The 11 Best AI Tools for Data Science to Consider in 2024 – Solutions Review

Posted on January 12, 2024 by Danzig

Solutions Reviews listing of the best AI tools for data science is an annual sneak peek of the top tools included in our Buyers Guide for Data Science and Machine Learning Platforms. Information was gathered via online materials and reports, conversations with vendor representatives, and examinations of product demonstrations and free trials.

The editors at Solutions Review have developed this resource to assist buyers in search of the best AI tools for data science to fit the needs of their organization. Choosing the right vendor and solution can be a complicated process one that requires in-depth research and often comes down to more than just the solution and its technical capabilities. To make your search a little easier, weve profiled the best AI tools for data science all in one place. Weve also included platform and product line names and introductory software tutorials straight from the source so you can see each solution in action.

Note: The best AI tools for data science are listed in alphabetical order.

Platform: DataRobot Enterprise AI Platform

Related products: Paxata Data Preparation, Automated Machine Learning, Automated Time Series, MLOps

Description: DataRobot offers an enterprise AI platform that automates the end-to-end process for building, deploying, and maintaining AI. The product is powered by open-source algorithms and can be leveraged on-prem, in the cloud or as a fully-managed AI service.DataRobotincludesseveralindependent but fully integrated tools (PaxataData Preparation,Automated Machine Learning, Automated Time Series,MLOps, and AI applications), and each can be deployed in multiple ways to match business needs and IT requirements.

Platform: H2O Driverless AI

Related products: H2O 3, H2O AutoML for ML, H2O Sparkling Water for Spark Integration, H2O Wave

Description: H2O.ai offers a number of AI and data science products, headlined by its commercial platform H2O Driverless AI. Driverless AI is a fully open-source, distributed in-memory machine learning platform with linearscalability. H2O supports widely used statistical and machine learning algorithms including gradient boosted machines, generalized linear models, deep learning and more. H2O has also developedAutoMLfunctionality that automatically runs through all the algorithms to produce a leaderboard of the best models.

Platform: IBM Watson Studio

Related products: IBM Cloud Pak for Data, IBM SPSS Modeler, IBM Decision Optimization, IBM Watson Machine Learning

Description: IBM Watson Studio enables users to build, run, and manage AI models at scale across any cloud. The product is a part of IBM Cloud Pak for Data, the companys main data and AI platform. The solution lets you automate AI lifecycle management, govern and secure open-source notebooks, prepare and build models visually, deploy and run models through one-click integration, and manage and monitor models with explainable AI. IBM Watson Studio offers a flexible architecture that allows users to utilize open-source frameworks likePyTorch, TensorFlow, and scikit-learn.

https://www.youtube.com/watch?v=rSHDsCTl_c0

Platform: KNIME Analytics Platform

Machine Learning: The Future of Predicting Health Outcomes in Aging Canadians – Medriva

Posted on January 12, 2024 by Danzig

Healthcare as we know it is being transformed by artificial intelligence (AI) and machine learning. A research team from the University of Alberta is pioneering this transformation by using machine learning programs to predict the future mental and physical health of aging Canadians. The project, which utilizes data from the Canadian Longitudinal Study on Aging (CLSA), focuses on over 30,000 Canadians between the ages of 45 and 85.

The research team has developed a unique biological age index using machine learning models, which allows them to assess the health of individuals more accurately than ever before. This index is not just about chronological age. Instead, it provides a holistic view of an individuals health by considering various health-related, lifestyle, socio-economic, and other data. The biological age index gives a more accurate reflection of an individuals overall health status, providing critical insights for personalized care plans.

In addition to the biological age index, the team has also developed a program that can accurately predict the onset of depression within three years. Depression is a common but serious condition that can significantly impact the quality of life, especially for the aging population. Early detection and intervention are critical, and this machine learning model could potentially revolutionize mental health care by allowing for early, proactive interventions.

These machine learning models are not yet ready for real-world implementation. However, they signify a significant shift towards individualized care tailored to each patients unique health profile. The ultimate aim is to contribute to healthy aging, benefiting not just Albertans but all Canadians. These models could potentially transform patient care by providing clinicians, patients, and people with lived experience with valuable insights into potential health outcomes.

This groundbreaking research is funded by various organizations, including the Canada Research Chairs program, Alberta Innovates, Mental Health Foundation, Mitacs Accelerate program, and others. The researchers plan to refine these models further, involving clinicians, patients, and individuals with lived experience in the process. The goal is to demonstrate the potential benefits of these models and pave the way for their eventual implementation in healthcare settings.

AI and machine learning have immense potential in the healthcare sector. The ability to process and interpret multi-modal data can lead to more personalized patient care. They can also save time for researchers analyzing clinical trial results. However, as with any transformative technology, there are challenges. For AI and machine learning to work effectively, the quality of data fed into these models needs to be high. There is also a need for technologies that help patients manage their health. In addition, the ethical and regulatory aspects of AI use in healthcare need careful consideration.

As the University of Alberta continues to lead in the intersection of machine learning, health, energy, and indigenous initiatives in health and humanities, the future of healthcare looks promising. The ability of machine learning to predict future health conditions in aging Canadians is just the beginning. As these models are refined and tested further, they could significantly contribute to the development of a healthier future for all.

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Machine Learning: The Future of Predicting Health Outcomes in Aging Canadians - Medriva

Weekly AiThority Roundup: Biggest Machine Learning, Robotic And Automation Updates – AiThority

Posted on January 12, 2024 by Danzig

This is your AI Weekly Roundup. We are covering the top updates from around the world. The updates will feature state-of-the-art capabilities inartificial intelligence (AI),Machine Learning, Robotic Process Automation, Fintech, and human-system interactions. We cover the role of AI Daily Roundup and its application in various industries and daily lives.

As the technology landscape evolves, Dell emerges in 2023 with a host of transformative developments, marking its continued impact on the world of computing and innovation. Dell, a stalwart in the tech industry, starts the year 2023 with a flurry of groundbreaking news stories, offering a glimpse into the companys strategic moves and technological advancements that are set to shape the future of computing.

Skylo, the global leader in non-terrestrial networks, announced that it will interconnect its NTN satellite network with FocusPoints PULSE platform enabling FocusPoints IoT monitoring and emergency escalation service.

Ansysannounced that Ansys AVxcelerate Sensors will be accessible within NVIDIA DRIVE Sim,a scenario-based AV simulator powered by NVIDIA Omniverse, a platform for developingUniversal Scene Description (OpenUSD)applications for industrialdigitalization.

Intel CorpandDigitalBridge Group, a global investment firm announced the formation of Articul8 AI, Inc. (Articul8), an independent company offering enterprise customers a full-stack, vertically-optimized and secure generativeartificial intelligence(GenAI) software platform.

Cerence Inc.AI for a world in motion, announced it is collaborating with Microsoft to deliver an evolved in-vehicleuser experiencethat combines Cerences extensiveautomotive technologyportfolio and professional services with the innovative technology and intelligence of Microsoft Azure AI Services.

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Weekly AiThority Roundup: Biggest Machine Learning, Robotic And Automation Updates - AiThority

Unlocking the Potential of Acceleration Data in Disease Diagnosis – Medriva

Posted on January 12, 2024 by Danzig

Unlocking the Potential of Acceleration Data in Disease Diagnosis

Advancements in technology have paved the way for innovative approaches to disease diagnosis, particularly in the realm of gait-related diseases such as peripheral artery disease (PAD). Traditional methods for diagnosing cardiovascular diseases, such as PAD, have proven to be inadequate in identifying individuals at risk, often resulting in late-stage diagnoses. This has necessitated the development of more accurate, cost-effective, and convenient diagnostic tools.

A recent study introduces a promising framework for processing acceleration data collected from reflective markers and wearable accelerometers. This data is key to diagnosing diseases affecting gait, including PAD. The framework shows impressive accuracy in distinguishing PAD patients from non-PAD controls using raw marker data. Although accuracy is slightly reduced when using data from a wearable accelerometer, the results remain promising.

Machine learning models have been proposed to overcome the limitations of current diagnostic methods. However, these models often require significant time, resources, and expertise. The new framework addresses these challenges by utilizing existing data and wearable accelerometers to gather detailed gait parameters outside laboratory settings.

One of the key advantages of this approach is the potential for data availability and consistency. With wearable accelerometers, data can be collected in a variety of real-world settings, providing a more accurate picture of an individuals gait. This could lead to earlier detection and treatment of PAD, and potentially other gait-related diseases.

Further advancements in technology have led to the development of self-powered gait analysis systems (SGAS) based on a triboelectric nanogenerator (TENG). These systems comprise a sensing module, a charging module, a data acquisition and processing module, and an Internet of Things (IoT) platform. They use specialized sensing units positioned at the forefoot and heel to generate synchronized signals for real-time step count and step speed monitoring. The data is then wirelessly transmitted to an IoT platform for analysis, storage, and visualization, offering a comprehensive solution for motion monitoring and gait analysis.

Aside from gait analysis, recent studies have also explored the use of eye movement patterns to diagnose neurodegenerative disorders such as Alzheimers disease, mild cognitive impairment, and Parkinsons disease. An algorithm has been developed to automatically identify these patterns, with significantly different saccade and pursuit characteristics observed in the patient groups compared to controls. This showcases the potential of non-invasive eye tracking devices to record eye motion and gaze location across different tasks, further contributing to early and accurate disease detection.

With the advent of smartwatch-smartphone technology, home-based monitoring of patients with gait-related diseases has become a realistic possibility. This technology can be used to process acceleration data, helping to diagnose diseases affecting gait. This approach offers a low-cost, convenient tool for diagnosing PAD and other gait-related diseases, marking a significant step forward in the field of disease diagnosis and management.

In conclusion, the use of acceleration data, machine learning, and wearable technology offers a promising pathway for the early detection and diagnosis of PAD and potentially other gait-related diseases. As we continue to push the boundaries of technology and harness the power of data, we can look forward to a new era of healthcare that is more proactive, personalized, and effective.

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Unlocking the Potential of Acceleration Data in Disease Diagnosis - Medriva

New study: Countless AI experts doesnt know what to think on AI risk – Vox.com

Posted on January 12, 2024 by Danzig

In 2016, researchers at AI Impacts, a project that aims to improve understanding of advanced AI development, released a survey of machine learning researchers. They were asked when they expected the development of AI systems that are comparable to humans along many dimensions, as well as whether to expect good or bad results from such an achievement.

The headline finding: The median respondent gave a 5 percent chance of human-level AI leading to outcomes that were extremely bad, e.g. human extinction. That means half of researchers gave a higher estimate than 5 percent saying they considered it overwhelmingly likely that powerful AI would lead to human extinction and half gave a lower one. (The other half, obviously, believed the chance was negligible.)

If true, that would be unprecedented. In what other field do moderate, middle-of-the-road researchers claim that the development of a more powerful technology one they are directly working on has a 5 percent chance of ending human life on Earth forever?

Each week, we explore unique solutions to some of the world's biggest problems.

In 2016 before ChatGPT and AlphaFold the result seemed much likelier to be a fluke than anything else. But in the eight years since then, as AI systems have gone from nearly useless to inconveniently good at writing college-level essays, and as companies have poured billions of dollars into efforts to build a true superintelligent AI system, what once seemed like a far-fetched possibility now seems to be on the horizon.

So when AI Impacts released their follow-up survey this week, the headline result that between 37.8% and 51.4% of respondents gave at least a 10% chance to advanced AI leading to outcomes as bad as human extinction didnt strike me as a fluke or a surveying error. Its probably an accurate reflection of where the field is at.

Their results challenge many of the prevailing narratives about AI extinction risk. The researchers surveyed dont subdivide neatly into doomsaying pessimists and insistent optimists. Many people, the survey found, who have high probabilities of bad outcomes also have high probabilities of good outcomes. And human extinction does seem to be a possibility that the majority of researchers take seriously: 57.8 percent of respondents said they thought extremely bad outcomes such as human extinction were at least 5 percent likely.

This visually striking figure from the paper shows how respondents think about what to expect if high-level machine intelligence is developed: Most consider both extremely good outcomes and extremely bad outcomes probable.

As for what to do about it, there experts seem to disagree even more than they do about whether theres a problem in the first place.

The 2016 AI impacts survey was immediately controversial. In 2016, barely anyone was talking about the risk of catastrophe from powerful AI. Could it really be that mainstream researchers rated it plausible? Had the researchers conducting the survey who were themselves concerned about human extinction resulting from artificial intelligence biased their results somehow?

The survey authors had systematically reached out to all researchers who published at the 2015 NIPS and ICML conferences (two of the premier venues for peer-reviewed research in machine learning, and managed to get responses from roughly a fifth of them. They asked a wide range of questions about progress in machine learning and got a wide range of answers: Really, aside from the eye-popping human extinction answers, the most notable result was how much ML experts disagreed with one another. (Which is hardly unusual in the sciences.)

But one could reasonably be skeptical. Maybe there were experts who simply hadnt thought very hard about their human extinction answer. And maybe the people who were most optimistic about AI hadnt bothered to answer the survey.

When AI Impacts reran the survey in 2022, again contacting thousands of researchers who published at top machine learning conferences, their results were about the same. The median probability of an extremely bad, e.g., human extinction outcome was 5 percent.

That median obscures some fierce disagreement. In fact, 48 percent of respondents gave at least a 10 percent chance of an extremely bad outcome, while 25 percent gave a 0 percent chance. Responding to criticism of the 2016 survey, the team asked for more detail: how likely did respondents think it was that AI would lead to human extinction or similarly permanent and severe disempowerment of the human species? Depending on how they asked the question, this got results between 5 percent and 10 percent.

In 2023, in order to reduce and measure the impact of framing effects (different answers based on how the question is phrased), many of the key questions on the survey were asked of different respondents with different framings. But again, the answers to the question about human extinction were broadly consistent in the 5-10 percent range no matter how the question was asked.

The fact the 2022 and 2023 surveys found results so similar to the 2016 result makes it hard to believe that the 2016 result was a fluke. And while in 2016 critics could correctly complain that most ML researchers had not seriously considered the issue of existential risk, by 2023 the question of whether powerful AI systems will kill us all had gone mainstream. Its hard to imagine that many peer-reviewed machine learning researchers were answering a question theyd never considered before.

I think the most reasonable reading of this survey is that ML researchers, like the rest of us, are radically unsure about whether to expect the development of powerful AI systems to be an amazing thing for the world or a catastrophic one.

Nor do they agree on what to do about it. Responses varied enormously on questions about whether slowing down AI would make good outcomes for humanity more likely. While a large majority of respondents wanted more resources and attention to go into AI safety research, many of the same respondents didnt think that working on AI alignment was unusually valuable compared to working on other open problems in machine learning.

In a situation with lots of uncertainty like about the consequences of a technology like superintelligent AI, which doesnt yet exist theres a natural tendency to want to look to experts for answers. Thats reasonable. But in a case like AI, its important to keep in mind that even the most well-regarded machine learning researchers disagree with one another and are radically uncertain about where all of us are headed.

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New study: Countless AI experts doesnt know what to think on AI risk - Vox.com

How Machine Learning is Transforming the Financial Industry – Medium

Posted on January 12, 2024 by Danzig

The financial industry has always relied heavily on using data to model risks, identify opportunities, and optimize decisions. Today, machine learning is taking financial data science to new levels analyzing massive datasets, uncovering subtle patterns, and powerfully predicting future outcomes. These AI-powered models are being woven into countless processes in banking, insurance, trading firms, and more.

In this article, well explore some of the most impactful applications of machine learning across the financial sector and why this technology represents a breakthrough in capabilities compared to traditional statistical methods. Well also consider some promising directions this transformation might take in the years to come.

Banks lose billions each year to payment fraud despite their best efforts to stop it. The volume and variety of transactions make spotting criminals in the act like finding a needle in a haystack. Fortunately, machine learning algorithms have an uncanny knack for finding needles.

By analyzing past payment data like timestamps, locations, devices, and more, unsupervised learning models can define a normal pattern of legitimate behavior for each customer. When a new payment strays too far from that norm, the algorithms flag it for review. This enables banks to catch many more fraudulent payments while minimizing false alarms that frustrate legitimate customers.

Whats most impressive is that these models continually monitors customers and adapt to their evolving behaviors over time. So banks can keep account security tight without compromising convenience for most payments. Unsupervised learning stops fraud in real-time behind the scenes without customers ever knowing.

Evaluating loan applications requires careful analysis of employment details, financial statements, credit reports, property values, and more to estimate risks and repayment capacity. This complex process is time-consuming, subjective, and inconsistent when done manually.

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How Machine Learning is Transforming the Financial Industry - Medium

Machine Learning: The Key to Quantum Device Variability – Medriva

Posted on January 12, 2024 by Danzig

Machine Learning: The Key to Quantum Device Variability

A breakthrough study led by the University of Oxford has managed to bridge the reality gap in quantum devices, a term referring to the inherent variability between the predicted and observed behavior of these devices. This was achieved through the innovative use of machine learning techniques. The studys findings provide a promising new approach to infer the internal disorder characteristics indirectly. The pioneering research could have significant implications for the scaling and combination of individual quantum devices. It could also guide the engineering of optimum materials for quantum devices.

The researchers at the University of Oxford used a physics-informed machine learning approach for their study. This method allowed the team to infer nanoscale imperfections in the materials that quantum devices are made from. These imperfections can cause functional variability in quantum devices and lead to a difference between predicted and actual behavior the so-called reality gap. The research group was able to validate the algorithms predictions about gate voltage values required for laterally defined quantum dot devices. This technique, therefore, holds significant potential for developing more complex quantum systems.

The studys findings could help engineers design better quantum devices. By being able to quantify the variability between quantum devices, engineers can make more accurate predictions of device performance. This could aid in the design and engineering of optimal materials for quantum devices. Applications range from climate modeling to drug discovery, making this a crucial development in the field.

The development in quantum device engineering comes at a time when the quantum computing market is experiencing exponential growth. According to a report by GlobalDatas Thematic Intelligence, the quantum computing market was valued between $500 million and $1 billion in 2022, and it is projected to rise to $10 billion between 2026 and 2030. This represents a compound annual growth rate of between 30% and 50%. With increasing investment and market growth, the Oxford studys findings could have far-reaching implications for the future of quantum computing.

In conclusion, the study led by the University of Oxford marks a significant leap forward in quantum computing. By utilizing machine learning to bridge the reality gap in quantum devices, the researchers have provided a new method to infer nanoscale imperfections in materials and quantify the variability between quantum devices. This not only allows for more accurate predictions of device performance but also informs the engineering of optimum materials for quantum devices. With quantum computing predicted to grow significantly in the coming years, these findings could have a profound impact on the industry.

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Machine Learning: The Key to Quantum Device Variability - Medriva

The Shaping of Material Science by AI and ML: A Journey Towards a Smarter, Greener Industrial Future – Medriva

Posted on January 12, 2024 by Danzig

The field of material science is experiencing a remarkable transformation, thanks to the integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies. These technological advancements are revolutionizing the process of material discovery and development, promising enhanced efficiency, innovation, and commitment to sustainability and environmental responsibility. The impact of this integration is far-reaching, touching various industries from consumer packaged goods to automotive, oil and gas, and energy. For businesses to stay competitive in this rapidly evolving, environmentally conscious landscape, embracing these technologies is crucial, representing a transformative journey towards a smarter, greener industrial future.

As highlighted by Forbes, the challenges in material development are being addressed by the use of ML, MLOps, and large language models (LLMs). These technologies enhance efficiency, innovation, and sustainability in material science, offering new prospects to various industries. Key factors for success in leveraging ML and LLMs in material science include foundational education in ML and LLMs, cross-collaboration between material scientists and data experts, a gradual approach through small-scale pilot projects, effective data management, and ethical considerations in AI ethics and data privacy.

According to a Springer article, advancements in high throughput data generation and physics-informed AI and ML algorithms are rapidly challenging the way materials data is collected, analyzed, and communicated. A novel architecture for managing materials data is being proposed to address the fact that current ecosystems are not well equipped to take advantage of potent computational and algorithmic tools.

The Materials Virtual Lab at UC San Diego has significantly increased the speed and efficiency of materials design by applying first principle calculations and machine learning techniques. These computational methods have transformed the process by streamlining calculations, increasing prediction velocities, and accelerating the discovery of new materials, reducing the time and cost required for data collection and analysis.

As per Arturo Robertazzi, machine learning is gradually integrating itself into the fabric of materials science, lowering barriers to future breakthroughs. Google DeepMind recently announced the discovery of 2.2 million new crystals using Graph Networks for Materials Exploration (GNoME), marking a significant advancement in structure selection and generation algorithms.

In a remarkable collaboration between Microsoft and Pacific Northwest National Laboratory (PNNL), AI and high-performance computing were used to discover a new material, N2116, which could reduce reliance on lithium in batteries by up to 70%. The fusion of AI and high-performance computing stands as a beacon of hope for finding sustainable solutions and reshaping industries.

Overall, the integration of AI and ML in material science marks a significant step in our journey towards a smarter, more sustainable future. These technologies are not just reshaping material science but also redefining our approach to environmental responsibility and sustainable development.

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The Shaping of Material Science by AI and ML: A Journey Towards a Smarter, Greener Industrial Future - Medriva

Vbrick Unveils Powerful AI Enhancements, Driving the Future of Video in the Enterprise – AiThority

Posted on January 12, 2024 by Danzig

Vbrick, the leading end-to-end enterprise video solutions provider, unveiled several new artificial intelligence (AI) capabilities within its video platform in general availability. Adding to its existing suite, Vbricks new AI transforms content management at scale, automates tasks, improves accessibility, and simplifies processes across the enterprise.

In the fast-evolving landscape of digital communication, video has become an indispensable tool for businesses. However, with the exponential rise in video content, from expertly produced training videos and company townhalls to user-created how-to videos and meeting recordings, effectively navigating through vast libraries and ensuring easy access to the right content poses a significant challenge.

Recommended AI News:Riding on the Generative AI Hype, CDP Needs a New Definition in 2024

Vbricks AI-powered enterprise video platform (EVP) transforms how organizations manage, share, and derive value from their video assets, enhancing accessibility, efficiency, and productivity for both content contributors and viewers alike. Building on Vbricks existing AI-powered transcription, translation, and user tagging features, new AI capabilities include:

Video Assistant:Powered by generative AI, Video Assistant extracts key insights from video content using transcripts. Users can increase productivity while posing specific questions to the assistant and receiving real-time responses about the video content.

Summarization:Utilizing generative AI, Summarization allows video owners to automatically create video descriptions based on the video transcript. This not only saves time but also enhances search functionality, simplifies content discovery, and improves video metadata.

Content Intelligence:Leveraging AI and natural language processing, Content Intelligence reviews videos to gain actionable insights instantly. This feature allows moderation of video content for high-value or sensitive material, delivery of personalized video recommendations, and tracking and analysis of video content trends.

Smart Search:Revolutionizing search capabilities with intelligent algorithms that identify concepts, not just keywords, Smart Search leverages vectorized metadata and machine learning to ensure more precise search results, quickly surfacing the most relevant content, interpreting context and intent behind searches, and accommodating diverse search behaviors.

Recommended AI News:World First: Continental Integrates Face Authentication Invisibly Behind Driver Display Console

The totality of an organizations video content is a treasure trove of unused value, said Paul Sparta, Vbrick Chairman and CEO. Vbricks EVP first federates video content, then, our video AI distills the value from the video and makes it consumable and available to the appropriate business process, providing enterprises with the capability to address the rapidly accelerating growth of video in the modern day.

Vbrick caters specifically to enterprise organizations, some of which have amassedvideo libraries exceeding 500 terabytes, stored securely in Vbricks intelligent cloud platform. With additional native video creation, eCDN distribution, live streaming, integrations, and analytic capabilities, Vbricks platform serves as the centralized, secure hub for all video activity within the enterprise.

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With video content aggregated in the Vbrick platform, organizations can truly begin to unlock the value of video by streamlining content discovery, automating tasks, and promoting global accessibility, all while providing an engaging experience for the entire enterprise, said Sparta.

[To share your insights with us, please write tosghosh@martechseries.com]

DOD’s cutting-edge research in AI and ML to improve patient care – DefenseScoop

Posted on January 12, 2024 by Danzig

The Defense Departments responsibility to its active and veteran service members extends to their health and well-being. One organization driving innovation for patient care is the DODs Uniformed Services University. And within the university is a center known as the Surgical Critical Care Initiative, SC2i a consortium of federal and non-federal research institutions.

In a recent panel discussion with DefenseScoop, Dr. Seth Schobel, scientific director for SC2i, shared how cutting-edge research in artificial intelligence and machine learning improves patient care. Schobel elaborated on one specific tool called the WounDx Clinical Decision Support Tool which predicts the best time for surgeons to close extremity wounds.

[These wounds] are actually one of the most common combat casualty injuries experienced by our warfighters. We believe the use of these tools will allow military physicians to close most wounds faster, and it has the potential to save costs and avoid wound infections and other complications. We believe by using this tool well increase the success rate of military surgeons on closing these wounds at first attempt [improving rates] from 72% to 88% of the time, he explained.

Uniformed Services Universitys Chief Technology and Senior Information Security Officer, Sean Baker, joined Schobel on the panel to elaborate on how when IT and medical research teams work together, they can drive better health outcomes in patient care.

Overall, our job is to provide cutting-edge tools into the hands of clinical experts, recognizing that risk management does not mean risk avoidance. Clinical care is not going to advance without taking some measure of digital risks, he explained.

Baker added, We need to continue to empower our users across the healthcare space, across government, to use these emerging capabilities in a risk-informed way to take this into the next level of education, of research, of care delivery.

Schobel and Baker both underlined AI and MLs disruptive potential to positively improve patient care in the near future.

We need to be ready for this [disruptor] by understanding how these tools are built and how they apply in different clinical settings. This will dramatically improve a data-driven and evidence-based healthcare system, Schobel explained. By embracing these considerations, the public health sector, as well as the military, can harness the power of AI and ML to enhance patient care and improve health outcomes, and really be at the forefront of that transformation for the future of healthcare.

Googles Francisco Rubio-Bertrand, who manages federal healthcare client business, reacted to the panel interview, saying: We believe that Google, by leveraging its vast resources and expertise, can be a driving force in advancing research and healthcare. Through access to our powerful cloud computing platforms and extensive datasets, we can significantly accelerate the development of AI/ML models specifically designed to address pressing needs in the healthcare sector.

Watch the full discussion to learn more about driving better patient care and health outcomes with artificial intelligence and machine learning.

This video panel discussion was produced by Scoop News Group for DefenseScoop, and underwritten by Google for Government.

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DOD's cutting-edge research in AI and ML to improve patient care - DefenseScoop

AI 101: Generative AI pioneering the future of digital creativity and automation – Proactive Investors USA

Posted on January 12, 2024 by Danzig

Artificial Intelligence (AI) has made significant strides in recent years, leading to the development of Generative AI, a subset of AI focused on creating new content.

This technology harnesses machine learning algorithms to generate text, images, audioand other forms of media it's not just about creating things that already exist, but also about inventing entirely new creations.

Generative AI operates by analysing vast amounts of data and learning patterns within it.

This enables the AI to produce new outputs that are similar in style, toneor function to its input data.

For example, if it's fed a large number of paintings, it can generate new artworks; if given pieces of music, it can compose new melodies.

Two main types of models are commonly used in generative AI: generative adversarial networks (GANs) and variational autoencoders (VAEs).

GANs involve two parts a generator that creates images and a discriminator that evaluates them.

The discriminator's feedback helps the generator improve its outputs.

VAEs, on the other hand, focus on encoding data into a compressed format and then reconstructing it, allowing the generation of new, similar data.

ChatGPT is a prime example of the intersection between generative AI and large language models, showcasing the capabilities of modern AI in understanding and generating human language.

As a generative AI platform, ChatGPT is designed to generate text-based content in response to user prompts. It can produce a wide range of outputs, including answers to questions, essays, creative stories, code and even poetry.

Its ability to create content that wasn't pre-written but is generated in real-time in response to specific prompts is a defining characteristic of generative AI.

ChatGPT is built on OpenAI's Generative Pre-trained Transformer (GPT) architecture, which is a type of a large language model (LLM).

LLMs are a specialised class of AI model that usenatural language processing (NLP) to understand and generate humanlike text-based content in response.

Unlike generative AI models, which have broad applications across various creative fields, LLMs are specifically designed for handling language-related tasks.

Generative AI's potential is vast and varied. In the creative industries, it is revolutionising how music, artand literature are created.

AI-generated art and music are already making waves, providing artists with new tools to express their creativity.

In business, Generative AI can be a game-changer for marketing and advertising, generating personalised content for targeted audiences.

For instance, AI can create varied versions of an advertisement tailored to different demographics, improving engagement rates.

Healthcare is another sector where generative AI is making an impact. It can assist in drug discovery by predicting molecular structures and their interactions, potentially speeding up the development of new medications.

Furthermore, in technology and engineering, generative AI assists in designing new products and solving complex problems. It can simulate multiple design scenarios, helping engineers optimise their creations.

The ability of AI to generate realistic content raises concerns about misinformation and the creation of deepfakes, which could be used for malicious purposes.

Ensuring the responsible use of this technology is paramount.

There is also the issue of intellectual property rights. When AI creates new content, who owns it? The programmer, the useror the AI itself? These are questions that legal systems around the world are currently grappling with.

Moreover, there's the potential impact on jobs. While generative AI can automate repetitive tasks, potentially increasing efficiency and reducing costs, it also raises concerns about job displacement in certain sectors.

Looking to the future, it's clear that generative AI will continue to evolve and influence various facets of life and industry.

Its ability to analyse and synthesise information at unprecedented scales holds the promise of breakthroughs in numerous fields.

In conclusion, generative AI is not just a technological marvel; it's a catalyst for innovation across sectors.

Its potential for creative expression, problem-solving and personalisation is immense.

However, as we harness its power, it's crucial to address the ethical and societal implications to ensure its benefits are realised responsibly and equitably.

As we step into an era where the lines between human and machine creativity become increasingly blurred, generative AI stands at the forefront, redefining the boundaries of possibility.

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AI 101: Generative AI pioneering the future of digital creativity and automation - Proactive Investors USA

Unleashing the Power of AI: Discover the Mind-Blowing Potential of Machine Learning – Medium

Posted on January 12, 2024 by Danzig

10 min read

1. Introduction: Exploring the World of AI and Machine Learning

Artificial Intelligence (AI) and Machine Learning (ML) have become buzzwords that permeate almost every aspect of our lives. From personalized recommendations on streaming platforms to voice assistants that make our homes smarter, AI and ML are revolutionizing how we interact with technology. In this article, we delve into the mind-blowing potential of AI and explore the endless possibilities that machine learning brings. Whether youre new to the world of AI or an enthusiast looking to gain a deeper understanding, join us on this journey to discover how AI is reshaping industries, the benefits it offers, the challenges it presents, and how you can tap into its power for a better future.

Unleashing the Power of AI: Discover the Mind-Blowing Potential of Machine Learning

1. Introduction: Exploring the World of AI and Machine Learning

1.1 What is AI and Machine Learning? Artificial Intelligence (AI) and Machine Learning (ML) are not just fancy buzzwords; theyre revolutionizing the way we live and work. In simple terms, AI refers to the ability of machines to mimic human intelligence and perform tasks that typically require human cognition. ML, on the other hand, is a subset of AI that focuses on enabling machines to learn from data and improve their performance over time.

1.2 The Evolution and Importance of AI AI has come a long way since its inception. From fictional characters like HAL 9000 to real-life applications like voice assistants and autonomous vehicles, AI has become an integral part of our daily lives. Its importance lies in its potential to solve complex problems, automate repetitive tasks, and make data-driven decisions faster than humans ever could.

And hey, if you want to stay up-to-date with the latest AI trends and news, dont forget to follow me on Twitter! I promise to keep you entertained and informed with my witty take on all things AI.

2. Understanding the Basics: What is Machine Learning?

2.1 Definition and Concept of Machine Learning Machine Learning is like having a personal tutor for computers. Its all about developing algorithms that allow machines to learn from data and make predictions or take actions without explicit programming. In essence, machine learning enables computers to recognize patterns, identify trends, and adapt to new information, just like we do as humans (minus the occasional coffee addiction).

2.2 Types of Machine Learning Algorithms Machine Learning algorithms come in various flavors, each with its own superpowers. We have supervised learning, where machines learn from labeled data to make predictions, and unsupervised learning, where they decipher patterns in unlabeled data to find hidden insights. And lets not forget about reinforcement learning, where machines learn through trial and error, like a determined puppy learning to fetch (and occasionally breaking a vase or two).

2.3 Supervised vs. Unsupervised Learning Supervised learning is like having a teacher guide you through your homework, while unsupervised learning is the joy of exploring new territories on your own. In supervised learning, the machine is given labeled examples to learn from, whereas in unsupervised learning, it discovers patterns and relationships in the data by itself. Its like the difference between solving a math problem with a step-by-step guide versus figuring out a puzzle without instructions.

3. Applications of AI in Various Industries: Real-Life Examples

3.1 AI in Healthcare In the healthcare industry, AI is saving lives and transforming patient care. From diagnosing diseases using medical imaging to developing personalized treatment plans, AI is helping doctors make more accurate decisions and improving patient outcomes. Its like having a brilliant medical assistant who never gets tired or forgets to wash their hands.

3.2 AI in Finance AI is also making waves in the finance industry. With its ability to analyze vast amounts of financial data in real-time, AI-powered algorithms can detect fraud, predict market trends, and optimize investment strategies. Its like having a financial advisor whos always one step ahead and never pressures you into buying that expensive latte.

3.3 AI in Retail In the world of retail, AI is revolutionizing the customer experience. From personalized recommendations based on browsing history to cashier-less stores, AI is making shopping more convenient and tailored to individual preferences. Its like having a personal shopper who knows your style better than you do (but without the judgmental stares).

3.4 AI in Manufacturing Manufacturing is getting a major makeover thanks to AI. From predictive maintenance to optimizing supply chains, AI is streamlining processes, reducing costs, and improving overall efficiency. Its like having a production manager who can predict machine failures before they happen and always knows where to find that missing screw.

4. The Benefits and Challenges of Implementing AI Solutions

4.1 Advantages of AI in Business Processes Implementing AI solutions can bring a myriad of benefits to businesses. It can automate repetitive tasks, increase productivity, improve decision-making, and enhance customer experiences. Its like having a team of super-efficient employees who never complain about Monday mornings or steal your snacks from the office fridge.

4.2 Challenges and Limitations of AI Implementation As amazing as AI is, its not without its challenges. Data quality and availability, algorithm biases, and ethical considerations are just a few hurdles that need to be overcome. Its like trying to teach a mischievous monkey to use proper table manners it takes time and patience.

4.3 Overcoming Ethical and Privacy Concerns AI raises important ethical and privacy concerns that need to be addressed. We must ensure that AI systems are fair, transparent, and respect individual privacy rights. Its like teaching AI to follow the Golden Rule: treat others data as you would like your data to be treated.

Remember, you dont want to miss out on the AI revolution. So, hit that follow button on Twitter and join me in exploring the mind-blowing potential of AI. Lets geek out together!

5. Future Trends: How AI is Evolving and What to Expect When it comes to the future of AI, the possibilities are as endless as a buffet with no time limit. Here are some exciting trends that will make your jaw drop and your brain do somersaults:

5.1 Advancements in Deep Learning Deep learning is like the Olympics of AI, where machines compete to become the Michael Phelps of algorithms. Were talking about models that can learn from vast amounts of data and make mind-blowing predictions. From image recognition to natural language processing, deep learning is leveling up faster than Mario on a quest to rescue Princess Peach.

5.2 AI-powered Automation and Robotics AI isnt just about machines taking over the world like a sci-fi movie plot. Its also about making our lives easier and more efficient. With AI-powered automation and robotics, we can delegate repetitive tasks to smart machines, giving us humans more time to binge-watch our favorite shows on Netflix. Its like having a personal assistant that never needs bathroom breaks.

5.3 Impact of AI on the Job Market Now, before you start panicking about robots stealing your job, lets take a deep breath. Yes, AI will change the job market, but its not all doom and gloom. While some jobs may become obsolete, new opportunities will emerge. Its like a game of musical chairs, where everyone gets a shot at finding a new seat. So, sharpen your skills, stay curious, and embrace the AI wave with open arms (but not too open, we still need hugs).

6. Ethical Considerations: Addressing Concerns and Ensuring Responsible AI Use AI is like a shiny new toy that can bring immense joy, but we shouldnt forget about the potential pitfalls. Here are some ethical considerations to keep AI on the right path:

6.1 Privacy and Data Security As AI gets smarter, the amount of data it needs to consume grows like a teenagers appetite during a growth spurt. This raises concerns about privacy and data security. We need to ensure that the information we feed AI is protected and used responsibly. Nobody wants their secrets leaking out faster than a dropped ice cream cone on a summer day.

6.2 Bias and Fairness in AI Algorithms AI is only as unbiased as the humans who create it. If were not careful, AI algorithms can amplify existing biases and perpetuate discrimination. We need to make sure our algorithms treat everyone fairly, regardless of race, gender, or whether they like pineapple on pizza (we wont judge, promise).

6.3 Transparency and Accountability AI can sometimes feel like a black box, leaving us wondering how it came up with certain decisions. To build trust, we need transparency and accountability. We need to know how AI works and have mechanisms in place to challenge its decisions when they dont make sense. Its like having a magician explain their tricks, but without the disappointment of discovering that rabbits dont really disappear.

7. Getting Started: Practical Steps for Harnessing the Power of AI Ready to dive into the AI pool? Here are some practical steps to make your journey smoother than a babys bottom (figuratively, of course):

7.1 Identifying Opportunities for AI Integration Look around your business or personal life and identify tasks that could benefit from a touch of AI magic. Whether its automating repetitive processes or analyzing mountains of data, theres an AI solution for almost everything. Think of it as finding the perfect tool to fix that leaky faucet or shave that stubborn unibrow.

7.2 Data Collection and Preparation AI runs on data, like a car needs fuel (or a coffee addict needs caffeine). Collect the right data, clean it up, and make it all shiny and presentable for AI to work its magic. Its like organizing your wardrobe before a big night out you want to make sure you look your best and find the perfect outfit in a flash.

7.3 Selecting and Implementing AI With so many AI tools and technologies out there, its easy to get overwhelmed. Take your time, do your research, and find the AI solution that aligns with your needs and goals. Implementing AI is like adopting a pet it requires commitment, care, and a willingness to clean up the occasional mess (both literal and metaphorical).

Remember, AI is not a one-size-fits-all solution, but with a little know-how and a lot of enthusiasm, youll be riding the AI wave like a pro in no time. Now, go forth and unleash the power of AI, but dont forget to follow me on Twitter for more AI-related awesomeness. I promise it wont disappoint (or at least, lets hope not).

In conclusion, the power of AI and machine learning is truly awe-inspiring. As technology continues to advance, we can expect to witness even more mind-blowing applications and advancements in this field. However, it is crucial to approach AI with responsibility and ethical considerations, ensuring that it is used for the betterment of society. By embracing the potential of AI and staying informed about its evolving trends, we can harness its power to create a future that is truly transformative. So, lets embark on this exciting journey together and unlock the boundless possibilities that AI and machine learning have to offer.

FAQ

1. What is the difference between AI and Machine Learning? AI refers to the broader concept of machines exhibiting human-like intelligence, while Machine Learning is a subset of AI that focuses on algorithms enabling machines to learn and make predictions based on data.

2. How is AI being used in different industries? AI is being utilized in various industries such as healthcare, finance, retail, and manufacturing. In healthcare, AI is helping with diagnosis and treatment planning, while in finance, AI is being used for fraud detection and algorithmic trading. Retail businesses are leveraging AI for personalized recommendations, and manufacturing industries are implementing AI for predictive maintenance and process optimization.

3. What are the ethical concerns surrounding AI? Ethical concerns in AI include issues related to privacy and data security, biases in algorithms, and the potential impact on the job market. It is crucial to address these concerns and ensure that AI is developed and implemented responsibly, with transparency, fairness, and accountability in mind.

4. How can businesses harness the power of AI? To harness the power of AI, businesses can start by identifying opportunities for AI integration within their processes and operations. Collecting and preparing relevant data, selecting appropriate AI algorithms, and partnering with experts in the field can help businesses effectively implement and leverage AI solutions for improved efficiency, decision-making, and customer experiences.

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Unleashing the Power of AI: Discover the Mind-Blowing Potential of Machine Learning - Medium

How a CT cardiologist makes his own rules for health and longevity. And he shares them with everyone – Hartford Courant

Posted on January 12, 2024 by Danzig

When Dr. Paul D. Thompson, stepped down from his position at Hartford Hospital, those who know him understood that his retirement didnt mean taking it easy.

At 76, Thompson, now chief of cardiology emeritus with Hartford Hospital, shares the wealth of knowledge from his 50-plus years in medicine. He teaches resident physicians and fellows, and is cataloging his thoughts and observations through snippets of wisdom intended to help other heart doctors.

Thompson calls his catalog of tips his 500 Rules of Cardiology although he admits to not having that many. Im working towards it, he said.

Self-described as a very hard worker, Thompson appears to not take himself too seriously in the larger scheme of life. His sense of humor is obvious and his positive attitude is infectious, those who know his say.

He considers his Pollyannaish optimism a key contributor to his good health.

Living a long, healthful life is heavily influenced by picking the right parents, Thompson quips, but for people without the perfect genes for optimal heart health, he adds, you have to work with the genetic material you have.

If you dont keep a reasonable body weight, that puts stress on your joints, which means that you cant be as active, and that means you dont have as good muscle tone and muscle development. Exercise helps with your heart, blood pressure and glucose. People should stand more and sit less, Thompson said.

Thompson got into medicine as a runner, inspired as a child by watching the 1960 Olympics on TV, he said.

He said he became fascinated by human performance and pushing it to its limits. Starting out as a young doctor, he ran to work just about every day about 6 miles and then back at the end of the day, which sometimes turned out to be 11 at night.

I wanted to try to qualify for the Olympic marathon trials. I knew I wasnt good enough to go to the Olympics, but I wanted to be invited to the trials, he said. Just for fun.

He qualified in 1972.

Later, Thompson, a past president of the American College of Sports Medicine, was doing studies on sudden death in athletes. For instance, someone who dies in the middle of the Boston Marathon. One of his articles showed up in the New York Times and created a snowball effect.

Having run the Boston Marathon himself, I think its been 27 times, Thompson was called to serve as a television medical commentator for two Boston and five New York City marathons. He became NBCs sports medicine analyst at the 1988 Olympic Games in Seoul, Korea and served similarly for ABCs coverage of the 1991 Pan American Games in Cuba, and hes been a guest on Good Morning America nine times.

The media work was fun, but Thompson said it was also a distraction from his real work. He co-edited a three-volume set of books called Exercise and Sports Cardiology, and authored literally hundreds of scientific articles, many of which were focused on athletes and heart health.

Writing, for me, is education because when you put your work on paper, and you have to write words for other people to look at and criticize, you have to learn it better yourself. I find it intellectually interesting. And I think I have the gift, and therefore the responsibility, to do it, Thompson said.

Thompsons 500 Rules of Cardiology can be found as a free subscription on the mobile app and blogging platform Substack. Its very clinical in nature; he calls them helpful principles. His audience is mainly comprised of other cardiologists and new physicians.

I believe I can improve medical care by being a good educator, he explains.

Lifelong learning is an important aspect of Thompsons approach to healthy aging, he said.

Thompson and his wife of 50 years recently returned from a six-week trip to Seville where they completed an immersive Spanish language educational program. During that trip, he made time for some hiking, and presented a lecture via Zoom to a group of doctors in South Africa. Yeah, I do that, he said, matter of factly.

Now hes back home in Simsbury and cataloging his 500 Rules of Cardiology.

One rule stems from the first time he inadvertently discovered a melanoma skin cancer on a patients back during an exam, now encouraging other cardiologists to look at a patients back when appropriate.

Ive found 11 melanomas in the last 20 years. People cant see whats on their back, so why not take a look? he said.

Thompson said he continues to work because it gives him a sense of purpose.

I feel like Im doing something useful. Im making other peoples lives a little better, which makes my life a little better, he said.

Longevity, he said, is not just about living a long time, but living happily. Happiness, purpose and social support are incredibly important, but even happy people go through tough times. Its being resilient to deal with those tough times, and having hope, he said.

Its about finding the good in people and being optimistic, Thompson said.

Marcia Simon is a Connecticut-based writer interested in health, wellness, environment and travel. Her email is marcia@mseusa.com.

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How a CT cardiologist makes his own rules for health and longevity. And he shares them with everyone - Hartford Courant

Infertility: Sperm need a breakthrough for fertilization – EurekAlert

Posted on January 12, 2024 by Danzig

image:

Beating pattern of a human sperm cell before (left) und after (right) activation of CatSper. The more powerful beat is required to fertilize the egg

Credit: University of Mnster / Strnker group

In half of the couples that are unable to conceive a child, the infertility is due to the man. A new study identifies the defective function of CatSper, an ion channel controlling calcium levels in sperm, as a common cause of seemingly unexplained male infertility. CatSper-deficient human sperm fail to fertilize the egg, because they cannot penetrate its protective vestments. Thus far, this sperm channelopathy has remained undetectable. Scientists from Mnster, Germany, have unravelled CatSpers role in infertility using a novel laboratory test that identifies affected men. Based on the results of the study, which has been published in the scientific journal The Journal of Clinical Investigation, diagnostics and care of infertile couples can be improved.

One in six couples fail to conceive a child. The underlying cause often remains unresolved. In fact, in about one third of infertile couples, the mans semen analysis yields no abnormalities in the number, motility, or morphology of the sperm. This poses a problem: the lack of a clear diagnosis prevents an evidence-based selection of a therapy option. As a result, affected couples often experience unsuccessful treatments.

How do men fail to conceive a child despite normal semen parameters? An interdisciplinary team of scientists from the University of Mnster in Germany, set out to answer this question. For quite a while, we have considered CatSper a prime suspect says Prof. Timo Strnker from the Centre of Reproductive Medicine and Andrology (CeRA). Some years ago, Strnker and colleagues revealed that sperm use CatSper as a sensor to detect messenger molecules released by the egg. These molecules activate CatSper, which leads to an influx of calcium into the flagellum, changing its beating pattern.

To scrutinize whether this is essential for fertilization, the researchers developed a simple laboratory test that enabled them to determine the activity of CatSper in sperm from almost 2300 men. This revealed that about one in a hundred infertile men with unremarkable semen parameters indeed features a loss of CatSper function. The most common cause is genetic variants in genes encoding one of CatSpers components, adds the Reproductive Geneticist Prof. Frank Tttelmann, Mnster.

Sperm require the changes in flagellar beating mediated by CatSper to break through the eggs protective coat. Another important finding of the study: CatSper-related male infertility also involves failure of medically assisted reproduction via intrauterine insemination, involving the application of sperm via a catheter into the uterus right before ovulation, or classical in-vitro fertilization (fertilization in the petri dish). This is not surprising, considering that these treatments still require the sperm to break through the egg coat. Affected men/couples could only conceive a child via the ICSI method, which involves the manual injection of a sperm cell into the egg.

Thanks to this comprehensive research endeavour, we can now identify and diagnose this channelopathy, enabling evidence-based treatment of affected couples, summarizes Prof. Sabine Kliesch, Head of the Department of Clinical and Surgical Andrology at the CeRA. Thereby, we minimize the medical risk for the couples and maximize the chances of success.

The function of sperm is not only controlled by CatSper but also various other proteins. These are also in the focus of the Clinical Research Unit (CRU326) Male Germ Cells, which, funded by the German Research Council, provided the collaborative framework for the current study. The overarching aim of the researchers in Mnster is to systematically elucidate the role of these proteins in (in)fertility, improving diagnostics and care of affected couples.

Journal of Clinical Investigation

Human fertilization in vivo and in vitro requires the CatSper channel to initiate sperm hyperactivation

2-Jan-2024

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

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Infertility: Sperm need a breakthrough for fertilization - EurekAlert

SpaceX sends 1st text messages using Starlink satellites – Space.com

Posted on January 12, 2024 by Danzig

Well, that was fast.

SpaceX just broke in its new direct-to-cell Starlink satellites, using one of them to send text messages for the first time.

The milestone came on Jan. 8, just six days after the six Starlink spacecraft launched atop a Falcon 9 rocket from California's Vandenberg Space Force Base, the company announced in an update on Wednesday (Jan. 10).

Those pioneering texts included the classic "New phone who dis?" as well as "Never had such signal" and "Much wow," according to a SpaceX post on X on Wednesday. (SpaceX founder and CEO Elon Musk said the first message was "LFGMF2024," but he was apparently joking.)

Related: SpaceX Starlink satellites to beam service straight to smartphones

Starlink is SpaceX's satellite network in low Earth orbit that provides internet service to people around the world.

The megaconstellation currently consists of more than 5,250 functional spacecraft, but the six that went up on Jan. 2 were the first with direct-to-cell capabilities. (Those half-dozen launched along with 15 traditional Starlink satellites.)

Beaming connectivity service from satellites directly to smartphones which SpaceX is doing via a partnership with T-Mobile is a difficult proposition, as SpaceX noted in Wednesday's update.

"For example, in terrestrial networks cell towers are stationary, but in a satellite network they move at tens of thousands of miles per hour relative to users on Earth," SpaceX wrote. "This requires seamless handoffs between satellites and accommodations for factors like Doppler shift and timing delays that challenge phone-to-space communications. Cell phones are also incredibly difficult to connect to satellites hundreds of kilometers away, given a mobile phone's low antenna gain and transmit power."

The direct-to-cell Starlink satellites overcome these challenges thanks to "innovative new custom silicon, phased-array antennas and advanced software algorithms," SpaceX added.

Overcoming tough challenges can lead to great rewards, and that's the case here, according to SpaceX President Gwynne Shotwell

"Satellite connectivity direct to cell phones will have a tremendous impact around the world, helping people communicate wherever and whenever they want or need to," Shotwell said via X on Wednesday.

The Jan. 2 Starlink launch was SpaceX's first of the year. But there will be many more: The company has said it aims to launch 144 orbital missions in 2024, which would break its record of 98, set last year.

Editor's note: This story was updated at 6:10 p.m. EST on Jan. 11 to include some of the first texts sent via the direct-to-cell Starlink satellites.

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SpaceX sends 1st text messages using Starlink satellites - Space.com

NASA Reportedly Forced to Push Back Moon Landing After SpaceX Fails to Deliver Starship – Futurism

Posted on January 12, 2024 by Danzig

SpaceX still has a lot to prove. Starship Has Sailed

NASA's efforts to return humans to the lunar surface are facing some serious delays.

As Reuters reports, the space agency's first crewed lunar landing mission in over half a century, dubbed Artemis 3 will likely slip from its tentative late 2025 launch date, with insider sources saying that the issue is SpaceX is taking longer than expected to reach certain milestones with its massive Starship spacecraft (you know, the one that keeps exploding.)

Similarly, NASA's Artemis 2 mission, a crewed journey around the Moon and back, will also likely be pushed back due to recently uncovered issues with Lockheed Martin's Orion crew capsule, per the report.

Given the astronomical complexities involved, the news shouldn't come as too much of a surprise. SpaceX has been working at a fever pitch to get its 165-foot stainless steel rocket into orbit and carried out two orbital launch attempts last year both of which ended in,well, don't call them failures but the missions didn't survive.

The plan is to have a Starship Human Landing System spacecraft rendezvous with an Orion spacecraft and ferry NASA astronauts from the Moon's orbit down to the surface.

It's a complicated mission that involves several Starship spacecraft fueling a Moon landing variant in Earth's orbit, before meeting up with the crew hundreds of thousands of miles away.

SpaceX still has a lot to prove, including achieving a stable orbit, swapping fuel between spacecraft, and of course the ability to make a safe and soft approach to the lunar surface.

Despite the delays, NASA is still making progress toward its goal of returning the first astronauts to the lunar surface in over half a century. So far, NASA already has one successful Artemis mission under its belt, having launched an uncrewed Orion capsule around the Moon and back in 2022.

According to Reuters, NASA is expected to announce revised plans today, so stay tuned.

Updated to correctly identify the manufacturer of the Orion crew capsule.

More on Artemis: This Multi-Purpose Moon Habitat Looks Cool as Hell

NASA Reportedly Forced to Push Back Moon Landing After SpaceX Fails to Deliver Starship - Futurism

SpaceX delays launch for third time out of VSFB, cites unfavorable weather – KSBY News

Posted on January 12, 2024 by Danzig

UPDATE (Thursday, Jan. 11) - SpaceX delayed the launch of a Falcon 9 rocket from Vandenberg Space Force Base for the third time, citing unfavorable weather conditions.

The aerospace company is now targeting Saturday, January 13 at 12:59 a.m. for the launch.

___ UPDATE (Wednesday, Jan. 10) - SpaceX is citing unfavorable weather conditions for again delaying the launch of a Falcon 9 rocket from Vandenberg Space Force Base.

The aerospace company is now targeting Friday, January 12 at 12:59 a.m. for the launch.

The rocket will carry 22 Starlink satellites into orbit. ___

UPDATE (6:27 p.m.) - SpaceX delayed its planned launch Tuesday out of Vandenberg Space Force Base to Thursday morning.

The launch is now targeted for Thursday, at 12:59 a.m.

If needed, an additional opportunity is also available on Friday, January 12 at 12:59 a.m. ___

(Tuesday, Jan. 9, 8:50 a.m.) - SpaceX is targeting Tuesday night for the launch of a Falcon 9 rocket from Vandenberg Space Force Base.

Liftoff is scheduled for 9:06 p.m. but backup opportunities are available until 11:28 p.m.

If the launch does not go, SpaceX will try again Wednesday starting at 9:08 p.m.

The launch will mark the 18th flight for the missions first-stage booster, which is expected to land on the Of Course I Still Love You droneship in the Pacific Ocean. No sonic boom is expected to be heard locally.

A live webcast of the launch will begin on X approximately five minutes before liftoff.

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SpaceX delays launch for third time out of VSFB, cites unfavorable weather - KSBY News

SpaceX Vandenberg Launch Delayed Again, This Time To Saturday Morning News Talk 1590 KVTA – KVTA

Posted on January 12, 2024 by Danzig

Thursday January 11, 2024

(File photo courtesy SpaceX)

Updated--SpaceX launch of a Falcon 9 at Vandenberg Space Force Base in northwestern Santa Barbara County has been delayed again, this time until early Saturday morning.

Liftoff is now targeted for 12:59 AM PT, Saturday morning.

Another way of looking at it would be almost an hour after midnight Friday night.

The payload in 22 Starlink Internet Satellites headed for low earth orbit.

The launch was originally scheduled for Tuesday night then rescheduled to Thursday, Friday, and now Saturday morning.

It's unclear if the delay has something to do with the rocket, or maybe the weather because of high winds, or the droneship because of rough seas.

A live webcast of this mission will begin on X @SpaceX about five minutes prior to liftoff. Watch live.

This is the 18th flight for the first stage booster supporting this mission, which previously launched Crew-1, Crew-2, SXM-8, CRS-23, IXPE, Transporter-4, Transporter-5, Globalstar FM15, ISI EROS C-3, Korea 425 and seven Starlink missions.

Following stage separation, the first stage will land on the Of Course I Still Love You droneship, which will be stationed in the Pacific Ocean.

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SpaceX Vandenberg Launch Delayed Again, This Time To Saturday Morning News Talk 1590 KVTA - KVTA

500 Million Reasons to Buy This Cathie Wood SpaceX Competitor in 2024 – The Motley Fool

Posted on January 12, 2024 by Danzig

The space exploration industry has garnered a lot of attention in recent years. The exciting progress of SpaceX, which was founded by Tesla CEO Elon Musk, has helped bring commercial space applications to the mainstream.

One company making noticeable strides is Rocket Lab USA (RKLB -3.37%). The company markets itself as an end-to-end space business -- specializing in launch services, satellite manufacturing, and software used on spacecraft.

At just $5.45 per share, the stock is hovering around all-time lows. Is this a grim sign, or is it possible that the stock is about to take flight?

Let's dig into what may have caused Rocket Lab's sell-off and why the future still looks bright.

Like fellow commercial space businesses Virgin Galactic and Astra Space, Rocket Lab went public through a special purpose acquisition company (SPAC). SPAC mergers had a fleeting moment in the spotlight a couple of years ago as entrepreneurs such as Chamath Palihapitiya and many others looked to democratize access to high-profile start-ups. While the intentions were good, the reality is that many companies that hit the public exchanges through SPACs were still pretty risky. As a result, many SPAC stocks experienced pronounced trading activity with share prices whipsawing all over the place. Unfortunately, many investors were left holding the bag.

Shortly after going public, Rocket Lab stock essentially doubled -- reaching a high of $20.72 per share. Although its cratering share price may not appear alluring, investors should understand some important details.

The space industry requires a significant level of capital investment. Whether it's building rocket ships and satellites or developing software, sending things into orbit is a costly endeavor.

RKLB Capital Expenditures (Quarterly) data by YCharts

The chart above illustrates Rocket Lab's research and development (R&D) and capital expenditures trends over the last several quarters. Unsurprisingly, the rising costs have taken a toll on the company's liquidity.

Given Rocket Lab is not yet generating positive free cash flow, the company's burn rate could extinguish its cash position. These concerns have likely led to a sell-off in the stock as investors realized that each new space business isn't necessarily the next SpaceX or Blue Origin.

Image source: Getty Images.

The dynamics outlined above make it clear that Rocket Lab not only needs to identify new sources of business but needs to do so quickly. According to a recent regulatory filing, on Dec. 21 the company entered into an agreement with an unnamed U.S. government contractor "to design, manufacture, deliver, and operate 18 space vehicles."

Under the contract, Rocket Lab will earn a base amount of $489 million and have the opportunity to collect $26 million of additional incentive pay. While the prospects of a $515 million inflow are encouraging, there are some stipulations investors should be aware of.

First off, the $515 million represents potential future revenue for Rocket Lab. As the graphs above illustrate, consistent profitability is difficult in the space industry. And per the terms and conditions of the deal, these space vehicles won't be delivered until 2027, and full operations aren't expected until 2030.

The long-term nature of Rocket Lab's new contract could be seen as a blessing and a curse. While it adds validation to the company's mission and the quality of its business, it doesn't necessarily solve the liquidity concerns. If anything, the payments from this government deal should extend the company's runway a bit, but it'll still be an uphill battle to long-term profitability.

With that said, Ark Invest CEO Cathie Wood has been scooping up shares of Rocket Lab. Since the company's announcement of its new deal on Dec. 21, Wood has increased her position in Rocket Lab by about 380,000 shares. This could be seen as a positive. However, keep in mind that Rocket Lab is still a relatively small position for her -- ranking 70th among her total holdings.

Investors interested in the space industry may want to consider a position in Rocket Lab. But keep in mind that the stock will likely experience pronounced volatility as it continues to build out its operations. To me, the government contract is a positive sign, and I think more deals will follow. The biggest questions that remain are when new business might enter the pipeline and how quickly the company can recognize those opportunities to strengthen its financial position.

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500 Million Reasons to Buy This Cathie Wood SpaceX Competitor in 2024 - The Motley Fool

Experience the Launch of NASA’s SpaceX Crew-8 Mission – NASA

Posted on January 12, 2024 by Danzig

Editors Note: This article was updated Jan. 9 to reflect the extension of the application deadline to 3 p.m. EST on Thursday, Jan. 11.

Digital content creators are invited to register to attend the launch of the eighth SpaceX Dragon spacecraft and Falcon 9 rocket that will carry crew to the International Space Station for a science expedition mission. This mission is part of NASAs Commercial Crew Program.

The targeted launch date for the agencys SpaceX Crew-8 mission is no earlier than mid-February from Launch Complex 39A at NASAs Kennedy Space Center in Florida. The launch will carry NASA astronauts Matthew Dominick, commander; Michael Barratt, pilot; and mission specialist Jeanette Epps, as well as Roscosmos cosmonaut mission specialist Alexander Grebenkin, to the International Space Station to conduct a wide range of operational and research activities.

If your passion is to communicate and engage the world online, then this is the event for you! Seize the opportunity to see and share the #Crew8 mission launch.

A maximum of 50 social media users will be selected to attend this three-day event and will be given access similar to news media.

NASA Social participants will have the opportunity to:

NASA Social registration for the Crew-8 launch opens on Friday, Jan. 5, and the deadline to apply is at 3 p.m. EST Thursday, Jan. 11. All social applications will be considered on a case-by-case basis.

APPLY NOW

Yes. This event is designed for people who:

Users on all social networks are encouraged to use the hashtag #NASASocial and #Crew8. Updates and information about the event will be shared on X via @NASASocial and @NASAKennedy, and via posts to Facebook and Instagram.

Registration for this event opens Friday, Jan. 5, and closes at 3 p.m. EST on Thursday, Jan. 11. Registration is for one person only (you) and is non-transferable. Each individual wishing to attend must register separately. Each application will be considered on a case-by-case basis.

Because of the security deadlines, registration is limited to U.S. citizens. If you have a valid permanent resident card, you will be processed as a U.S. citizen.

After registrations have been received and processed, an email with confirmation information and additional instructions will be sent to those selected. We expect to send the acceptance notifications on Jan. 17.

All social applications will be considered on a case-by-case basis. Those chosen must prove through the registration process they meet specific engagement criteria.

If you do not make the registration list for this NASA Social, you still can attend the launch offsite and participate in the conversation online. Find out about ways to experience a launch here.

Registration indicates your intent to travel to NASAs Kennedy Space Center in Florida and attend the three-day event in person. You are responsible for your own expenses for travel, accommodations, food, and other amenities.

Some events and participants scheduled to appear at the event are subject to change without notice. NASA is not responsible for loss or damage incurred as a result of attending. NASA, moreover, is not responsible for loss or damage incurred if the event is cancelled with limited or no notice. Please plan accordingly.

Kennedy is a government facility. Those who are selected will need to complete an additional registration step to receive clearance to enter the secure areas.

IMPORTANT: To be admitted, you will need to provide two forms of unexpired government-issued identification; one must be a photo ID and match the name provided on the registration. Those without proper identification cannot be admitted.

For a complete list of acceptable forms of ID, please visit: NASA Credentialing Identification Requirements.

All registrants must be at least 18 years old.

Many different factors can cause a scheduled launch date to change multiple times. If the launch date changes, NASA may adjust the date of the NASA Social accordingly to coincide with the new target launch date. NASA will notify registrants of any changes by email.

If the launch is postponed, attendees will be invited to attend a later launch date. NASA cannot accommodate attendees for delays beyond 72 hours.

NASA Social attendees are responsible for any additional costs they incur related to any launch delay. We strongly encourage participants to make travel arrangements that are refundable and/or flexible.

If you cannot come to the Kennedy Space Center and attend in person, you should not register for the NASA Social. You can follow the conversation online using #NASASocial.

You can watch the launch on NASA Television or http://www.nasa.gov/live. NASA will provide regular launch and mission updates on @NASA, @NASAKennedy, and @Commercial_Crew.

If you cannot make this NASA Social, dont worry; NASA is planning many other Socials in the near future at various locations! Check backherefor updates.

Excerpt from:

Experience the Launch of NASA's SpaceX Crew-8 Mission - NASA

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