learning to personalize its customer experience, from in-flight entertainment options to tailored travel recommendations.

Emitates Airlines

Emirates Airlines – Emirates Airlines uses machine learning algorithms to analyze customer data, including booking history, preferences, and feedback. This analysis is used to improve the customer experience by offering personalized services and recommendations. Additionally, Emirates Airlines uses machine learning to optimize flight routes and schedules, reducing fuel costs and improving on-time performance.

As you can see, machine learning is playing a crucial role in the aviation industry by improving safety, efficiency, and customer experience while also saving airlines millions of dollars in costs. With continued advancements in technology and data analysis, we can expect even more advancements and improvements in the future. From optimizing flight operations to detecting fraud, machine learning is revolutionizing the way we travel and shaping the future of air travel. So next time you board a flight, remember to thank machine learning for making your journey safer, smoother, and more affordable.

In conclusion, it is evident that machine learning is revolutionizing the aviation industry. From improving safety to increasing efficiency and enhancing customer experience, the potential impact of machine learning in aviation is immense. As technology continues to advance, we can only expect to see more innovative applications of machine learning in the aviation industry, ultimately leading to a safer, more efficient, and enjoyable travel experience for all. So buckle up and get ready for a future of flying that is powered by machine learning! So, let’s continue exploring the potential impact of machine learning on other industries as well. Machine learning has already made its mark in healthcare, finance, marketing, and many other sectors. As this technology continues to evolve, we can expect to see even more industries adopting it and exploring its capabilities. With the help of machine learning, companies can make faster and more accurate decisions, optimize processes and resources, and provide better services to their customers. The future is bright for machine learning, and its potential to transform industries is limitless. So let’s keep an eye on this rapidly evolving technology and see where it takes us in the future. The possibilities are endless, and we can only imagine the exciting advancements that are yet to come.

How the Internet of Behaviors Will Shape the Future of Digital Marketing

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In the digital age, businesses need to leverage every possible platform and cutting-edge technology in order to get a leg up on the competition. We’ve covered the Internet of Things extensively on the Mystic Media blog, but a new and related tech trend is making waves. This trend is called the Internet of Behaviors and according to Gartner, about 40% of people will have their behavior tracked by the IoB globally by 2023.

WHAT IS THE IOB?

Internet of Behavior, or the IoB, exists at the intersection of technology, data analytics, and behavioral science. The IoB leverages data collected from a variety of sources, including online activities, social media, wearable devices, commercial transactions and IoT devices, in order to deliver insights related to consumers and purchasing behavior.

With devices more interconnected than ever, the IoB tracks, gathers, combines and interprets massive data sets so that businesses can better understand their consumers. Businesses leverage analysis from the IoB to offer more personalized marketing with the goal of influencing consumer decision making.

HOW DOES IT WORK?

Traditionally, a car insurance company would analyze a customer’s driving history in order to determine if they are a good or bad driver. However, in today’s digital age, they might take it a step further and analyze social media profiles in order to “predict” whether a customer is a safe driver. Imagine what insights they could gather from a user’s Google search history or Amazon purchases? Access to large datasets enables large companies to create psychographic profiles and gather an enhanced understanding of their customer base.

Businesses can use the IoB for more than just purchasing decisions. UX designers can leverage insights to deliver more effective customer experiences. Large companies such as Ford are designing autonomous vehicles that change based on the city, modulating behavior based on vehicle traffic, pedestrians, bicycles and more.

GBKSOFT created a mobile application that collects data from wearable devices in order to help golfers improve their skills. The application records each golf ball hit, including the stroke, force, trajectory and angle, and delivers visual recommendations to improve their swing and technique. Insights gathered through data are translated into behavioral trends that are then converted into recommendations to improve the user’s game.

The IoB is all about collecting data that can be translated into behavior which helps companies understand consumer tendencies and translate them into meaningful actions.

CONCERNS

While there is quite a bit of enthusiasm surrounding the potential impact of the IoB for B2C companies, a number of legal concerns come with it. A New York Times article, written by Harvard Business School emeritus professor Shoshana Zuboff, warns of the age of surveillance capitalism where tech behemoths surveil humans with the intent to control their behavior.

Due to the speed at which technology and the ability to collect data has proliferated, privacy and data security are under-regulated and major concerns for consumers. For example, Facebook was applying facial recognition scans in advance of the 2016 election without user’s consent. Cambridge Analytica’s use of psychoanalytic profiles has been the subject of much derision. Momentum for data privacy regulation is growing and since the IoB hinges on the ability for companies to collect and market data, forthcoming regulations could inhibit its impact.

CONCLUSION

Despite regulatory concerns, the IoB is a sector that we expect to see grow over time. As the IoT generates big data and AI evolves to learn how to parse through and analyze it, it’s only natural that companies will take the next step to leverage analysis to enhance their understanding of their customers’ behaviors and use it to their advantage. The IoB is where that next step will take place.

How AI Fuels a Game-Changing Technology in Geospatial 2.0

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Geospatial technology describes a broad range of modern tools which enable the geographic mapping and analysis of Earth and human societies. Since the 19th century, geospatial technology has evolved as aerial photography and eventually satellite imaging revolutionized cartography and mapmaking.

Contemporary society now employs geospatial technology in a vast array of applications, from commercial satellite imaging, to GPS, to Geographic Information Systems (GIS) and Internet Mapping Technologies like Google Earth. The geospatial analytics market is currently valued between $35 and $40 billion with the market projected to hit $86 billion by 2023.

GEOSPATIAL 1.0 VS. 2.0

geospatial

Geospatial technology has been in phase 1.0 for centuries; however, the boon of artificial intelligence and the IoT has made Geospatial 2.0 a reality. Geospatial 1.0 offers valuable information for analysts to view, analyze, and download geospatial data streams. Geospatial 2.0 takes it to the next level–harnessing artificial intelligence to not only collect data, but to process, model, analyze and make decisions based on the analysis.

When empowered by artificial intelligence, geospatial 2.0 technology has the potential to revolutionize a number of verticals. Savvy application developers and government agencies in particular have rushed to the forefront of creating cutting edge solutions with the technology.

PLATFORM AS A SERVICE (PaaS) SOLUTIONS

Effective geospatial 2.0 solutions require a deep vertical-specific knowledge of client needs, which has lagged behind the technical capabilities of the platform. The bulk of currently available geospatial 2.0 technologies are offered as “one-size-fits-all” Platform as a Service (PaaS) solutions. The challenge for PaaS providers is that they need to serve a wide collection of use cases, harmonizing data from multiple sensors together while enabling users to simply understand and address the many different insights which can be gleaned from the data.

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In precision agriculture, FarmShots offers precise, frequent imagery to farmers along with meaningful analysis of field variability, damage extent, and the effects of applications through time.

Mayday

In the disaster management field, Mayday offers a centralized artificial intelligence platform with real-time disaster information. Another geospatial 2.0 application Cloud to Street uses a mix of AI and satellites to track floods in near real-time, offering extremely valuable information to both insurance companies and municipalities.

SUSTAINABILITY

The growing complexity of environmental concerns have led to a number of applications of geospatial 2.0 technology to help create a safer, more sustainable world. For example, geospatial technology can measure carbon sequestration, tree density, green cover, carbon credit & tree age. It can provide vulnerability assessment surveys in disaster-prone areas. It can also help urban planners and governments plan and implement community mapping and equitable housing. Geospatial 2.0 can analyze a confluence of factors and create actionable insight toward analyzing and honing our environmental practices.

As geospatial 1.0 models are upgraded to geospatial 2.0, expect to see more robust solutions incorporating AI-powered analytics. A survey of working professionals conducted by Geospatial World found that geospatial technology will likely make the biggest impact in the climate and environment field.

CONCLUSION

Geospatial 2.0 platforms are very expensive to employ and require quite a bit of development.  The technology offers great potential to increase revenue and efficiency for a number of verticals. In addition, it may be a key technology to help cut down our carbon footprint and create a safer, more sustainable world..

AIoT: How the Intersection of AI and IoT Will Drive Innovation for Decades to Come

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We have covered the evolution of the Internet of Things (IoT) and Artificial Intelligence (AI) over the years as they have gained prominence. IoT devices collect a massive amount of data. Cisco projects by the end of 2021, IoT devices will collect over 800 zettabytes of data per year. Meanwhile, AI algorithms can parse through big data and teach themselves to analyze and identify patterns to make predictions. Both technologies enable a seemingly endless amount of applications retained a massive impact on many industry verticals.

What happens when you merge them? The result is aptly named the AIoT (Artificial Intelligence of Things) and it will take IoT devices to the next level.

WHAT IS AIOT?

AIoT is any system that integrates AI technologies with IoT infrastructure, enhancing efficiency, human-machine interactions, data management and analytics.

IoT enables devices to collect, store, and analyze big data. Device operators and field engineers typically control devices. AI enhances IoT’s existing systems, enabling them to take the next step to determine and take the appropriate action based on the analysis of the data.

By embedding AI into infrastructure components, including programs, chipsets, and edge computing, AIoT enables intelligent, connected systems to learn, self-correct and self-diagnose potential issues.

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One common example comes in the surveillance field. Surveillance camera can be used as an image sensor, sending every frame to an IoT system which analyzes the feed for certain objects. AI can analyze the frame and only send frames when it detects a specific object—significantly speeding up the process while reducing the amount of data generated since irrelevant frames are excluded.

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While AIoT will no doubt find a variety of applications across industries, the three segments we expect to see the most impact on are wearables, smart cities, and retail.

WEARABLES

Wearable-IoT-Devices

The global wearable device market is estimated to hit more than $87 billion by 2022. AI applications on wearable devices such as smartwatches pose a number of potential applications, particularly in the healthtech sector.

Researchers in Taiwan have been studying the potential for an AIoT wearable system for electrocardiogram (ECG) analysis and cardiac disease detection. The system would integrate a wearable IoT-based system with an AI platform for cardiac disease detection. The wearable collects real-time health data and stores it in a cloud where an AI algorithm detects disease with an average of 94% accuracy. Currently, Apple Watch Series 4 or later includes an ECG app which captures symptoms of irregular, rapid or skipped heartbeats.

Although this device is still in development, we expect to see more coming out of the wearables segment as 5G enables more robust cloud-based processing power, taking the pressure off the devices themselves.

SMART CITIES

We’ve previously explored the future of smart cities in our blog series A Smarter World. With cities eager to invest in improving public safety, transport, and energy efficiency, AIoT will drive innovation in the smart city space.

There are a number of potential applications for AIoT in smart cities. AIoT’s ability to analyze data and act opens up a number of possibilities for optimizing energy consumption for IoT systems. Smart streetlights and energy grids can analyze data to reduce wasted energy without inconveniencing citizens.

Some smart cities have already adopted AIoT applications in the transportation space. New Delhi, which boasts some of the worst traffic in the world, features an Intelligent Transport Management System (ITMS) which makes real-time dynamic decisions on traffic flows to accelerate traffic.

RETAIL

AIoT has the potential to enhance the retail shopping experience with digital augmentation. The same smart cameras we referenced earlier are being used to detect shoplifters. Walmart recently confirmed it has installed smart security cameras in over 1,000 stores.

smart-shopping-cart

One of the big innovations for AIoT involves smart shopping carts. Grocery stores in both Canada and the United States are experimenting with high-tech shopping carts, including one from Caper which uses image recognition and built-in sensors to determine what a person puts into the shopping cart.

The potential for smart shopping carts is vast—these carts will be able to inform customers of deals and promotion, recommend products based on their buying decisions, enable them to view an itemized list of their current purchases, and incorporate indoor navigation to lead them to their desired items.

A smart shopping cart company called IMAGR recently raised $14 million in a pre-Series A funding round, pointing toward a bright future for smart shopping carts.

CONCLUSION

AIoT represents the intersection of AI, IoT, 5G, and big data. 5G enables the cloud processing power for IoT devices to employ AI algorithms to analyze big data to determine and enact action items. These technologies are all relatively young, and as they continue to grow, they will empower innovators to build a smarter future for our world.

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.

via Mehmet Toprak (Medium)
via Mehmet Toprak (Medium)

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

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“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

IBM-Watson

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

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.

The Future of Indoor GPS Part 5: Inside AR’s Potential to Dominate the Indoor Positioning Space

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In the previous installment of our blog series on indoor positioning, we explored how RFID Tags are finding traction in the indoor positioning space. This week, we will examine the potential for AR Indoor Positioning to receive mass adoption.

When Pokemon Go accrued 550 million installs and made $470 million in revenues in 2016, AR became a household name technology. The release of ARKit and ARCore significantly enhanced the ability for mobile app developers to create popular AR apps. However, since Pokemon Go’s explosive release, no application has brought AR technology to the forefront of the public conversation.

When it comes to indoor positioning technology, AR has major growth potential. GPS is the most prevalent technology navigation space, but it cannot provide accurate positioning within buildings. GPS can be accurate in large buildings such as airports, but it fails to locate floor number and more specifics. Where GPS fails, AR-based indoor positioning systems can flourish.

HOW DOES IT WORK?

AR indoor navigation consists of three modules: Mapping, Positioning, and Rendering.

via Mobi Dev
via Mobi Dev

Mapping: creates a map of an indoor space to make a route.

Rendering: manages the design of the AR content as displayed to the user.

Positioning: is the most complex module. There’s no accurate way of using the technology available within the device to determine the precise location of users indoors, including the exact floor.

AR-based indoor positioning solves that problem by using Visual Markers, or AR Markers, to establish the users’ position. Visual markers are recognized by Apple’s ARKit, Google’s ARCore, and other AR SDKs.  When the user scans that marker, it can identify exactly where the user is and provide them with a navigation interface. The further the user is from the last visual marker, the less accurate their location information becomes. In order to maintain accuracy, developers recommend placing visual markers every 50 meters.

Whereas beacon-based indoor positioning technologies can become expensive quickly, running $10-20 per beacon with a working range of around 10-100 meters of accuracy, AR visual markers are the more precise and cost-effective solution with an accuracy threshold down to within millimeters.

Via View AR
Via View AR

CHALLENGES

Performance can decline when more markers have been into an AR-based VPS because all markers must be checked to find a match. If the application is set up for a small building where 10-20 markers are required, it is not an issue. If it’s a chain of supermarkets requiring thousands of visual markers across a city, it becomes more challenging.

Luckily, GPS can help determine the building where the user is located, limiting the number of visual markers the application will ping. Innovators in the AR-based indoor positioning space are using hybrid approaches like this to maximize precision and scale of AR positioning technologies.

CONCLUSION

AR-based indoor navigation has had few cases and requires further technical development before it can roll out on a large scale, but all technological evidence indicates that it will be one of the major indoor positioning technologies of the future.

This entry concludes our blog series on Indoor Positioning, we hope you enjoyed and learned from it! In case you missed it, check out our past entries:

The Future of Indoor GPS Part 1: Top Indoor Positioning Technologies

The Future of Indoor GPS Part 2: Bluetooth 5.1′s Angle of Arrival Ups the Ante for BLE Beacons

The Future of Indoor GPS Part 3: The Broadening Appeal of Ultra Wideband

The Future of Indoor GPS Part 4: Read the Room with RFID Tags