Tag Archives: Machine Learning

Unleashing Molecular Magic: Machine Learning is Transforming Chemical Alchemy

machine learning and chemical alchemy
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With the growing need for computational power and analytical accuracy in various industries, machine learning has created a paradigm shift in the way we process, analyze, and make decisions based on vast amounts of data. In the chemical industry specifically, machine learning has brought about tremendous benefits in the development of new chemical compounds, formulations, and processes.

Machine learning has the potential to outperform traditional methods of chemical development by learning from existing data, predicting results for new scenarios, and continuously improving through iterative processes. This accelerates discovery and reduces the cost of the development of new chemicals. Moreover, machine learning algorithms can process large amounts of data in a fraction of the time it would take for humans, allowing for quicker and more efficient decision-making.

One of the main applications of machine learning in chemical development is in the prediction and optimization of chemical properties. By analyzing large datasets of existing compounds and their properties, machine learning algorithms can identify patterns and relationships between different chemical structures and their corresponding properties. This enables researchers to predict the properties of new compounds with a high degree of accuracy, saving time and resources in the development process.

Additionally, machine learning can also aid in the design of new chemical reactions. By analyzing existing reaction data, algorithms can suggest modifications or substitutions to improve yields and reduce side reactions. This not only speeds up the research and development process but also helps in creating more efficient and sustainable chemical processes.

The Influence of Machine Learning on Chemical Development

3D chemical compound

Revolutionizing Prediction Models

Machine learning has become the vanguard of chemical prediction, breaking free from the shackles of costly and time-consuming experiments. With its cutting-edge algorithms and statistical prowess, it empowers scientists to foresee the chemical properties of compounds and materials swiftly and with unparalleled precision. The result is a renaissance of novel chemical products, previously unfathomable in their complexity. Chemists today are pushing the boundaries of optimization, attaining levels of efficiency and cost-effectiveness that were once mere dreams.

Optimizing Formulations

Machine learning algorithms are the conductors in the orchestra of chemical formulations. They masterfully direct a symphony composed from enormous data sets, creating a melodious blend of chemical innovation. The fruits of this symphonic concoction are nothing short of miraculous: self-repairing polymers, solvents capable of capturing carbon, and robust coatings that stand undeterred in the face of extreme temperatures. These advancements are pushing the very limits of what we thought possible in material innovation.

Productivity Unleashed

Machine learning algorithms act as unwavering workhorses in the field of chemical development, taking the reins of the grueling tasks, and setting free scientists to ascend on the stratosphere of innovation and strategic planning. Consequently, productivity takes flight, as research and development luxuriate in the newly discovered realm of efficiency.

Unlocking New Frontiers

Machine learning’s discerning eye sifts through the avalanche of data to unearth novel chemical applications. It is the compass guiding companies towards uncharted territories, introducing groundbreaking products that cater to the overlooked needs of customers. For instance, consider Zymergen’s alchemy, where machine learning births industrial microorganisms without a touch of conventional chemistry, birthing materials that span the realms of industry, health, personal care, and agriculture.

The Elixir of Drug Discovery

cryptic chemistry set of elixirs

Streamlining the quest for drug discovery through machine learning possesses the oracle’s vision to predict the properties of chemical compounds, simplifying the arduous journey of identifying drug candidates. This leads to a lighter financial burden and a quicker pace, promising remedies and innovations at an unprecedented pace.

Designing Remarkable Materials

In the workshop of materials, machine learning is the virtuoso. Whether it be supercharged batteries, alchemical catalysts, or avant-garde polymers, it crafts tailor-made materials with properties precisely as desired.

Conjuring Chemical Reactions

Witness the sorcerer’s apprentice in the laboratory, where the alchemy of machine learning unfolds. Behold as the algorithm predicts the outcome of chemical reactions with uncanny precision and insight. Aided by its insights, chemists cast fewer experiments into the cauldron, preserving precious time and resources.

Guardian of Safety and Compliance

The ever-vigilant guardian of chemical processes, machine learning algorithms scrutinize copious data to ensure compliance with safety regulations and environmental standards. They ward off accidents and violations, ensuring a sanctuary of safety and integrity.

Healthcare

In the realm of healthcare, machine learning adorns the fabric of personalized medicine, customizing treatments to fit the intricate patterns of an individual’s genetic makeup and medical journey. It ushers in a utopia of healthcare, where the remedy is as exceptional as the patient themselves.

Challenges and Opportunities

Human face with DNA strand

While the benefits of machine learning are manifold, the journey toward fully

 implementing it into the chemical industry is not devoid of challenges. Machine learning, as efficient as it is, craves large, quality datasets – a requirement that can be daunting in an industry where data can sometimes be scarce or inconsistent in quality.

Moreover, the complexity of ML models can sometimes obscure their interpretation, a crucial aspect when it comes to understanding chemical processes and ensuring transparency. This opacity extends to the realm of regulatory compliance, where clarity is non-negotiable. Ethical concerns also arise when machine learning is applied in personalized medicine and drug discovery, particularly around issues of privacy, consent, and data security.

Ensuring that ML models can generalize to different chemical contexts is another challenge, essential for their broad applicability and reliability. However, these challenges, while formidable, are critical to unlocking the full potential of machine learning in the chemical industry. Addressing them will be instrumental in realizing the transformative impact of machine learning in chemical development, paving the way for a more efficient, sustainable, and innovative future for the industry.

Companies that leverage machine learning in chemical development

ExxonMobil: ExxonMobil is utilizing machine learning to improve its chemical manufacturing processes, making them more efficient and sustainable. They have also partnered with MIT’s Energy Initiative to advance research in the energy sector.

Novartis: Pharmaceutical giant Novartis has adopted machine learning for drug discovery, assisting in identifying potential treatments for various diseases. They have also collaborated with Google to develop an AI-based drug discovery platform.

BASF: German chemical company BASF is harnessing machine learning for predictive maintenance, reducing downtime and improving process efficiency. They have also established partnerships with various start-ups and research institutions to explore new applications of ML in the chemical industry.

Merck: Merck & Co is deploying machine learning across its research pipeline in the Boron complex, hoping to develop new drugs at a faster and more effective rate. It is evolving machine learning technology to understand and predict molecular noise.

Zymergen: Zymergen is a startup that integrates machine learning, software engineering, and robotic automation to identify, evaluate, and optimize microbial strains for omics applications. They create innovative materials for industrial, health, personal care, and agriculture quality products.

IBM: IBM’s AI research division is making substantial strides in the intersection of machine learning and chemistry. This global technology powerhouse is exploring ways to harness the power of AI in diverse areas of chemistry, including but not limited to drug discovery, materials science, and chemical optimization. Their innovative work is helping to expedite the design and synthesis of new molecules, opening up fresh possibilities in the world of chemistry.

Schrodinger: Schrodinger is at the forefront of incorporating machine learning into chemical research. With a keen focus on accelerating the pace of drug discovery and materials research, the company presents a range of software solutions that leverage machine learning to simulate and predict molecular properties. Their platform deftly combines computational capabilities with deep scientific insights, allowing researchers to delve deeper into the world of molecules and their myriad potentials.

Future Possibilities

Using VR to create new chemical compounds

The prospective applications of machine learning in the field of chemical development are vast and exciting. Here’s a glimpse into the directions where machine learning is poised to make substantial contributions:

Innovative Material Discoveries: Thanks to ML algorithms, we could see unprecedented materials with specially tailored properties, stirring innovations in domains like energy storage, catalysis, and sophisticated materials.

Environmental Considerations: ML technology could assist in making chemical processes greener by reducing waste, decreasing emissions, and ensuring adherence to strict environmental policies.

Robotics Integration: The fusion of machine learning with robotics could lead to self-governing experimentation, with robots executing experiments round-the-clock and using AI to assess outcomes and make informed decisions.

Energy Efficiency and Sustainability: ML can aid in the development of sustainable energy solutions and greener chemistry, which is essential for climate change mitigation.

Quantum Computing Integration: The amalgamation of quantum computing with machine learning could push the boundaries of solving intricate chemical problems by simulating quantum systems more effectively.

Enhanced Research Collaboration: Machine learning could facilitate faster analysis and sharing of research findings and data on a global scale, fostering worldwide collaboration among institutions.

alchemy set

In summary, the future trajectory of machine learning in chemical development points towards significant strides in research, innovation, and sustainability, steering the chemical industry towards heightened efficiency and environmental responsibility. As more companies and institutions explore the potential of machine learning, we can expect to see even more groundbreaking applications in this dynamic field. With its transformative capabilities, machine learning is paving the way for a brighter future for chemistry and all its allied industries.

Machine learning has emerged as a powerful tool in chemical development, providing significant benefits to the industry’s efficiency, accuracy, and innovation. The integration of machine learning and chemical development has opened up unprecedented possibilities that could revolutionize the world. In conclusion, it’s not a matter of whether machine learning will shape the chemistry industry but more of when and how it would shape the industry. Companies that do not integrate machine learning into their chemical processes risk being left behind in what is sure to become a volatile and dynamic market.

With its potential for groundbreaking advancements and significant contributions to sustainability, it’s clear that machine learning has a bright future in the world of chemistry. So, buckle up and get ready to witness the unprecedented transformation of the chemical industry with machine learning at its core.

The many ways machine learning has revolutionized the aviation industry

Augmented Reality and Aviation
Artificial Intelligence & Machine Learning, , , ,

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 Chatbots Make Healthcare More Efficient

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In the mid 1960s, Joseph Weizenbaum of the MIT Artificial Intelligence Laboratory created ELIZA, an early natural language processing computer program and the first chatbot therapist. While ELIZA did not change therapy forever, it was a major step forward and one of the first programs capable of taking the Turing Test. Researchers were surprised by the amount of people who attributed human-like feelings to the computer’s responses.

Fast-forward 50 years later, advancements in artificial intelligence and natural language processing enable chatbots to become useful in a number of scenarios. Interest in chatbots has increased by 500% in the past 10 years and the market size is expected reach $1.3 billion by 2025.

Chatbots are becoming commonplace in marketing, customer service, real estate, finance, and more. Healthcare is one of the top 5 industries where chatbots are expected to make an impact. This week, we explore why chatbots appeal to help healthcare providers run a more efficient operation.

SCALABILITY

Chatbots can interact with a large number of users instantly. Their scalability equips them to handle logistical problems with ease. For example, chatbots can make mundane tasks such as scheduling easier by asking basic questions to understand a user’s health issues, matching them with doctors based on available time slots, and integrating with both doctor and patient calendars to create an appointment.

At the onset of the pandemic, Intermountain Healthcare was receiving an overload of inquiries from people who were afraid they may have contracted Covid-19. In order to facilitate the inquiries, Intermountain added extra staff and a dedicated line to their call center, but it wasn’t enough. Ultimately, they turned to artificial intelligence in the form of Scout, a conversational chatbot made by Gyant, to facilitate a basic coronavirus screening which determined if patients were eligible to get tested at a time when the number of tests were limited.

Ultimately, Scout only had to ask very basic questions—but it handled the bevy of inquiries with ease. Chatbots have proved themselves to be particularly useful for understaffed healthcare providers. As they employ AI to learn from previous interactions, they will become more sophisticated which will enable them to take on more robust tasks.

ACCESS

Visiting a doctor can be challenging due to the considerable amount of time it takes to commute. Working people and those without access to reliable transport may prevent them from taking on the hassle of the trip. Chatbots and telehealth in general provide a straightforward solution to these issues, enabling patients to receive insight as to whether an in-person consultation will be necessary.

While chatbots cannot provide medical insight and prognoses, they are effective in collecting and encouraging an awareness of basic data, such as anxiety and weight changes. They can help effectively triage patients through preliminary stages using automated queries and store information which doctors can later reference with ease. Their ability to proliferate information and handle questions will only increase as natural language processing improves.

A PERSONALIZED APPROACH — TO AN EXTENT

Chatbot therapists have come a long way since ELIZA. Developments in NLP, machine learning, and more enable chatbots to deliver helpful, personalized responses to user messages. Chatbots like Woebot are trained to employ cognitive-behavioral therapy (CBT) to aid patients suffering from emotional distress by offering prompts and exercises for reflection. The anonymity of chatbots can help encourage patients to provide more candid answers unafraid of human judgment.

However, chatbots have yet to achieve one of the most important features a medical provider should have: empathy. Each individual is different, some may be scared away by formal talk and prefer casual conversation while for others, formality may be of the utmost importance. Given the delicacy of health matters, a lack of human sensitivity is a major flaw.

While chatbots can help manage a number of logistical tasks to make life easier for patients and providers, their application will be limited until they can gauge people’s tone and understand context. If recent advances in NLP and AI serve any indication, that time is soon to come.

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.

nlp

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

Artificial Intelligence & Machine Learning, , , , , , , , , , , , , , ,

“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?

openai-cover

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.

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.

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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

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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.

How Artificial Intelligence Has Revolutionized Digital Marketing

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Last week, we explored the real power of Artificial Intelligence. AI’s ability to comprehend complex data sets and form patterns enables infinite new possibilities for personalization through the analysis of digital activity. Within the digital marketing industry, AI has been nothing short of a revolution. Here are the top ways in which Artificial Intelligence is impacting digital marketing:

NATURAL LANGUAGE PROCESSING

Natural Language Processing (NLP) is a field that focuses on the ability for computers to process human language to the point where it can generate replies based on inferred meaning. Machine Learning has sharply increased the ability for machines to generate sentiments designed to not only seem as if they were written by a human, but that are optimized based on data to elicit a specific action or emotional response.

Digital marketers fret over when to reach out, what to say, and what channel is most appropriate. AI’s NLP abilities mean that the guessing game has come to an end. AI can analyze big data to decide upon what the best method, channel, and timing will be in order to foster growth, engagement, and sales.

NLP as a trend is on the rise. Angel.co recently valued the average NLP start-up at $4.8 million.

SEARCH FILTERING

In days of yore, Google search rankings were determined by human-created metrics and social media feeds showed posts in chronological order. Now, programs like RankBrain are vital to deciding the criteria for Google’s search rankings while Facebook’s DeepText creates your newsfeed.

ADVERTISING

Artificial Intelligence drives programmatic purchasing, which is when AI determines who to show ads to and when to show them. Removing the burden of purchasing analysis leaves marketers room to focus on crafting powerful messages.

NLP enables AI to understand (through numbers and sentiment analysis) the abstract criterion of “context” and to match individuals with ads based on context to maximize the chances of generating a click or purchase.

According to Ad Exchange, programmatic purchasing accounted for 67% of all global display ads in 2017.

PSYCHOGRAPHIC PROFILES

Perhaps the most anxiety-inducing example of Artificial Intelligence impacts not only digital marketing, but politics.

Psychographic profiles are data-driven psychological profiles of consumers designed to shed light on why they do what they do. Firms like CaliberMind and Cambridge Analytica have turned this into a multi-million dollar industry. Insights gleaned from psychographic profiles are intended to optimize the messaging of both political and commercial ads to induce a desired action from the viewer.

Cambridge Analytica has taken credit for influencing both the Brexit vote and the 2016 presidential election; however, many (including the New York Times) cast a shadow of doubt over the extent of their impact. Regardless, as long as there are insights to be gleaned from digital activity, psychographic profiles will only continue to develop.

SELF-DESIGNING WEBSITES

That’s right, AI has become adept enough to design websites based on data. Wix ADI created this personal trainer’s website and Grid has been designing websites since 2014.

CONCLUSION

Every application of artificial intelligence in digital marketing is relatively new. While these applications are increasing in popularity, expect them to also increase in efficiency and effectiveness as technology continuously advances.

The Real Power of Artificial Intelligence

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Technological innovations expand the possibilities of our world, but they can also shake-up society in a disorienting manner. Periods of major technological advancement are often marked by alienation. While our generation has seen the boon of the Internet, the path to a new world may be paved with Artificial Intelligence.

WHAT IS ARTIFICIAL INTELLIGENCE

Artificial intelligence is defined as the development of computer systems to perform tasks that normally require human intelligence, including speech recognition, visual perception, and decision-making. As recently as a decade ago, artificial intelligence evoked the image of robots, but AI is software not hardware. For app developers, the modern-day realization of artificial intelligence takes on a more amorphous form. AI is on all of your favorite platforms, matching the names and faces of your friends. It’s planning the playlist when you hit shuffle on Apple Music. It’s curating the best Twitter content from you based on data-driven logic that is often too complex even for the humans who programmed the AI to decipher.

MACHINE LEARNING

Currently, Machine Learning is the primary means of achieving artificial intelligence. Machine Learning is the ability for a machine to continuously improve its performance without humans having to explain exactly how to accomplish all of the tasks it has been given. Web and Software programmers create algorithms capable of recognizing patterns in data imperceptible to the human eye and alter their behavior based on them.

For example, Google’s autonomous cars view the road through a camera that streams the footage to a database that centralizes the information of all cars. In other words, when one car learns something—like an image or a flaw in the system—then all the cars learn it.

For the past 50 years, computer programming has focused on codifying existing knowledge and procedures and embedding them in machines. Now, computers can learn from examples to generate knowledge. Thus, Artificial Intelligence has already permanently disrupted the standard flow of knowledge from human to computer and vice versa.

PERCEPTION AND COGNITION

Machine learning has enabled the two biggest advances in artificial intelligence:  perception and cognition. Perception is the ability to sense, while cognition is the ability to reason. In a machine’s case, perception refers to the ability to detect objects without being explicitly told and cognition refers to the ability to identify patterns to form new knowledge.

Perception allows machines to understand aspects of the world in which they are situated and lays the groundwork for their ability to interact with the world. Advancements in voice recognition have been some of the most useful. In 2007, despite its incredibly limited functionality, Siri was an anomaly that immediately generated comparisons to HAL, the Artificial Intelligence in 2001: A Space Odyssey. 10 years later, the fact that iOS 11 enables Siri to translate French, German, Italian, Mandarin and Spanish is a passing story in our media lifecycle.

Image recognition has also advanced dramatically. Facebook and iOS both can recognize your friends’ faces and help you tag them appropriately. Vision systems (like the ones used in autonomous cars) formerly made a mistake when identifying a pedestrian once in every 30 frames. Today, the same systems err less than once in 30 million frames.

EXPANSION

AI has already made become a staple of mainstream technology products. Across every industry, decision-making executives are looking to capitalize on what AI can do for their business. No doubt whoever answers those questions first will have a major edge on their competitors.

Next week, we will explore the impact of AI on the Digital Marketing industry in the next installment of our blog series on AI.