What Is Deep Learning And How Does It Work? Explained

A few years ago, the idea of artificial intelligence was futuristic. In fact, if someone told you that there would be self-driving cars, you would easily brush that off as a wishful thought. A few years later, we are deep amid artificial intelligence, machine learning, and the complex neural network architecture that runs deep learning. These developments have given rise to new careers and tech, increasing the need for experts in the vast field. Deep learning mimics the human brain to create sophisticated tasks that can perform tasks autonomously, which could soon revolutionize our world through intelligent automation technologies used virtually everywhere.

What Is Deep Learning: Complete Explanation

There is a saying that with age comes experience. As people age, they gain more life experiences and lessons. This is not that different from how AI-powered machines learn through the data fed into them. Therefore the output you get depends on the quality and quantity of data that runs through the system. In artificial intelligence, machines use deep learning to perform tasks like driving cars without human help.

Deep Learning: An Exact Definition

Deep learning is a branch of machine learning that focuses on learning and perfecting its output by utilizing artificial neural networks. This field borrows a lot from how humans process information and learn. It is an expansive field that goes beyond standard computing capabilities to complex structures, algorithms, and systems that work towards autonomous functionality in various fields. Through deep thinking, computers can learn and react to situations more efficiently than humans. This explains why computers are now used in driving cars, language translation, and image classification applications.

© Provided by History Computer Deep learning allows computers to react and adapt to situations more quickly and efficiently than humans. ©Blue Planet Studio/Shutterstock.Com The History of Deep Learning

Deep learning has evolved rapidly over the past few decades, with its roots tracing back to the 1940s. In its initial phase, researchers focused on neural networks that mimicked specific parts of how our brains operate. However, due to limited processing power and data, this pioneering phase lost momentum after a while. The re-emergence of deep learning came naturally in parallel with the tremendous amounts of data being generated in today's digitalized world matched by efficient processors that could perform massive calculations within seconds.

Scientists initially tried using these powerful new tools for simple speech recognition challenges. Eventually, they learned that their capabilities extended much farther than identifying individual words spoken aloud or written on paper. In a short time, deep learning had accomplished remarkable breakthroughs, e.G., identifying objects accurately in images nearly as well as we humans do it. And just like our eyes learn features at different levels, from edges to shapes to texture, these deep nets have succeeded well beyond expectations. Predictive text suggestions also experienced an overhaul due to natural language processing models' better representation. Of course who can forget AlphaGo — Google's AI-powered software beating world-champion Go players without breaking a sweat? Researchers continue trying several novel approaches to improve already-perfected techniques and expand deep-learning usage in other domains.

How Does Deep Learning Work?

Think about how we learn something new. Let's say we're trying to recognize our friend's face. We might start by looking at a picture of them and labeling each part of their face, eyes, nose, mouth, etc. Next, we might compare that image to others and look for similar features. Over time, as we see more pictures of our friends or even different people with similar facial features, our brain gets better at recognizing them. This concept forms the basis of deep learning, using algorithms inspired by the human brain to recognize patterns in data.

Essentially, deep learning models develop their understanding and categorization system as they analyze more data. One commonly used method is through neural networks, interconnected layers of nodes that process information from previous layers before outputting an answer. These layers are then adjusted through a training process until accurate results are achieved. But it doesn't stop there.

Thanks to advancements in technology and access to huge amounts of data, there are endless possibilities for deep learning applications. These include natural language processing such as chatbots, image recognition, and even healthcare decisions (making medical diagnoses).

What Are the Applications of Deep Learning?

Deep learning has various applications, including:

Computer vision: Deep learning enables machines to recognize objects and images, which helps researchers solve many scientific problems like facial recognition or autonomous vehicle navigation.

Customer experience: Businesses now utilize chatbots to respond promptly to customers' queries, reducing the turnaround time. Chatbots use deep learning to provide accurate feedback based on available data. The prompt response improves customer satisfaction and brand reputation.

Text generation: Content creators and marketing specialists are now leveraging the immense capabilities of Natural Language Processing (NLP) which use deep learning to generate texts. As technology advances, we expect better text outputs without grammar errors.  

Aerospace and military: military and aerospace sectors leverage deep learning to detect objects in space and provide crucial feedback for decision-making.

Industrial automation: Besides automating processes in industries and warehouses, machines now operate through deep learning to protect workers from the risks of being hurt by machines.

Medical research: Thanks to technologies like deep learning, the healthcare sector has made massive strides through technology. Researchers can now implement this technology in detecting complex cancer cells in the early stages.

© Provided by History Computer ChatGPT is an artificial intelligence chatbot designed to interact in a conversational way , just as humans would. ©CHUAN CHUAN/Shutterstock.Com Examples of Deep Learning in the Real World Agriculture

Farmers have the advantage of leveraging AI in modern farming. Weeding and spraying pesticides has been easy with machines that identify and differentiate crops from weeds. Large-scale farming is now effortless thanks to machines that use deep learning to apply fertilizers, run irrigation systems, and bring in the harvest. It is also possible to predict yields following rain patterns and soil moisture. This allows farmers to plan their resources well. Through deeper learning algorithms, it is also possible to capture pests before they damage crops and thus mitigate the effects.

Robotics

Recently, we have seen a rise in robot applications. This growth can be attributed to deep learning and advancement in artificial intelligence. Robots can sense and react to their surroundings using artificial intelligence. That's why they can pick objects to avoid obstacles and navigate their path without collisions. The sensor robots use leverage deep learning to guide their operations. As this field advances, we expect robots to continue being effective and efficient in performing tasks just like humans. 

Healthcare and Medical Imaging

Medical advancements are continuously pushing technology boundaries, and deep learning offers incredible possibilities. Medical imaging has been simplified and improved thanks to the unique combination of high-quality data and convolutional neural. Unlike a few years ago, cancers are easier to detect in their early stages, paving the way for timely treatments.

Final Thoughts

Technology has paved the way for things we never imagined possible a few years ago. No one ever thought of the possibility of machines that could mimic human brains and perform tasks independently without the need for human help. But that reality is here with us. Deep learning stretches the limits of artificial intelligence and computing power to release incredible performance in industries like medicine, agriculture, and the military.

The recent concept of self-driving cars that has taken the world by storm has its basis in deep learning. The complex deep learning and artificial intelligence frameworks have revolutionized most sectors. As deep learning models evolve with more complex and advanced architectures, we expect incredible performance and capabilities in the near future.

The post What Is Deep Learning and How Does It Work? Appeared first on History-Computer.

Are AI Deep Learning Models The Future Of Environmental Conservation?

Unsustainable practices pose longstanding threats to our deciduous forests, while issues such as water drainage, pollution and improper use further endanger the country's vital wetland ecosystems. In Maryland, a research team is committed to using AI maps that contribute to the conservation of both these terrains. 

The data science team at the Chesapeake Conservancy, based in Annapolis, already has experience transforming soil-related information into data. In early 2023, it introduced an innovative "AI wetlands model" that showed promising results in addressing the longstanding challenges associated with wetlands protection and conservation. Now, the organization's research team has developed an advanced deep-learning model that uses satellite imagery to automatically map ground-mounted solar arrays.

By employing this AI-driven system, the team created annual maps from 2017 to 2022 to assess the solar capacity, or the number of solar panels in given areas. The researchers found that solar capacity has been expanding at different rates across DC, Delaware, Maryland, Pennsylvania, New York, Virginia and West Virginia. Notably, Maryland and Virginia have experienced the most rapid growth in solar capacity. This growth is initiated by the installation of solar arrays, which are interconnected rows of solar panels often visible in fields during flights or drives through rural areas (and even on rooftops in urban settings).

In accord with the Chesapeake Conservancy's findings, solar arrays have primarily replaced cultivated areas like agricultural fields, pastures and timberlands while avoiding such natural land covers as deciduous forests and wetlands. Michael Evans, a senior data scientist with Chesapeake Conservancy's Conservation Innovation Center, said that this trend points toward something positive.

"From a conservation perspective, we were encouraged to see natural areas being avoided over the past six years," he said. "In places where solar is replacing the previously degraded land cover, these facilities may also present a unique opportunity to restore biodiversity and ecosystem services in the watershed if native vegetation is maintained in areas under and around arrays."

The findings indicate that agriculture has been the most frequently converted land use for solar, showing the highest level of selection in terms of area converted relative to the available area. This is beneficial because it helps avoid further endangerment or threats to forests and wetlands by the human population.

"These data reveal the land use transitions associated with past solar energy growth and the transitions we're likely to see in the future if those trends continue," Evans added. 

Arrays mapped for the Chesapeake Bay watershed, with colors indicating the year they were constructed. (Courtesy image)

The Chesapeake Conservancy research team ultimately conducted a study on predicting patterns of solar energy development and its potential impact on biodiversity conservation. The culminating detailed publication, "Predicting Patterns of Solar Energy Buildout to Identify Opportunities for Biodiversity Conservation" is available online.

Read the publication

The Chesapeake Conservancy also broke down its team's methodology for determining the locations of most solar arrays in the Chesapeake Bay watershed in an announcement. Since not everyone has expertise in geospatial technology or data science, we've further simplified the method using ChatGPT. Here's an edited version of what it said:

The research team studied how solar energy has been built in the past and looked at different characteristics of the areas where solar panels were installed.

It collected information about things like the slope of the land if it was protected and the types of land cover like forests or open areas. It also looked at the distance to roads and power lines, as well as factors like population, income and agriculture suitability.

The team used all this information to create a computer model that predicted where solar energy might be built in the future. It found that solar panels were more likely to be installed in places previously used for farming, close to roads and unsuitable for agriculture. They were less likely to be built in steep areas, protected places and areas with lots of trees.

Using the model's calculations, the team made maps that showed how long it might take for solar energy to be developed in different locations across five states and DC. These maps can be seen on a public website:  

View the maps

Deep Learning & AI Igniting Live Cell Imaging Market Expansion The Market To Worth $3.54 Billion By 2028 – Arizton

The research study on the live cell imaging market – global outlook & forecast market 2022-2027, recently published by Arizton Advisory & Intelligence, states that the market will grow at a CAGR of 8.44% during 2021-2027.

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The global live-cell imaging market growth is primarily driven by the increasing prevalence of cancer and infectious diseases, which require innovative therapies. Additionally, the demand for cell biology studies and the growing awareness of stem cell therapy contributes to live-cell imaging technologies' adoption.

Researchers from pharmaceutical and biotech companies, academic institutions, and research centers heavily rely on fluorescence microscopy techniques, resulting in a high demand for live-cell imaging systems. Furthermore, integrating artificial intelligence (AI) and machine learning in live-cell imaging will drive the market's growth in the coming years.

The global live-cell imaging market is segmented based on product, technique, application, end-user, and geography. In 2021, the equipment segment accounted for the largest share of the market. However, during the forecast period, the consumables segment is projected to witness the highest CAGR of 9.35%. This growth can be attributed to the continuous need for consumables such as reagents, probes, and assays in live-cell imaging experiments.

Overall, the expanding global live-cell imaging market signifies the importance of these technologies in various fields. With advancements in AI and machine learning and the rising demand for novel therapies and cell biology research, live-cell imaging is poised to play a crucial role in driving advancements in healthcare and life sciences.

Live Cell Imaging Market Report Scope

Report Attributes Details Market Size (2027) USD 3.54 Billion Market Size (2021) USD 2.18 Billion CAGR (2022-2028) 8.44% Base Year 2021 Forecast Year 2022-2027 Market Segmentation Product Type, Technique Type, Application Type, End-User Type Geographic Analysis North America, Europe, APAC, Latin America, and Middle East & Africa Market Dynamics

Rising Number of Patients with Cancers & Infectious Diseases

Adoption Of High Content Screening Techniques in Drug Discovery & Precision Medicine

Newer Imaging Techniques Enhancing the Imaging of Live Cells

Growing R&D Activities in The Pharmaceutical Industry

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Recent Developments in the Global Market

In 2021, CytoSMART launched CytoSMART Lux3 BR, a new type of bright-field microscope, i.E., a live-cell imaging microscope equipped with a high-quality CMOS camera to assist label-free cell imaging procedures.

In 2021, the Zeiss group announced that they would launch Zeiss Visioner 1, a Zeiss live cell imaging system, an innovative digital microscope that facilitates real-time all-in-one focus via a micro-mirror array system.

In 2020, CytoSMART Technologies launched CytoSMART Multi Lux, a remote live cell imaging system.

Geographical Analysis

The global live cell imaging market is poised for substantial growth, with North America leading. This growth is primarily driven by increased healthcare spending and funding for research and development in live cell-driven drug discovery, development, and personalized medicine.

Europe holds the second-largest share in the global market, driven by a growing patient population requiring innovative treatments like stem cell therapy and gene therapy. Additionally, the region benefits from a surge in drug approvals for precision medicine, government funding for research-based activities, rapid advancements in live cell imaging, and several other contributing factors.

The Asia-Pacific (APAC) region is expected to witness the fastest growth in the global live cell imaging market. Key factors contributing to this growth include a continual rise in the prevalence of cancers and infectious diseases, increasing demand for stem cell research studies, rising research and development expenditures, the growing utility of biomarkers for diagnostic purposes, heightened awareness of cell and gene therapies, the need for precision medicine, and advancements in drug discovery, cell biology, and development.

On the other hand, Latin America and the Middle East & Africa regions currently hold minimal shares in the global market for live cell imaging.

The expansion of the live cell imaging market indicates the increasing importance of these systems in various sectors. They play a crucial role in diagnostics, drug discovery and development, and precision medicine. As research and technological advancements progress, live cell imaging systems are expected to become even more essential tools in healthcare and life sciences.

Market Trends & Opportunities

Deep Learning & Artificial Intelligence

The role of Artificial intelligence (AI) in life science is rapidly expanding and holds great potential for microscopy. In the past, the power of microscopy for supporting or disproving scientific hypotheses got limited by scale, and the time associated with quantifying, capturing, and analyzing large numbers of images was often prohibitive. Recently, AI has made fast inroads into many scientific fields and the world of microscopy. AI-based self-learning microscopy shows the potential to produce high throughput image analysis that is more effortless and less time-consuming. Newer AI technology allows better visualization of unlabeled live cells over a prolonged period.

Increase in Funding for Cell & Gene Therapy

The demand for regenerative medicine has increased across developed countries, and investments in cell & gene therapy have grown drastically in recent years. The public and private sectors are at the forefront of funding cell and gene therapy developers. Recently, many government organizations and private firms have started funding many biotech start-ups and research institutes that invest in the R&D of cell and gene therapy products. According to the Alliance for Regenerative Medicines, there was a 164% jump in funding for cell & gene therapy in 2019 compared to 2017.

Advancements & Newer Imaging Techniques

Live cell imaging arises from scientific interest and improvements in imaging and labeling technology. Combining various technological advancements with biological interests gives scientists many more ways to use live cell imaging. In particular, exciting progress in probe development has enabled a broad array of nucleic acids, proteins, glycans, lipids, ions, metabolites, and other targets to be labeled. Many recent advancements in microscopic technologies use software that enables a better quantitative image analysis of label-free images.

Also, current microscopy techniques limit the quantity and quality of information available to researchers and clinicians and harm living cells during long-term studies. Hence new imaging technologies are being developed to overcome various limitations. These advancements will help towards future market growth. For instance, the progress of combining 3D fluorescence imaging and holotomography microscopy has overcome some limitations.

Key Vendors

Danaher

Agilent Technologies

PerkinElmer

Merck KGaA

ZEISS

Thermo Fisher Scientific

Axion BioSystems

BD

Bio-Rad Laboratories

Blue-Ray Biotech

Bruker

Eppendorf

Etaluma

Grace Bio-Labs

ibidi GmbH

Intelligent Imaging Innovations

KEYENCE

Logos Biosystems

NanoEntek

Nanolive SA

Nikon

Evident

ONI

Oxford Instruments

Phase Focus

Phase Holographic Imaging PHI AB

Proteintech Group

Sartorius AG

Sony Biotechnology

Tomocube

Market Segmentation

Product: Equipment, Consumables, and Software

Technique: Fluorescence microscopy, Confocal microscopy, Transmitted light microscopy, and Others

Application: Cell Biology, Drug Discovery, Developmental Biology, and Stem Cells

End-user: Pharma & Biotech Companies, Academic & research centers, and Others

Geography: North America, Europe, APAC, Latin America, and Middle East & Africa

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Arizton's Expertise in the Healthcare Industry

Arizton offers syndicate and custom market intelligence & competitive intelligence solutions across MedTech, pharmaceuticals, and life sciences & biotechnology markets within the healthcare industry. Arizton's healthcare vertical provides unbiased research and evidence-based analysis to support business decisions and comprehensive end-to-end solutions to its customers, covering all dimensions of the healthcare value chain. We track various devices (equipment, implants, and hospital supplies) and drugs (patented, generic, & OTC) across North America, Europe, APAC, Latin America, and the Middle East & Africa regions. The healthcare reports provide historic and forecast data for 20+ key countries worldwide. We track product approvals/launches, M&A activities, and collaboration/partnership activities among pharma/biotech, medical device, and life sciences companies. Our pharmaceutical portfolio tracks 15+ therapy areas with a major focus on immunology, oncology, rare & genetic diseases, dermatology, and metabolic diseases. Our medical devices and life sciences portfolio covers in-vitro diagnostics, healthcare IT, patient monitoring, cardiovascular devices, medical imaging, personal protective equipment, clinical laboratory services, and CMO/CDMO services.

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