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AI has allowed these startups to process vast amounts of patient data and drug data to find new drug treatments.
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Applied Technology Review | Tuesday, February 09, 2021
The pharmaceutical industry has been dominated by large pharmaceutical companies, often known as “big pharma”. This was for a very good reason. Developing drugs is incredibly expensive, time-consuming, and risky. Pharmaceutical companies spend hundreds of millions of dollars and years discovering new drugs, testing them, and then seeking regulatory approval. However, the majority of promising drug candidates fail to obtain regulatory approval because they do not have the necessary level of clinical benefit or have unacceptable side-effects. Artificial intelligence (AI) is changing the landscape by shortening discovery times whilst reducing the number of failed drug candidates.
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In recent years, AI has become ubiquitous with modern businesses. Far from the realms of science fiction, almost every sector and industry has been changed in some way using AI to automate previously manual processes that took humans far longer to carry out. From finance to agriculture, AI has been implemented to assist humans in their work, improving accuracy, decision making, and time efficiency.
The healthcare and especially the health tech industries are no different. Previously, healthtech companies developed traditional software technology to remind patients to take pills, facilitate virtual doctor’s appointments or allow those with diabetes to track blood sugar levels. Although these software applications are entirely useful, AI has now swept in and provided an entirely new and exciting opportunity for healthtech companies to interact with the pharma pipeline. Most importantly, the computing power of AI algorithms has specifically impacted the way healthtech companies can now enter the lucrative drug discovery, drug repurposing, and personalised medicine markets.
The growth of AI healthtech startups has given rise to a need for patenting of not just the computer software but also inventions derived using the software to protect startups from losing out on monetising their innovations. However, using AI to help facilitate invention or innovation has become a contentious issue in recent months with the DABUS AI inventor patent cases receiving media attention on the issue as to whether an AI platform can be named as an inventor in a patent application – the answer was a firm “No”! The important thing to note is that in most cases in healthtech AI is not actually inventing but rather facilitating and speeding up innovation. There is no question that you can patent the insights that AI provides.
The high barrier of entry to the pharma pipeline has been broken down by the introduction of AI that can do much of the leg-work operating on huge data sets using the power of modern computer processors, and at a fraction of the cost. What previously took the likes of AstraZeneca and GlaxoSmithKline thousands of iterations using hundreds of pharmacists and lab hours can now be done by a handful of data scientists and pharmacists with a computer and access to appropriate data sets. The ability to patent computer assisted discoveries allows AI startups in this field to quickly and securely monetise them to allow the company to become revenue generating.
FREMONT, CA: AI has allowed these startups to process vast amounts of patient data and drug data to find new drug treatments. For example, AI can be used to design the ideal structure for a completely new drug, by crunching data regarding the biological target. Al can also be used to match a disease with an unmet need with already-approved drugs, by analysing the complex pharmacology of drugs and the physiology of a disease. As every drug and disease has a profile, the computer can match the disease with a possible treatment. What the computer can do is match these elements rapidly and without stopping, whilst possibly learning which criteria are the most important. The silico data that AI provides may not necessarily yield new drug candidates, but there is no doubt it aids the drug discovery process by narrowing down the possible candidates and thus reducing the workload for the pharmacologists. It is an important tool.
The drug candidates that may be identified by AI still require real world testing, but the time to reach this point is shortened. Once the drug candidate has been identified and verified in the lab, patent applications can be filed in the usual way. This combination of real-world data and a patent application has significant value and can be taken to a large pharmaceutical company for partnering, for example. Big pharma are often best placed to finance the large scale clinical trials needed before a drug can be approved.
By using this strategy, both the tech startups and the big pharma “win”. The tech startup is able to deliver a partnerable asset in a realistic timescale (that often ties in with the investors’ requirements) and the big pharma saves money and time that they would have otherwise have needed to spend in early stage research (which for big pharma can be very costly due to the methods they use).
Entry for tech startups funded by venture capital to do drug discovery using AI is now far lower. Previously companies were having to raise millions of pounds just to get to the stage where it had a potential drug candidate. Investors faced the prospect of putting in large sums of money and gambling that an effective drug was found. Often this didn’t happen, and the investors would lose everything. Now with the use of AI, investors can fund a startup business with a much lower level of capital and with increased confidence that the technology is going to deliver effective solutions.
These new technologies are also applicable to vaccine development. Traditionally, vaccine development is very slow and very difficult, especially for certain viruses. Despite this, AI is still being trialled in the search for vaccines, with some early success being shown.
The key with AI is that the name somewhat misconstrues what it actually is. At present, AI is a complex algorithm or set of algorithms that churn through vast amounts of data to provide outcomes or insights. It is a tool. It does not answer a question, because it does not know what the question is. It does not invent. It assists pharmacists and data scientists in faster innovation to make discoveries.
It is important to train the machine on reliable data and this is why it is vital that data scientists are involved in training the algorithms on good, unbiased data. Large medical research institutions, including the NHS, have loads of health data to mine. These data can help them train the algorithms to spot patterns in certain data sets of certain cohorts of patients. However, should the wrong or incomplete data sets be used to train the algorithms then the outcomes will be unreliable.
There is a clear need for personalised medicine and one way to rapidly achieve this is through AI. Access to huge data sets and the ability to sift through vast quantities of it rapidly means that healthtech companies are able to develop personalised drug therapies. By looking at data for specific cohorts of people, AI algorithms are able to stratify patient populations and personalise therapies.
Ultimately, the large pharmaceutical companies will start to recruit the sort of people at these healthtech businesses. They will also start to partner with digital innovation specialists outside of the business that can broaden or deepen the expertise in handling data to find these inventions. If a pharmaceutical company fails to develop a digital technology division or capacity they will be left behind. AI has already changed the way many businesses operate and has successfully proven itself as indispensable in modern business. Now, AI is set to change the pharmaceutical industry through rapidly increasing the speed and range of drug discovery, supporting clinical trials, and driving personalised medicine, and allowing smaller healthtech firms to thrive alongside big pharma.
Interior design trends are changing, influenced by evolving visual tastes, cultural movements, technological progress, and shifts in lifestyle. The trends adapt to reflect people's changing needs and desires. While they might appear straightforward, the styles selected often carry more profound significance, reflecting personal and societal values. Interior design remains a dynamic field that is continuously evolving to align with the contemporary landscape and individual expressions.
Use of Color
Spaces can be made bigger, warmer, and calmer by color. Bright colors, such as 'Viva Magenta' 18-1750, make them stand out. With its vibrant hue, this bold hue illustrates how color plays a huge role in interior design. Color plays a vital role in creating a unique and captivating space, and this choice proves it.
Mixed Materials
Biophilic design is a popular trend in interior design, combining natural elements with beautiful textures and tones to enhance comfort and aesthetics. This style can be achieved by pairing multiple materials, such as stones, timber grains, tiles, and plants, to create a strong juxtaposition that communicates the space's function or a more prominent aesthetic theme.
Warm Colour Palettes
Bold colors dominate this year's trends, but warm color palettes are expected to become more prominent. Despite years of Covid-19 restrictions, people are now back to normalcy, yearning for more variety and comfort in their daily lives. Warm, earthy tones can create a sense of tranquillity in biophilic design, addressing the cravings of the new normal.
Geometric Shapes
The COVID-19 pandemic has prompted a shift in interior design towards minimalistic styles, requiring more vibrancy and life in spaces. Geometric patterns create solid visual features on walls, floors, and ceilings, creating a more balanced and visually appealing space. These patterns can develop focal points and add movement to the overall design, making spaces more dynamic and stimulating.
Sustainable Design
The design world embraces sustainable design, incorporating recycled materials, reused products, and long-term materials. This trend not only benefits the environment but also creates unique aesthetics. Businesses and places can demonstrate their commitment to reducing carbon footprint and promoting a wider and greener future. Sustainable design also creates visually appealing spaces that promote health and well-being for occupants, showcasing their commitment to a greener future. ...Read more
Sector convergence, which involves the breakdown of traditional divisions among several industries, is being accelerated by practical technology. This phenomena allows firms to leverage technology and proficiencies outside of their principal domain, fostering new business paradigms, value constellations, and opportunities.
Key Technological Catalysts
Several transformative technologies are serving as the primary drivers of industry convergence, providing the infrastructure and capabilities that enable cross-sector collaboration and the creation of new value. The Internet of Things (IoT) connects physical assets to digital networks, generating vast streams of data that integrate physical and virtual operations. For example, smartwatches and fitness trackers, initially consumer electronics, now serve the healthcare sector by supporting remote patient monitoring and preventative care. Artificial Intelligence (AI) and Machine Learning (ML) build on this data by enabling advanced analytics, driving smarter decision-making, and delivering hyper-personalized services across various industries. Retailers utilize AI to predict consumer trends, optimize supply chains, and personalize shopping experiences. At the same time, financial institutions leverage it for fraud detection and algorithmic trading, thereby blurring the boundaries between technology and traditional banking. Blockchain adds another dimension by offering a secure, transparent framework for managing transactions and data across multiple parties, streamlining cross-sector collaboration in areas such as supply chain management by reducing reliance on intermediaries. The rollout of 5G connectivity provides the speed and low latency necessary to support these technologies at scale, enabling real-time communication between devices and seamless integration across various industries. Autonomous vehicles, for instance, depend on instantaneous connectivity with smart city infrastructure and other cars, exemplifying the convergence of automotive, telecommunications, and urban planning.
Impact on Business and Society
Sector convergence is profoundly altering conventional business paradigms. A single product or service no longer defines enterprises; instead, they are evolving into comprehensive ecosystems that deliver an array of integrated solutions. This evolution fosters novel opportunities for innovation, concurrently introducing complexities such as navigating intricate regulatory frameworks and managing data privacy across disparate sectors. From a consumer perspective, this convergence facilitates enhanced convenience, personalization, and seamless experiences; however, it also raises concerns regarding data security and market dominance. As the trajectory of applied technology continues its advancement, the demarcations between industries will inevitably diminish, thereby ushering in a future characterized by interconnected and integrated services.
Ultimately, applied technology transcends mere efficiency; it represents a fundamental force for change, reshaping the very structure of our economy. The future will be defined by ecosystems of integrated services, where companies succeed not by dominating a single sector, but by seamlessly connecting their offerings with others. This era of convergence promises unprecedented innovation and convenience for consumers. Yet, it also necessitates a proactive approach from businesses and policymakers to navigate the challenges of regulation, data privacy, and market power. Embracing this paradigm shift is crucial for companies seeking to develop in a world where the distinctions between sectors no longer exist. ...Read more
Artificial intelligence and machine learning are proving to be very useful for weather prediction, as they are in many other fields of science. Machine learning has shown some potential in our research on forecasting high-impact weather. Researchers also believe these tools open up new possibilities for better forecasts where more than human expertise is required.
There still are major challenges with employing just human expertise. It remains difficult to predict tornadoes, large hail, or heavy rain during thunderstorms. Inaccurate predictions trigger subsequent inaccuracies in predicting small changes in complex processes, making weather less predictable.
Models: Numerical weather prediction models are today's primary tools for weather forecasters. Weather stations, balloons, and satellites provide observations of the current state of the atmosphere for these models, which solve equations governing air movement.
It is possible to predict most weather systems with these models, but the smaller the event, the harder it is to do. Integrating weather prediction models with human analysis leads to better predictions. Forecasters are remarkably good at analyzing the huge amount of weather data they receive daily, but their memory and bandwidth could be improved. Machine learning and artificial intelligence can help with some of these challenges.
Artificial intelligence (AI): These tools allow forecasters to make predictions beyond models' capabilities, such as high-impact weather. A machine learning approach is also being incorporated into numerical weather prediction models to speed up tasks that can be computationally intensive, such as predicting how water vapor turns into rain, snow, and hail.
Machine learning (ML): In the future, machine learning models may replace traditional numerical weather forecasting models. These systems process past weather maps instead of solving complex physical equations as models do to learn how weather systems typically behave. They then make weather predictions based on historical data and current weather conditions.
Research has shown that machine learning-based forecast systems can predict general weather patterns and numerical weather prediction models with only a fraction of the computing power. Researchers are closely testing these new tools and developing new methods for forecasting local weather. Although they still need to provide detailed information about local weather, these tools have promising prospects. ...Read more
Submarine optical fiber technology forms the backbone of the modern global communication network, enabling seamless data transmission across continents. These undersea cables are pivotal in supporting internet, telecommunication, and financial systems by transmitting vast amounts of data at high speeds and low latency.
The concept of submarine communication dates back to the mid-19th century with the installation of the first undersea telegraph cable between England and France in 1850. However, it wasn’t until the 1980s, with the advent of fiber-optic technology, that undersea cables began to approach their modern potential. Fiber-optic cables revolutionized communication by using light signals to carry data, achieving unprecedented bandwidth and reliability compared to earlier copper-based systems.
The first submarine fiber-optic cable, TAT-8, was deployed in 1988, connecting the United States, the United Kingdom, and France. With a capacity of 280 Mbit/s, it was a groundbreaking advancement. Since then, the technology has evolved rapidly, with modern cables capable of transmitting terabits of data per second.
Today, submarine optical fiber cables are the foundation of global connectivity, carrying approximately 99 percent of international data traffic. They support the internet, telephone communications, and financial systems, underpinning activities from social networking to realtime stock trading.
One of the key advantages of submarine cables is their ability to offer low-latency communication over vast distances. This feature is crucial for applications like video conferencing, cloud computing, and e-commerce, where delays can significantly impact user experience and functionality.
The geographic distribution of submarine cables is closely tied to global economic activity. Major routes connect hubs like North America, Europe, and Asia, while regional cables serve to link smaller markets. Recent efforts have focused on expanding connectivity to underserved regions, particularly in Africa and the Pacific islands, to bridge the digital divide.
Despite their critical importance, submarine cables face several challenges. Physical damage from natural events like earthquakes and human activities like fishing and anchoring is a significant concern. For instance, the 2006 Hengchun earthquake in Taiwan caused extensive damage to cables in the region, disrupting internet services across Asia.
Cables are also vulnerable to intentional acts of sabotage. Given their critical role, they are increasingly recognized as strategic assets, and their protection has become a priority for governments and organizations worldwide.
Another challenge is the high cost of installation and maintenance. Deploying a submarine cable involves complex logistics, including surveying the ocean floor, manufacturing the cable, and laying it using specialized ships. Once operational, cables require regular maintenance, particularly in shallow waters where the risk of damage is higher.
The submarine optical fiber industry is continuously evolving to meet the growing demand for bandwidth and reliability. One area of innovation is the development of ultra-highcapacity cables. These systems leverage advancements in optical amplification and multiplexing technologies to transmit data at rates exceeding 100 terabits per second.
Another focus is improving the resilience of cables to physical damage. Innovations include stronger armoring materials and the development of monitoring systems that can detect and locate potential threats in real time. Advances in cable repair technology are also reducing downtime when damage occurs.
The integration of artificial intelligence (AI) and machine learning into cable network management is another emerging trend. These technologies enable predictive maintenance and optimize data traffic, enhancing the efficiency and reliability of submarine cable systems.
While submarine cables are generally considered environmentally benign, their installation and operation can have localized effects on marine ecosystems. Laying a cable involves disturbing the seabed, which can impact benthic habitats. To mitigate these effects, environmental assessments are conducted before installation, and routes are carefully planned to avoid sensitive areas.
During their operational life, submarine cables pose minimal risk to marine life. However, ongoing research aims to better understand and address any potential long-term impacts, ensuring that this critical infrastructure remains sustainable.
Looking ahead, the demand for submarine optical fiber technology is expected to grow as internet penetration increases globally and data consumption rises. The advent of technologies like 5G, the Internet of Things (IoT), and artificial intelligence will further drive the need for robust and high-capacity communication networks.
Emerging trends include the deployment of regional and intracontinental cables to enhance local connectivity and reduce dependence on intercontinental links. Projects like the 2Africa cable, which aims to encircle the African continent, exemplify this shift.
Another area of focus is the integration of renewable energy solutions into cable operations. Powering repeaters along submarine cables is a significant energy requirement, and exploring sustainable energy sources could reduce the environmental footprint of these systems.
Submarine optical fiber technology has transformed global communication, enabling the seamless exchange of information across continents and underpinning the digital economy. As the demand for data continues to grow, this technology will remain at the forefront of connectivity, evolving to meet new challenges and opportunities. By addressing issues like physical resilience, environmental impact, and energy efficiency, submarine cables will continue to play a vital role in shaping the future of global communication networks. ...Read more