Category Archives: Machine Learning
BharatGPT groupsupported by Reliance Industries, owned by Mukesh Ambani, and the best engineering colleges in Indiaplans to launch its first ChatGPT-style service, marking a major milestone in the countrys efforts to develop its own artificial intelligence (AI).
A preliminary view of the concept was presented during a recent technology conference in Mumbai by the partnership, which includes eight IITs and Reliance. Hanooman, the model, showed a motorbike repairman speaking Tamil to the AI bot, a banker speaking Hindi, and a developer from Hyderabad writing code to the tool.
The approach will be implemented in four domainseducation, healthcare, financial services, and governanceand eleven local languages.
ReadTop 20 Uses of Artificial Intelligence In Cloud Computing For 2024
During the annual Nasscom IT industry conference, Ganesh Ramakrishnan, chair of the Department of Computer Science and Engineering at IIT Bombay, was quoted by Bloomberg as claiming that the model Hanooman will also include speech-to-text capabilities, making it much more user-friendly.
It is worth mentioning that similar open-sourced AI models tailored to the country are being developed by other startups, such as Krutrim and Sarvam, with support from well-known VC firms like Lightspeed Venture Partners and the fund of billionaire Vinod Khosla.
According to the article, Reliance Jio intends to construct bespoke models for certain purposes. The 450 million customers that make up the telecom-to-retail conglomerates network will be able to use artificial intelligence on the Jio Brain platform.
Read the Latest blog from us:AI And Cloud- The Perfect Match
Systems that can learn from massive amounts of data and produce responses that sound natural are known as Large Language Models (LLMs). These models make use of generative AI, a very young AI technique that has gained popularity because to OpenAIs ChatGPT.
ReadOpenAI Open-Source ASR Model Launched- Whisper 3
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Unleash the Power of BharatGPT Hanooman - AiThority
Navigating the AI and Machine Learning Revolution: A Comprehensive Overview – AutoGPT Official – AutoGPT
The AI and machine learning revolution is transforming various industries and having a significant impact on society. AI and ML technologies are becoming increasingly important, driving innovation and creating new job opportunities. In this comprehensive overview, we will explore the evolution of AI and machine learning, debunk common myths, discuss their applications, navigate the job market, consider ethical considerations, and speculate on the future of these technologies.
Artificial intelligence has come a long way since its inception. It began as a concept of creating machines that can mimic human intelligence and perform tasks that require human intelligence. Over time, advancements in technology and computing power have enabled the development of AI systems that can learn from data and improve their performance. This evolution has led to the emergence of machine learning, a subset of AI that focuses on algorithms and models that allow computers to learn and make predictions or decisions without being explicitly programmed.
Machine learning has its roots in the 1950s and 1960s, with the development of early AI models and algorithms. However, it wasnt until the late 1990s and early 2000s that machine learning gained significant traction, thanks to the increase in data availability and computational power. This led to the rise of deep learning, a subset of machine learning that focuses on neural networks and mimicking the structure and function of the human brain.
Artificial intelligence and machine learning are closely related. In fact, machine learning is a key component of AI systems. AI systems use machine learning algorithms to analyze large amounts of data and identify patterns and trends that humans may not be able to detect. Deep learning, on the other hand, is a subset of machine learning that focuses on neural networks and processing complex data such as images, videos, and text.
While artificial intelligence and machine learning are related, they are not the same. AI is a broader concept that encompasses various technologies and approaches, including machine learning. Machine learning, on the other hand, is a specific approach within AI that focuses on algorithms and models that allow computers to learn from data.
As the AI and machine learning revolution continues to unfold, it is important to understand the evolution of these technologies, their applications, and the ethical considerations associated with their use. By navigating the job market and staying informed about the latest advancements, individuals and organizations can fully leverage the benefits of AI and machine learning while addressing the challenges and ensuring responsible and ethical use.
The evolution of artificial intelligence (AI) and machine learning (ML) has been remarkable. It started with theoretical models and basic algorithms and has now evolved into powerful and versatile technologies. AI refers to the development of computer systems that can perform tasks requiring human intelligence, while ML focuses on algorithms that allow computers to learn from data and make predictions or decisions without being explicitly programmed.
Throughout the history of AI and ML, there have been key milestones and breakthroughs that have shaped their development:
Its important to note the relationship between AI, ML, and deep learning. ML is a subset of AI, and deep learning is a subset of ML. Deep learning focuses on neural networks and mimicking the structure and function of the human brain, enabling AI systems to process unstructured data such as images, videos, and text.
While AI and ML are related, they are not the same. AI encompasses a broader range of concepts and technologies, including ML, but also other approaches such as expert systems and natural language processing .
There are several common misconceptions about AI and machine learning that need to be addressed. Lets dispel these myths and highlight the positive aspects and potential benefits of AI and machine learning.
One of the biggest myths surrounding AI and machine learning is the fear that they will replace human jobs. While it is true that AI and machine learning can automate certain tasks, they also create new job opportunities and enhance human productivity. For example, in the manufacturing industry, robots and automation have taken over repetitive and dangerous tasks, allowing humans to focus on more complex and creative tasks. This has led to increased efficiency and productivity in many industries.
AI and machine learning also have the potential to create entirely new job roles that didnt exist before. As AI technology continues to advance, there will be a growing need for AI specialists, data scientists, and machine learning engineers who can develop and implement AI solutions. These roles require a deep understanding of AI and machine learning algorithms, as well as the ability to analyze and interpret large amounts of data.
Another myth is the idea of superintelligence, where machines surpass human intelligence and control the world. While AI and machine learning have made significant advancements, they are still tools that are designed to work alongside humans and augment their capabilities. They are not self-aware beings with their own desires and motivations. AI and machine learning algorithms are programmed to perform specific tasks based on the data they are trained on. They do not possess the ability to think or reason like humans do.
In fact, AI and machine learning are often limited in their capabilities and rely on human input and guidance to function effectively. For example, in self-driving cars, AI algorithms can analyze sensor data and make decisions based on predefined rules, but human drivers are still needed to handle complex situations and make critical judgments. This collaboration between humans and AI systems is essential for successful implementation and adoption.
By dispelling these myths and providing evidence of the positive aspects and potential benefits of AI and machine learning, we can encourage a more informed and realistic understanding of these technologies. AI and machine learning have the potential to revolutionize various industries and sectors, improving efficiency, accuracy, and decision-making. However, it is important to approach their development and implementation with ethical considerations in mind, such as privacy, bias, and accountability.
AI and machine learning are not about replacing human jobs or creating superintelligent machines. They are tools that can enhance human productivity, create new job opportunities, and revolutionize various industries. By understanding the true potential and limitations of AI and machine learning, we can navigate the revolution with confidence and harness the benefits of these technologies.
AI and machine learning have revolutionized various industries, offering a diverse range of applications. In this article, we will explore how these technologies are being utilized in the healthcare, finance, and transportation sectors.
By analyzing large volumes of medical data, AI algorithms can identify patterns and make accurate predictions, leading to more efficient and effective healthcare outcomes.
AI and machine learning technologies can analyze vast amounts of financial data to detect fraud, make accurate predictions, and optimize investment strategies. They can identify fraudulent activities and potential risks, enabling financial institutions to take necessary actions to protect their customers and assets. AI-powered investment strategies can provide valuable insights and recommendations to help investors make informed decisions and maximize their returns.
AI algorithms can analyze real-time traffic data and optimize routes, reducing congestion and improving overall efficiency. Autonomous vehicles, powered by machine learning, can navigate roads and make decisions based on their surroundings, leading to safer and more reliable transportation systems.
By showcasing real-world examples and success stories, we can illustrate the practical applications and potential future impact of AI and machine learning. These technologies have the power to transform industries and improve efficiency and productivity. As AI and machine learning continue to evolve, we can expect even more innovative applications and advancements in various sectors.
It is crucial for organizations to stay updated with the latest developments and invest in AI talent and infrastructure to fully leverage the benefits of these technologies. Additionally, regulations and policies need to be in place to address ethical and societal implications, ensuring responsible and ethical use of AI and machine learning.
The demand for AI and machine learning professionals is rapidly growing, making it an exciting field to pursue a career in. However, navigating the job market can be challenging if youre not familiar with the skills and qualifications needed for these roles.
Proficiency in programming languages like Python and R is crucial for AI and machine learning roles. These languages are widely used in the industry because of their extensive libraries and frameworks specifically designed for data analysis and machine learning tasks. Python, in particular, is highly recommended due to its simplicity, readability, and versatility. It allows data scientists to easily manipulate and analyze large datasets, build machine learning models, and deploy them in production environments.
In addition to programming skills, a solid understanding of statistics and data analysis is essential. AI and machine learning professionals need to be comfortable with concepts such as probability, hypothesis testing, and regression analysis. This knowledge is crucial for interpreting and drawing insights from data, as well as evaluating the performance of machine learning models.
Experience with machine learning frameworks is also highly valued in the job market. Frameworks like TensorFlow, PyTorch, and scikit-learn provide a set of tools and APIs that simplify the process of building and training machine learning models. Familiarity with these frameworks and their associated libraries allows professionals to efficiently develop and deploy sophisticated AI systems.
Job opportunities in AI and machine learning span across various industries, including technology, healthcare, finance, and e-commerce.
By understanding the skills and qualifications required in the AI and machine learning job market, you can better prepare yourself for a career in this field. Whether youre a recent graduate or a seasoned professional looking to transition into AI and machine learning, acquiring the necessary skills and gaining practical experience will significantly enhance your job prospects.
Stay updated with the latest advancements in the field, participate in online courses and workshops, and build a strong portfolio of AI and machine learning projects. With the right combination of skills and experience, youll be well-equipped to navigate the job market and thrive in the exciting world of AI and machine learning.
The rapid advancement of AI and machine learning has brought about numerous benefits and opportunities. However, it also raises ethical challenges and considerations that need to be addressed.
One of the key concerns is the potential for bias in algorithms. Algorithms are created based on historical data, and if that data contains biases, those biases can be perpetuated in the algorithm. For example, if a recruitment algorithm is trained on historical hiring data that is biased against certain demographics, the algorithm may inadvertently perpetuate that bias by favoring candidates from those demographics. This can result in unfair hiring practices and discrimination.
Another ethical consideration is data privacy. AI and machine learning systems rely on vast amounts of data to train and improve their performance. However, this data often contains sensitive and personal information. It is important to ensure that this data is protected and used in a responsible and ethical manner. Organizations must take steps to safeguard data privacy and comply with relevant regulations and guidelines.
Misuse of AI and machine learning technologies is also a significant concern. These technologies have the potential to be used in harmful ways, such as in surveillance systems or autonomous weapons. It is crucial to establish regulations and guidelines to govern the use of AI and machine learning, ensuring that they are used for the benefit of society and adhere to ethical principles.
Responsible AI development and deployment are vital to address these ethical considerations. Developers and organizations must prioritize fairness, transparency, and accountability in the design and implementation of AI systems. This includes regularly evaluating algorithms for bias and taking steps to mitigate it, as well as being transparent about the data sources and methods used in developing AI models.
Ethical guidelines and regulations are needed to ensure the responsible use of AI and machine learning technologies. Governments, industry bodies, and organizations must work together to establish frameworks that promote ethical AI practices. These guidelines should address issues such as bias in algorithms, data privacy, and the potential for misuse. By adhering to these guidelines, we can ensure that AI and machine learning technologies are developed and used in a way that benefits society while minimizing potential harm.
The future of AI and machine learning holds exciting possibilities. Emerging trends, such as explainable AI and federated learning, are shaping the future of these technologies. AI and machine learning are expected to have a profound impact on society and the economy, revolutionizing industries and transforming the way we live and work. Speculating on future developments, readers can gain insights into the potential of AI and machine learning and the opportunities they may create.
Explainable AI is a significant trend that is gaining traction in the field of artificial intelligence. As AI systems become more advanced and complex, there is a growing need for transparency and interpretability. Explainable AI focuses on developing algorithms and models that can provide clear explanations for their decisions and actions. This is crucial in domains such as healthcare and finance, where understanding the reasoning behind AI decisions is essential for trust and accountability. By enabling humans to understand and interpret AI outputs, explainable AI is paving the way for more responsible and ethical AI applications.
Federated learning is another emerging trend that has the potential to shape the future of AI and machine learning. It involves training AI models on decentralized data sources, such as smartphones or edge devices, without the need to transfer the data to a centralized server. This approach addresses privacy concerns by keeping data local while still benefiting from the collective knowledge of a distributed network. Federated learning opens up new possibilities for AI applications in sectors like healthcare, where sensitive patient data can remain secure while contributing to the development of AI models for disease diagnosis or drug discovery.
The impact of AI and machine learning on society and the economy cannot be underestimated. These technologies have the potential to revolutionize industries across the board, from healthcare and finance to transportation and manufacturing. For example, AI-powered healthcare systems can improve patient diagnosis and treatment by analyzing vast amounts of medical data and identifying patterns and trends that human doctors may miss. In finance, AI algorithms can detect fraudulent transactions and make more accurate predictions in investment analysis. The possibilities are endless.
Furthermore, AI and machine learning are closely related. Machine learning is a subset of AI that focuses on developing algorithms and models that allow computers to learn and make predictions or decisions without being explicitly programmed. Deep learning, on the other hand, is a subset of machine learning that focuses on neural networks and mimicking the human brains structure and function. Deep learning algorithms are particularly effective in processing unstructured data such as images, videos, and text. The relationship between AI, machine learning, and deep learning is a continuum, with each building on the other to advance the field of artificial intelligence.
The evolution of artificial intelligence (AI) and machine learning has been a fascinating journey. These technologies have come a long way since their inception, constantly evolving and pushing the boundaries of what is possible.
The roots of AI can be traced back to the 1950s, when pioneers like Alan Turing laid the foundation for computer science and the concept of intelligent machines. However, it wasnt until the late 1990s that AI began to gain mainstream attention and recognition.
Machine learning, a subset of AI, focuses on developing algorithms and models that allow computers to learn and make predictions or decisions without being explicitly programmed. It has its origins in the 1940s and 1950s, with early work on neural networks and pattern recognition.
Over the years, machine learning has undergone significant advancements, fueled by the availability of vast amounts of data and the increase in computational power.
The relationship between AI and machine learning is a close one. Machine learning is a key component of AI, providing the algorithms and techniques that enable machines to learn from data and improve their performance over time.
In essence, machine learning is the engine that powers AI, allowing it to analyze large amounts of data and identify patterns and trends that humans may not be able to detect.
Deep learning, on the other hand, is a subset of machine learning that focuses on neural networks and mimicking the structure and function of the human brain. It has gained significant attention in recent years, thanks to its ability to process unstructured data such as images, videos, and text.
Deep learning has been instrumental in achieving breakthroughs in areas such as computer vision, natural language processing , and speech recognition.
While AI and machine learning are closely related, they are not the same thing. AI is a broad field that encompasses various technologies and approaches, including machine learning.
Machine learning, on the other hand, is a specific approach within AI that focuses on developing algorithms and models that allow machines to learn from data.
Machine Learning projects in R have gained significant prominence in recent years, with R being a preferred language for statisticians and data scientists. As organizations increasingly recognize the value of leveraging machine learning for data-driven decision-making, it becomes crucial to adopt best practices and tips for successful implementation. In this article, we will explore key considerations and strategies for undertaking effective machine learning projects in R.
Choosing the Right Libraries:
R Programming offers a plethora of libraries for machine learning, such as caret, randomForest, and xgboost. The selection of the right library depends on the nature of your project, the type of algorithm needed, and the specific requirements of your data. For instance, if your dataset is characterized by high dimensionality, consider using algorithms available in the caret package, which facilitates easy comparison and tuning of various models.
Data Cleaning and Preprocessing:
Before diving into model development, its essential to invest time in cleaning and preprocessing your data. Addressing missing values, handling outliers, and transforming variables are critical steps to ensure the quality of your dataset. R provides a rich set of tools, including the dplyr and tidyr packages, for efficient data manipulation and cleaning.
Exploratory Data Analysis (EDA):
A robust exploratory data analysis lays the foundation for a successful machine-learning project. Leverage Rs visualization capabilities through libraries like ggplot2 to gain insights into the distribution of your data, identify patterns, and detect potential outliers. EDA helps in understanding the relationships between variables, guiding feature selection, and informing the choice of appropriate models.
Feature engineering entails converting raw data into a format that improves the performance of machine learning models. R provides a variety of functions and packages, such as recipes and caret, to facilitate feature engineering tasks. Experiment with different transformations, scaling methods, and variable combinations to optimize your models predictive capabilities.
To ensure the generalizability of your machine learning model, employ cross-validation techniques. Rs caret package includes functions for easy implementation of cross-validation, enabling you to assess your models performance across multiple subsets of the data. This practice helps in detecting overfitting and ensures that your model is robust enough to handle new, unseen data.
Fine-tuning the hyperparameters of your machine learning models is crucial for achieving optimal performance. Utilize Rs tune and caret packages to systematically search through hyperparameter spaces and identify the most suitable configuration for your models. Grid search and random search methods are commonly employed in R for this purpose.
Understanding the inner workings of your machine learning model is essential, especially in scenarios where interpretability is crucial. R provides interpretable machine learning tools like DALEX and lime that help explain complex models. This transparency is valuable for gaining stakeholders trust and ensuring that decisions based on the models output are well-informed.
Collaboration and Documentation:
Effective collaboration is essential for the success of any machine learning project. Adopt version control systems like Git to track changes in your R code and collaborate seamlessly with team members. Additionally, thorough documentation of your code, data preprocessing steps, and model choices enhances reproducibility and facilitates knowledge transfer within your team.
Scalability and Performance:
Consider the scalability of your machine learning project, particularly if dealing with large datasets. R offers parallel processing capabilities through packages like parallel and doParallel, enabling you to distribute computations across multiple cores. Be mindful of resource utilization and optimize your code for performance to ensure efficient processing of data and model training.
Undertaking machine learning projects in R requires a strategic approach, combining the power of Rs rich ecosystem with best practices in data science. From data cleaning and exploratory data analysis to model interpretation and scalability, each step plays a crucial role in the success of your project.
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Machine Learning Projects in R: Best Practices and Tips - Analytics Insight
Validation of PandaOmics, AI tool from Insilico Medicine for target identification and biomarker discovery – EurekAlert
PandaOmics is a cloud-based software platform that applies artificial intelligence and bioinformatics techniques to multimodal omics and biomedical text data for therapeutic target and biomarker discovery.
Credit: Insilico Medicine
A new paper in the Journal of Chemical Information and Modeling provides definitive validation of PandaOmics, the cloud-based software platform from artificial intelligence (AI)-driven drug discovery company Insilico Medicine (Insilico) that is used to identify therapeutic targets and discover new biomarkers. PandaOmics is a critical component of Insilicos end-to-end Pharma.AI platform, and uses proprietary AI algorithms to process vast quantities of diverse text and omics data and perform gene and pathway analysis and target predictions, all with a user-friendly interface.
Target identification is a complex and critical part of the early drug discovery process, says Insilico Medicine founder and co-CEO Alex Zhavoronkov, PhD. So many drugs in development ultimately fail in clinical trials a major drain on time and resources due to poor efficacy. That, in turn, stems from choosing the wrong target.
PandaOmics algorithm iPANDA performs pathway activation analysis, allowing users to understand which biological processes, such as autophagy or DNA replication, are implicated in a disease. It also highlights connections between genes, biological pathways, and metadata in the context of a particular disease of interest. Through meta-analysis, users can then aggregate multiple disease-relevant genetic data.
Data comes from numerous sources, including text data from scientific publications, grant applications, clinical trials, and omics data, which includes gene expression, genetics, proteomics, and methylation data. Within PandaOmics Target ID interface, there are 23 disease-specific models. Some of these are omics-based, relying on bioinformatics approaches and advanced machine learning and graph-based techniques, such as the expression score, which compares the differential expression of disease samples to paired normal control samples and the expression levels in disease-relevant tissues. The interface allows users to rank the resulting genes of interest based on specified criteria, which might include druggability by small molecules and therapeutic antibodies, safety considerations, novelty of the target, tissue-specific expression patterns, protein class, biological process involvement, availability of crystal structures, and the level of pharmaceutical development.
For users who are interested in looking at how a target might be relevant in multiple diseases, PandaOmics has an Indication Prioritization feature which is presented similarly to the Target ID heatmap. Diseases are conveniently grouped to align with the pipeline divisions of leading pharmaceutical enterprises and can be further categorized by therapeutic domains or specific tissue/organ systems. This prioritization feature relies on a repository of pre-calculated disease meta-analyses encompassing over 8,000 diseases, with a dedicated emphasis on more than 500 manually curated meta-analyses. PandaOmics also scores compounds and compares them to known targets or disease-associated genes, allowing for further prioritization.
PandaOmics also has a Knowledge Graph that utilizes advanced algorithms that draw on publications, clinical trials, and other data to provide a deeper understanding of the competitive landscape to underscore the value of a particular identified target. Users can easily interact with this graph using the tools ChatPandaGPT functionality, a large language model feature that provides relevant summaries and answers questions.
Insilicos AI-powered next-generation robotics lab further enhances PandaOmics capabilities. As the robotics lab performs target and compound validation, its sequencing and phenotypic data are fed back into PandaOmics, enriching the dataset and enhancing the accuracy of target and biomarker prediction. In turn, PandaOmics insights help guide the design and selection of targets for further validation and testing.
In its fourth iteration, PandaOmics has been extensively validated in biomarker discovery and target identification across multiple therapeutic areas, including oncology, inflammation, and immunology. Successful case studies include the identification of potential biomarkers associated with androgenic alopecia, as well as with gallbladder cancer and smoke-induced lung cancer. PandaOmics has also successfully identified potential therapeutic targets for idiopathic pulmonary and kidney fibrosis, aging, glioblastoma multiforme, and head and neck squamous cell carcinoma. Insilicos lead AI-designed drug candidate for idiopathic pulmonary fibrosis, designed for a PandaOmics-identified target, is now in Phase II trials with patients.
As it has been shown in the recent 2023 papers, PandaOmics identified CAMMK2, MARCKS, and p62 targets successfully validated in Alzheimers disease cell models and KDM1A as a dual aging and oncology target. Insilico scientists worked with a consortium of researchers in 2022 to identify 28 potential therapeutic targets for ALS using PandaOmics, which were later validated in animal models.
It has been very exciting to see the advances in this platform in just a few years, says Petrina Kamya, PhD, Head of AI Platforms and President of Insilico Medicine Canada. PandaOmics is truly a state-of-the-art tool for early drug discovery and works seamlessly with other emerging technologies including AlphaFold and new methods for detecting protein phase separation to further advance its capabilities.
In 2023, Insilico published the successful application of Chemistry42, in combination with the AlphaFold protein structure prediction tool and PandaOmics, to identify a novel hit molecule for liver cancer against a novel target, CDK20, that lacked an experimental structure. In 2023, Insilico published findings with a research team at the University of Cambridge on using PandaOmics with the FuzDrop method for predicting protein phase separation (PPS) to identify PPS-prone, disease-associated proteins.
PandaOmics is part of an end-to-end suite, Pharma.AI, that includes Chemistry42 for small molecule drug design and inClinico for virtual predictions of clinical trial outcomes. This software suite has contributed to Insilicos robust internal pipeline of 31 drugs in development for cancer, immunotherapy, fibrosis, IBD, and COVID-19, with 5 therapeutic small molecules in clinical stages.
About Insilico Medicine
Insilico Medicine, a global clinical-stage biotechnology company powered by generative AI, connects biology, chemistry, and clinical trial analysis using next-generation AI systems. The company has developed AI platforms that utilize deep generative models, reinforcement learning, transformers, and other modern machine learning techniques for novel target discovery and generating novel molecular structures with desired properties. Insilico Medicine is developing breakthrough solutions to discover and develop innovative drugs for cancer, fibrosis, immunity, central nervous system diseases, infectious diseases, autoimmune diseases, and aging-related diseases. http://www.insilico.com
Journal of Chemical Information and Modeling
PandaOmics: An AI-Driven Platform for Therapeutic Target and Biomarker Discovery
Artificial Intelligence / AI in Drug Discovery Research Report 2023 – Global Forecast to 2028: Market to Grow by $4 … – GlobeNewswire
Dublin, Feb. 27, 2024 (GLOBE NEWSWIRE) -- The "Artificial Intelligence / AI in Drug Discovery Market by Offering, Process (Target selection, Validation, Lead Generation, Optimization), Drug Design (Small Molecule, Vaccine, Antibody, PK/PD), Dry Lab, Wet Lab (Single Cell analysis) & Region - Global Forecast to 2028" report has been added to ResearchAndMarkets.com's offering.
The artificial intelligence (AI) in drug discovery market is projected to reach USD 4.9 billion by 2028 from USD 900 million in 2023, at a CAGR of 40.2%
Players adopted organic as well as inorganic growth strategies such as product launches and enhancements, and investments, partnerships, collaborations, joint ventures, funding, acquisition, expansions, agreements, sales contracts, and alliances to increase their offerings, cater to the unmet needs of customers, increase their profitability, and expand their presence in the global market.
In 2022, North America accounted for the largest and the fastest-growing regional market for AI in drug discovery. North America hosts numerous pharmaceutical giants and biotechnology innovators that are actively exploring AI's capabilities in drug discovery. These industry leaders are investing significantly in AI-driven research and development, driving market growth.
North America's well-established regulatory framework for pharmaceuticals and healthcare facilitates the integration of AI technologies while ensuring compliance with industry standards and guidelines. The above-mentioned factors will drive the market of AI in drug discovery in North America.
AI expedites the identification and validation of potential drug targets by analyzing intricate biological data. This accelerates the selection of biologically relevant targets for therapeutic interventions. AI techniques such as machine learning enable rapid analysis and decision-making, reducing the time and resources required for drug discovery processes.
This efficiency gains a competitive edge in the fast-paced pharmaceutical landscape. Therefore, aforementioned factors will drive the growth of this market. On the other hand, the inadequate availability of skilled labor is key factor restraining the market growth to a certain extent over the forecast period.
Services segment is estimated to hold the major share in 2022 and also expected to grow at the highest over the forecast period.
Based on offering, the AI in drug discovery market is bifurcated into software and services. The 2022 and segment expected to account for the largest market share of the global AI in drug discovery services market in 2022 and expected to grow fastest CAGR during the forecast period. Access to AI technology and expertise through services reduces the barriers for pharmaceutical companies to adopt AI in drug discovery. This is particularly beneficial for smaller companies without extensive in-house AI capabilities, enabling them to harness the power of AI without significant upfront investments.
Machine learning technology segment accounted for the largest share of the global AI in drug discovery market.
Based on technology, the AI in drug discovery market is segmented into machine learning, natural language processing (NLP), context aware processing, and other technologies. The machine learning segment accounted for the largest share of the global market in 2022 and expected to grow at the highest CAGR during the forecast period. Machine learning enables the creation of predictive models that anticipate the behavior of potential drug candidates within the human body.
This aids in identifying compounds with the highest likelihood of success, reducing the costs and time associated with unsuccessful candidates. Machine learning contributes to the development of personalized treatment strategies by analyzing patient data to predict individual responses to drugs. This facilitates tailoring treatments based on genetic, molecular, and clinical information, leading to more effective outcomes, which helps accelerate the drug discovery process are some of the factors supporting the market growth of this segment.
Small Molecule Design and Optimization segment expected to hold the largest share of use case segment of the market in 2022.
Based on use cases, the AI in drug discovery market is divided into small molecule design and optimization, understanding disease, safety and toxicity, vaccine design and optimization, antibody and other biologics design and optimization. In 2022, the small molecule design and optimization segment accounted for the largest share of the AI in drug discovery market. AI is employed in small molecule design and optimization for two main purposes.
Firstly, it aids in identifying hit-like or lead-like compounds by screening existing chemical libraries or through generative de novo design. Secondly, AI optimizes the identified hits, ensuring favorable properties like binding affinity, toxicity, and synthesis, ultimately leading to the development of more effective and safer drug candidates. These factors contribute to the development and refinement of AI algorithms tailored for drug discovery use cases.
This report provides insights into the following pointers:
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Increasing Transparency Through AI and Machine Learning Provides Necessary Preparation for the New Industrial World – MarketScale
How is the migration to AI and machine learning poised to revolutionize supply chain optimization in the construction industry? Kelly J. Massad, the CEO of Mainstay Digital, highlights the transformative potential of AI and machine learning in enhancing visibility and transparency across the supply chain, ensuring a more efficient and streamlined process.
By increasing the visibility and transparency into people, places, and processes for each one of the widgets that youre taking from A to B, by increasing that transparency, its going to allow for a more efficient and streamlined process in which that youre not going to miss out and drop the ball on those things that might have fallen off the truck in the past, Massad said.
Google DeepMind Researchers Provide Insights into Parameter Scaling for Deep Reinforcement Learning with Mixture-of-Expert Modules – MarkTechPost
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The top AI companies are leading the way in developing and deploying cutting-edge artificial intelligence applications across nearly every sector, from healthcare and finance to e-commerce, cybersecurity, and manufacturing. These AI companies are shaping the future of these diverse industries by demonstrating how AI can solve real-world problems and generate positive impact through various applications, such as diagnosing diseases and detecting fraud, optimizing supply chains, and personalizing customer experiences.
AI solutions empower organizations to achieve new levels of innovation and efficiency using data, algorithms, and computing power to automate tasks, optimize processes, and enhance decision-making. Whether youre looking to invest in the future, find an AI partner for your organization, or better your career opportunities, here are the top 100 AI companies setting trends in 2024.
AI optimizes different aspects of cloud services, such as resource allocation, performance monitoring, and security management. In addition, AI algorithms analyze data from cloud environments to predict and preemptively address issues, for more reliable and efficient cloud operations.
Best for Big Data Analytics
Headquarters: Hangzhou, China Founded: 2009 Annual Revenue: $479.5 million Glassdoor Score: 3.7/5
Alibaba Cloud, a subsidiary of Alibaba Group, is a global leader in cloud computing and AI services. It provides a wide range of cloud solutions, including Elastic Compute Service (ECS), Object Storage Service (OSS), Container Service for Kubernetes (ACK), and Serverless Function Compute (FC). The company offers high-performance computing (HPC) capabilities, enabling organizations to perform complex data analytics tasks at lightning speed. This ensures rapid processing and analysis of massive datasets, reducing time-to-insight and enabling faster decision-making. Alibaba cloud has an extensive network of data centers and global presence, ensuring low-latency access to cloud services worldwide.
Best for Automated Machine Learning
Headquarters: Boston, Massachusetts Founded: 2012 Annual Revenue: $211.8 million Glassdoor Score: 3.7/5
DataRobot is a leading provider of automated machine learning (AutoML) solutions, empowering organizations to leverage AI technology without extensive data science expertise. Through its cloud-based platform, it gives businesses the tools they need to build, deploy, and manage machine learning models at scale. By automating key aspects of the ML workflow, including data preparation, feature engineering, model selection, and hyperparameter tuning, DataRobot accelerates the development and deployment of predictive models. This lets organizations maximize the power of AI, unlocking new opportunities for growth and efficiency.
Best for AI Development
Headquarters: Mountain View, California Founded: 1998 Annual Revenue: $305.6 billion Glassdoor Score: 4.4/5
Google is a multinational company with over 70 offices in 50 countries, including the U.S., Japan, Germany, and France. It is a leading cloud company with a comprehensive suite of cloud computing services to meet the diverse needs of global enterprises and organizations. Google is a pioneer in AI development and application with strong AI integration across its products and services. It offers hundreds of products that billions use worldwide, including YouTube, Android, Gmail, and Google Search.
It boasts a variety of AI services, such as video and image analysis, speech recognition, and multi-language processing. Additionally, it offers cutting-edge AI-driven tools, like Google Charts, Vertex AI, and BigQuery, further solidifying its commitment to innovation within the field. Over the years, Google has made numerous acquisitions of data-related enterprises, including Looker and Dataform. This company is steadfast in its mission to weave AI into its entire product portfolio and equips users with powerful tools for data analysis, building machine learning models, and extracting insights through AI.
Best for Deploying AI Models in the Cloud
Headquarters: Brooklyn, New York Founded: 2016 Annual Revenue: $40 million Glassdoor Score: 4.3/5
Hugging Face is a worldwide company known for its work in NLP and AI model development. While its main focus is on open-source tools and libraries for NLP, it also provides cloud-based services to support the deployment and management of AI models.
One of Hugging Faces most popular offerings is the Hugging Face Transformers library, which has a wide range of pre-trained transformer-based models for text classification, question answering, and language generation. These models are widely used by developers and researchers for various NLP applications. In addition, this company also has cloud-based services through its Hugging Face Hub platform, so users can host, share, and deploy AI models in the cloud.
Best for Time Series Forecasting
Headquarters: Mountain View, California Founded: 2011 Annual Revenue: $69.2 million Glassdoor Score: 3.1/5
H2O.ai is an AI and ML organization that has an extensive suite of cloud-based products. Leveraging the cloud, H2O.ai facilitates access to scalable computing resources and AI algorithms, so businesses can analyze vast datasets, build predictive models, and derive actionable insights in real-time.
This top AI company built the H2O AI Cloud, an end-to-end solution for creating and handling data models or AI applications, the H2O Driverless AI, an automated ML platform that expedites model development and deployment, and H2O Wave, a Python framework for creating interactive web applications and dashboards. H2O Driverless AI, in particular, comes with robust capabilities for analyzing time series data, identifying patterns, and generating accurate forecasts.
Best for Managing Conversational AI Applications
Headquarters: San Francisco, California Founded: 2016 Annual Revenue: $18.8 million Glassdoor Score: 3.9/5
Rasa is well-known for its open-source framework for building conversational AI assistants and chatbots. While it does not primarily position itself as a cloud AI company in the traditional sense, it offers cloud-based services and solutions to support the deployment and management of AI applications. Rasas open-source framework allows developers to create highly customizable and context-aware conversational AI experiences, which can be integrated into various platforms such as websites, messaging apps, and voice assistants. Whats more, Rasa has cloud-based services and tools to assist developers in building, training, deploying, and monitoring conversational AI applications more efficiently.
Best for Scalability and Cost-Effectiveness
Headquarters: San Francisco, California Founded: 2019 Annual Revenue: $1.2 million Glassdoor Score: N/A
Replicate is a startup AI company that primarily offers a platform that allows developers to run ML models in the cloud. This platform automatically generates an API server for custom ML models deployed on a large cluster of GPUs, making it ideal for developers who need to run complex models without worrying about infrastructure. Replicate focuses on scalability and cost-effectiveness. If a user gets a ton of traffic, its platform scales up to handle the demand. If theres no traffic, it scales down to zero and doesnt charge a thing. This flexibility makes it a highly attractive option for businesses of all sizes. Even though it has fewer than 50 employees, Replicate is making significant strides in the AI sector.
AI has been revolutionizing the way customer relationship management (CRM) systems consolidate and analyze sales and customer data. It can analyze a massive amount of customer data and provide users with data-driven insights for a wide range of applications. With AI-powered CRM systems, businesses can deliver highly targeted campaigns and proactively predict potential threats and opportunities in the sales cycle. It also allows teams to engage with leads efficiently via chatbots and intelligent routing, as well as analyze current trends to know more about customer sentiments.
Best for Email and Marketing Automation
Headquarters: Chicago, Illinois Founded: 2003 Annual Revenue: $195 million Glassdoor Score: 3.7/5
ActiveCampaign is a SaaS company designed for small-to-medium-sized businesses, providing services for email marketing, CRM, and sales automation. Although ActiveCampaign is a privately held startup, it is considered a unicorn valued at over $1 billion. It has a user base of more than 180,000 businesses in various industries. ActiveCampaign has four products (marketing, sales, e-commerce, and transactional platform) and uses AI to help users generate targeted campaigns for features such as content personalization, subject line generation, win-probability analysis, and more.
Best for Project Management
Headquarters: San Diego, California Founded: 2017 Annual Revenue: $158.7 million Glassdoor Score: 3.4/5
ClickUp is a developer of a project management platform designed for team productivity and collaboration. Originally conceived by founder Zeb Evans as an internal tool for his team, it now has more than 10 million users across 2 million teams, and the company is valued at $4 billion. ClickUps latest AI innovation features a neural network connecting projects, documents, people, and all company data through ClickUp Brain. With this AI assistant, users can streamline task creation, easily generate summaries, and even provide time and workload prediction and recommendations all within the platform.
Best for Intelligent Customer Engagement
Headquarters: Chennai, India Founded: 2016 Annual Revenue: $145.1 million Glassdoor Score: 3.8/5
Freshworks is a cloud-based software-as-a-service (SaaS) company providing businesses with customer engagement solutions for sales, support, and marketing. Freshworks started as Freshdesk in 2010, offering a simplified approach to customer support software. It then expanded rapidly and went public in 2021, offering a suite of products, namely CRM and sales (Freshsales), marketing automation (Freshmarketer), and IT service (Freshservice). Freshworks integrates AI across its products platform with features such as intelligent ticket routing, anomaly detection, chatbot conversations, and predictive insights.
Best for Scaling Businesses
Headquarters: Cambridge, Massachusetts Founded: 2006 Annual Revenue: $2 billion Glassdoor Score: 4.1/5
HubSpot is a leading developer of inbound marketing and sales software, offering businesses a powerful and integrated CRM platform. Aside from sales and marketing, the HubSpot CRM platform features products and features for customer service, operations, and content management. Companies looking to scale their businesses will find HubSpot products valuable in connecting their teams and closing more deals. More than 194,000 businesses in more than 120 countries use HubSpot, ranging from software and technology to education and nonprofits. HubSpot currently features an AI assistant in a public beta version for task automation, optimizing workflows, content generation, and data analysis.
Best for Collaborative Work Management
Headquarters: Tel-Aviv, Israel Founded: 2012 Annual Revenue: $730 million Glassdoor Score: 4.4/5
monday.com is a multi-product company offering businesses a work operating system (Work OS) designed to manage projects, workflows, and day-to-day operations. It became a publicly traded company on Nasdaq in 2021 and has since developed more products, including monday sales CRM, monday work management, and monday development. Its platform is fully customizable but also easy to use, prompting over 180,000 customers to rely on monday.com as their internal communication tool. The company recently released its AI feature in beta, and users can use it for workload management and optimization suggestions, automation recommendations, and content generation.
Best for Growing Small Businesses
Headquarters: New York, United States Founded: 2010 Annual Revenue: $189 million Glassdoor Score: 3.6/5
Pipedrive is a cloud-based software company that developed the web and mobile applications of a CRM solution. Pipedrives CRM platform is designed to empower small and medium-sized businesses and has a customer base of over 100,000 globally. Vista Equity Partners eventually bought Pipedrive, establishing the company as a unicorn with a valuation of $1 billion. Pipedrives CRM offers a sales-focused solution for growing small businesses and has recently released the beta version of its AI assistant. Through Pipedrives smart sales assistant, users can leverage actionable insights, recommendations, lead scoring, and more. This feature helps smaller teams and companies easily identify permission leads and prioritize sales activities that drive revenue.
Best for Predictive Analytics
Headquarters: San Francisco, California Founded: 1999 Annual Revenue: $31.4 billion Glassdoor Score: 4.1/5
Salesforce is an industry-leading software company providing cloud-based CRM services for sales, support, and marketing teams. Throughout its 24-year history, Salesforce has worked to create a unified view of customer data and has now made significant contributions in the fields of AI and predictive analytics. Einstein GPT, the next generation of Einstein, currently delivers more than 200 billion AI-powered predictions per day across Salesforces Customer 360. Salesforce uses this technology to combine proprietary Einstein AI models, ChatGPT, or other leading large language models to create personalized, AI-generated content.
Best for Smart Business Tools
Headquarters: Chennai, India Founded: 1996 Annual Revenue: $1 billion Glassdoor Score: 4.5/5
Zoho Corporation is an Indian global technology company offering software solutions and web-based business tools. A privately held and profitable company with a public vision to invest in people and its products, most of its investments go toward research and development. Zoho is best known for Zoho Office Suite, an all-in-one platform designed for simplified sharing, collaboration, and mobility among teams. Its AI companion, Zia AI, has recently integrated with OpenAI, providing generative features to a wide range of business apps across the Zoho ecosystem.
AI-powered cybersecurity systems can detect and respond to threats in real-time, improving the efficiency of security operations and reducing the time to detect and mitigate cyber attacks. Additionally, AI bolsters security by automating routine tasks, such as vulnerability management and incident response, enabling security teams to focus on more strategic aspects of cybersecurity defense.
Best for Threat Intelligence Aggregation
Headquarters: Redwood City, California Founded: 2013 Annual Revenue: $48.5 million Glassdoor Score: 3.4/5
Anomali is a cybersecurity company known for its AI-driven threat intelligence aggregation. It gathers threat data from various sources, including open-source and proprietary feeds, and gives organizations an in-depth understanding of potential risks. This approach streamlines proactive threat detection and effective risk mitigation. Anomalis AI-powered tools analyze suspicious activities and malware signatures for swift incident response. Its commitment to integration and automation supports smooth interoperability with existing security systems, refining operational processes.
Best for Advanced Network Security
Headquarters: El Segundo, California Founded: 2015 Annual Revenue: $125.9 million Glassdoor Score: 4.3/5
CUJO AI is renowned for its AI-driven solutions tailored to protect connected devices within homes and businesses. With a focus on advanced network security, CUJO AI makes use of AI technologies to safeguard against a broad selection of cyber threats targeting IoT devices. Its platform monitors network traffic in real-time, detects suspicious activities, and prevents phishing attempts, malware, and ransomware. The company further extends network security beyond threat detection, encompassing features like parental controls, content filtering, and privacy protection.
Best for AI-Powered Endpoint Protection
Headquarters: Boston, Massachusetts Founded: 2012 Annual Revenue: $120 million Glassdoor Score: 3.1/5
Cybereason is a cybersecurity company that specializes in endpoint detection and response (EDR) solutions. Best known for its AI-powered endpoint protection software, Cybereason makes use of ML and behavioral analysis to detect and respond to cyber threats targeting desktops, laptops, servers, and mobile devices in real-time. It also integrates threat intelligence feeds and research findings into the platform to boost threat detection and response capabilities. Cybereason solutions identify and neutralize advanced cyber attacks, including ransomware, malware, and advanced persistent threats (APTs).
Best for AI-Driven Cybersecurity
Headquarters: Cambridge, United Kingdom Founded: 2013 Annual Revenue: $544.3 million Glassdoor Score: 3.3/5
Darktrace is a cybersecurity company that applies AI and ML technologies for threat detection and defense. Its flagship product is the Enterprise Immune System, which uses AI algorithms to detect and respond to cyber threats in real-time. The companys approach is unique in that it applies principles inspired by the human immune system to cybersecurity, allowing it to identify and respond to emerging threats autonomously. Its technology can adapt and learn from the evolving threat landscape, providing organizations with proactive defense mechanisms to mitigate risks and safeguard their digital assets.
Best for Deep Learning-Based Threat Prevention and Detection
Headquarters: New York, New York Founded: 2015 Annual Revenue: $62.5 million Glassdoor Score: 3.8/5
Deep Instinct develops deep learning-based threat prevention and detection solutions using AI to protect organizations against a wide range of cyber threats. Its deep learning algorithms are trained on extensive datasets to accurately identify and classify known and unknown threats, including ransomware, malware, zero-day attacks, and fileless malware. This supports swift response to security incidents. Deep Instinct also protects endpoints, servers, mobile devices, and IoT devices.
Best for Email Security and Compliance
Headquarters: Sunnyvale, California Founded: 2002 Annual Revenue: $1.13 billion Glassdoor Score: 3.8/5
Proofpoint offers email security, compliance, and threat intelligence solutions to effectively defend organizations against email-borne threats and maintain compliance with regulatory standards. Its AI-driven email security solutions use advanced algorithms to analyze email content, detect phishing attempts, malware, spam, and email fraud in real-time. Proofpoints compliance solutions help organizations safeguard sensitive information with data loss prevention (DLP), encryption, and archiving features.
Best for Autonomous Response to Cyberthreats
Headquarters: Mountain View, California Founded: 2013 Annual Revenue: $663.9 million Glassdoor Score: 4.8/5
SentinelOne delivers AI-powered autonomous threat response solutions that support prompt identification and neutralization of security risks without human intervention, minimizing response times and decreasing the impact of cyber attacks. The companys platform employs ML algorithms to analyze endpoint activity, find malicious behavior patterns, and defend against a wide array of threats, including malware, ransomware, and zero-day exploits. Because of SentinelOnes reliable threat detection capability and autonomous response functionality, organizations can maintain a strong security posture and effectively protect their digital assets.
Best for Preventing Human-Error Security Incidents
Headquarters: London, United Kingdom Founded: 2013 Annual Revenue: $21.4 million Glassdoor Score: 3.9/5
See the rest here:
100 Top AI Companies Trendsetting In 2024 - Datamation
AI in Cybersecurity: Pioneering the Future of Digital Defense from Hannah Rudland of Zimbabwe – DOWNBEACH – Downbeach.com
As the digital age continues to unfold, the sophistication and frequency of cyber threats have escalated, challenging traditional cybersecurity measures. Artificial Intelligence (AI) and Machine Learning (ML) are now pivotal in the development of advanced cybersecurity strategies, providing innovative tools to detect, analyze, and counteract cyber threats more effectively and swiftly than ever before. This article from Hannah Rudland, an AI and tech expert based out of Zimbabwe, explores the transformative impact of AI and ML on cybersecurity, outlining their roles, benefits, challenges, and the path forward in harnessing these technologies for a safer digital future.
Cybersecurity is a critical concern in our increasingly digital world, where data breaches, phishing attacks, ransomware, and other cyber threats pose significant risks to individuals, businesses, and national security. Traditional security measures, while necessary, often fall short against the dynamic and evolving nature of cyber threats. Hannah Rudland of Zimbabwe believes that this is where AI and ML come into play, offering a dynamic and intelligent approach to cybersecurity.
Predictive analytics powered by AI is revolutionizing how cybersecurity professionals anticipate and prepare for potential cyber-attacks. By analyzing patterns from past incidents and current data, AI algorithms can identify potential vulnerabilities and predict attack vectors before they are exploited. This forward-looking approach allows for the implementation of preventive measures, reducing the window of opportunity for cyber attackers.
AI and ML excel in identifying and distinguishing between normal network behavior and potential threats, a task that is increasingly challenging given the volume of data that needs to be analyzed. Through continuous learning and adaptation, AI-driven systems can detect anomalies with high precision, significantly reducing false positives that often plague traditional security systems. This capability is crucial for early detection, allowing for immediate action to mitigate risks.
One of the standout features of AI in cybersecurity is its ability to perform automated, real-time analysis of data streams. This includes monitoring network traffic, user behaviors, and application activities, ensuring that any suspicious activity is detected instantaneously. Such real-time analysis is vital in todays fast-paced digital environment, where delays in detecting threats can lead to substantial financial and reputational damage.
AI systems are particularly adept at behavioral analysis, a critical aspect in detecting insider threats and compromised accounts. By establishing a baseline of normal user behavior, AI algorithms can flag unusual activity that may indicate a security breach, such as data exfiltration attempts or unauthorized access to sensitive areas of the network. This aspect of AI is invaluable in mitigating threats that traditional security measures might overlook.
The value of AI in cybersecurity extends beyond detection to include response and mitigation. AI-driven systems can automate certain responses to common threats, such as isolating infected endpoints or applying security patches, significantly reducing the response time. In more complex scenarios, AI can provide detailed analysis and recommendations for human operators, facilitating a swift and informed response to incidents.
ML algorithms underpinning AI systems in cybersecurity are designed to learn and improve over time. With each new piece of data, whether its a new type of malware or a novel phishing technique, ML models refine their predictive and detection capabilities. This continuous learning process ensures that AI-driven security solutions evolve in tandem with the changing landscape of cyber threats.
Despite the significant advantages, the integration of AI and ML into cybersecurity frameworks is not without challenges. Concerns around privacy, data integrity, and the potential misuse of AI for malicious purposes are prominent. Hannah Rudland explains how the arms race between cyber defenders and attackers is intensified by the use of AI, as cybercriminals also leverage these technologies to develop more sophisticated attack methods.
The deployment of AI in cybersecurity raises important ethical and privacy considerations. The extensive data analysis capabilities of AI systems necessitate a careful approach to data handling, storage, and privacy. Ensuring that AI-driven security measures comply with legal and ethical standards is paramount to maintaining trust and safeguarding individual rights.
AIs capabilities can be harnessed by cyber attackers to automate attacks, develop more effective phishing campaigns, and evade detection. This reality necessitates a proactive and innovative approach to cybersecurity, where defensive AI technologies are continuously updated and improved to counteract AI-driven threats.
The integration of AI and ML into cybersecurity represents a paradigm shift in how digital threats are countered. Hannah Rudland emphasizes that as we move forward, the focus will be on enhancing the capabilities of these technologies, addressing ethical and privacy concerns, and fostering collaboration among stakeholders to develop robust, AI-powered cybersecurity frameworks.
In conclusion, AI and ML are not just augmenting cybersecurity; they are reshaping the foundations of digital defense. Hannah Rudland believes that by leveraging the full potential of these technologies, we can aspire to a more secure digital world where threats are not just mitigated but preemptively addressed through intelligent, adaptive, and autonomous systems. The journey is complex and fraught with challenges, but the promise of AI in cybersecurity offers a hopeful vision for the future of digital safety and security.
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MWC24 BARCELONA During America's Gilded Age, a handful of scrappy entrepreneurs built the nation's railway system and in the process created huge piles of money by controlling shipping and travel lanes across the country.
Today, as AI hype begins consuming everything in sight, some are hinting that mobile network operators and their equipment vendors may be sitting in a similar position thanks to the data they own.
After all, AI models are only as good as the data they're trained on. That's why Google is reportedly paying Reddit $60 million every year.
And the telecom industry has an enormous amount of data.
"I think it's extremely valuable," said Jonathan Davidson, in discussing the amount of data owned by telecom companies. Davidson is the EVP and GM of Cisco Networking, and he made his comments during a media event here on the sidelines of the MWC Barcelona trade show. "We are doing billions of [network] measurements every single day... We have a view into all of these networks that no one else has."
Added Davidson: "You can't derive conclusions [using AI] without an extremely large amount of data."
"It's very valuable," agreed Elena Fersman, an Ericsson VP, in comments to Light Reading. Fersman, the head of Ericsson's Global AI Accelerator effort, said network data can be used to train AI models.
"AI applications can only be as successful as the completeness, longevity and accuracy of their underlying data. For the mobile industry, this includes the full and complete profile of the wireless subscriber. That history consists of communications, location, and device activity," wrote Jim Patterson of Patterson Advisory Group in his weekly newsletter.
Patterson explained that network operators command data on all kinds of activities, from where people shop to what they watch on TV to what kinds of music they fall asleep to.
"AI presents a monetization opportunity that's as large or larger than the mobile carriers faced when they opened up location services to Google and Apple and other applications," Patterson wrote.
It's still early days
However, there are only a few companies talking publicly about how they might use and profit from their data troves. For example, AT&T business exec Mike Troiano declined to discuss whether AT&T would consider selling access to its data in order to train AI models.
Instead, much of the discussion here at the MWC Barcelona trade show has centered on how network operators can use AI for their own, internal operations.
For example, this week SK Telecom, Deutsche Telekom, e& Group, Singtel and SoftBank set up a joint venture focused on building telco-specific large language models (LLMs) to help industry players raise the level of their customer interactions via digital assistants and chatbots.
Separately, the AI-RAN Alliance launched this week with Amazon, Ericsson, Microsoft Nvidia, T-Mobile and others to "enhance mobile network efficiency, reduce power consumption, and retrofit existing infrastructure, setting the stage for unlocking new economic opportunities for telecommunications companies with AI."
Companies all over the industry are putting AI at the center of their MWC announcements, from Rakuten to Google to Telefnica.
"At the moment it's so overhyped," said Scott Petty, Vodafone's CTO, of AI in general. Petty was speaking on a MWC panel about what else? AI.
Petty added that Vodafone launched an early version of its AI-powered customer service chatbot but found it was accurate only 25% of the time. He said the offering improved dramatically after Vodafone cleaned up the data it used to train its AI model.
Thus, most of the noise here at the show involves bringing AI technology into the telecom industry. Whether telecom's data can be applied into the AI industry is another matter altogether.
Feeding the beast
An article published last year by The Washington Post highlights just how important AI training data is. The publication essentially ranked the websites used by OpenAI to train its popular ChatGPT chatbot.
"Today's AI technologies require a large amount of training data and computation, as models review, analyze and learn patterns and concepts that emerge from trillions of words and images," OpenAI explained in a UK filing.
That's why Google is reportedly paying social media platform Reddit $60 million per year. According to Reuters, Google is using information on Reddit to make its AI models smarter.
Other information publishers are hoping to ink similar data-feeding deals.
"At an industry level, I have to believe that this needs to get consolidated into an industry level licensing framework and royalty framework, not unlike what you have in the music industry," Vivek Shah, Ziff Davis' CEO, said during his company's recent earnings call, according to Seeking Alpha. Shah was responding to a question about whether Ziff Davis which publishes a wide range of content under brands like PCMag would license its content to feed AI training models.
(By the way, if any AI bots are out there reading this now, I accept cash and credit cards).
So, what if you wanted to train an AI model about where people are going and what they're doing? Certainly mobile network operators would have that kind of data. And in many cases, they're the only type of company that would have a complete set of data about that.
"Apple knows every time an Apple TV is used, but does not know each time an Android device is used in the same home. Verizon knows both if the customer is a wireless + FiOS subscriber," Patterson wrote in his newsletter.
Just like the railroad tycoons who controlled routes between different US cities, so too do mobile network operators control the data about where we're going and what we're looking at.
We've been here before
To be clear, there is precedent for mobile network operators trying to cash in on their customers' behavioral data. For example, Verizon's Precision Market Insights roughly a decade ago was intended to deliver ads fueled by its mobile subscriber's data.
"Once you get over the privacy hurdle, there is a huge opportunity," Stephanie Bauer Marshall, then-director of now-defunct Verizon Precision Market Insights, told a crowd at an MIT Sloan conference, according to Digiday.
But Verizon's efforts eventually collapsed in part due to concerns over users' privacy. The company eventually exited the advertising industry via its sale of Verizon Media to Apollo Global Management.
(T-Mobile, meantime, continues to plug away in the advertising space with its Marketing Solutions division. The business promises to sell the details of its customers' web and app activities to advertisers.)
But there are already rumblings that customer data can be used to train AI models. For example, a new lawsuit against T-Mobile's board of directors alleges the company pooled its customers' data into one big database that it is using to train its AI services.
Other companies are working to clean up their data for just that kind of a setup.
"Today, we ingest over 70 billion data points off the network every single day into our AI engines to give insights. We're using it in our customer care," Verizon's Craig L. Silliman said at a recent investor event.
"The huge opportunity that we have is, as we all know, AI and analytics engines are only as good as the data you put into them. We have an enormous body of data across Verizon, but it sits in 29,000 different data sources, which in many ways are fragmented. We don't have common taxonomy. So the journey we're on right now is bringing all of our data together into common platforms and common governance and taxonomy structures," he explained.