Revolutionizing healthcare: the role of artificial intelligence in clinical practice
Monalee Uses The Power Of AI To Slash The Price Of Solar For US Homeowners
Calling itself the world's first "AI-driven solar company," Monalee is cutting the soft costs associated with solar by more than 50%, and they're passing those savings – sometimes thousands of dollars – directly to the homeowner.
The solar industry has seen a number of high-profile closures and bankruptcies in recent months, but Monalee is a venture-backed, digital-first startup that's just getting started … and that's significant in an industry where most of the work is still done offline. How does being "digital-first" shape the home solar experience for customers? It starts with they get a quote for solar – a process that takes just seconds with Monalee. A person just enters the address that they want to add solar to and they'll receive an instant design and quote.
From there, Monalee's technology takes over: Monalee's AI-powered machine learning software uses satellite imagery to figure out a roof's plane, azimuth, and pitch the software identifies roof obstacles like chimneys and roof vents and plans around them a computer vision algorithm virtually places solar panels on the roof based on all the data it's accumulated the software runs a number of simulations on that data, using solar irradiance algorithms to determine how much sunlight is hitting each area of the roof, then optimizes solar panel placement accordingly once it understands what equipment will deliver the best results (what panels, what controllers and accessories will be needed to support those panels, mounting hardware, conduit, cables, etc.), the software layers in other essential information, including financing options from top-tier partners like GoodLeap and Mosaic, and any available federal, state, and utility incentives into its calculations to deliver a quote that's up to 85% accurate That 85% number is incredibly important, by the way, because it means that there will be fewer change orders (modifications to the initial quote) later on, and every one of those adds cost to the final bill. Even small change orders can really mess with a project's timeline and put more financial burden on the homeowner, so avoiding a "surprise" upgrade of an electrical panel or unexpected roof repair is mission-critical. Getting that quote right the first time can cut the total project time in half! 
What's more, because Monalee can generate these accurate quotes without incurring some of the traditional "soft costs" of cold callers, marketers, sales staff, and estimators, they can pass those savings on to customers. Those savings can be significant, too. In fact, Monalee has saved more than 2,000 customers an average of $12,000 across 24 US states (so far). Keep in mind, though — just because they've streamlined the process, it doesn't mean they're not experts. The team behind Monalee's tech is also top-notch. It's been featured by Time and NPR, and its employees bring years of solar experience from companies like Tesla, Blue Raven, and SolarCity to the table. And, they're fully dedicated to getting homeowners excited about adding affordable home solar to their properties. 
Anyone can get a solar quote for free, and without any obligation. That's real pricing transparency that customers can trust and appreciate—especially customers in the technology space, who can appreciate just how innovative this AI-powered solar solution really is. To date, Monalee has served more than 2,000 homeowners across 24 U.S. States, with more states being added monthly. On average, homeowners save upwards of $12,000 with Monalee without ever sacrificing the quality of equipment. In fact, Monalee installs include Tier One Mitrex solar panels, Tesla inverters, and the Tesla Powerwall 3. If you're interested in seeing how much you can save, you can get a quote in 20 seconds here. Going one step further, Monalee offers lifetime warranties for solar equipment. This means that, if at any point during a homeowner's lifetime, a piece of equipment stops working, the repair or replacement is fully covered through Monalee (for comparison, most warranties through the manufacturer typically cap at 25 years for solar panels and 10 years for solar batteries). Monalee is a climate tech company that is accelerating the adoption of home solar, storage, and EV charging. By leveraging powerful machine learning and removing salespeople and system designers from the process, they are able to secure the same solar panel systems for homeowners at half the price compared to the top traditional solar companies in the U.S., making them the fastest, most efficient way to go solar. Check out Monalee on Facebook here, Twitter here, and Instagram here. FTC: We use income earning auto affiliate links. More. How To Navigate The Challenges Of ML And AI In Pharmaceutical R&D
Navigating the integration of machine learning (ML) and artificial intelligence (AI) in pharmaceutical R&D involves overcoming data management, quality, and expertise challenges to unlock their full potential in drug discovery, reveals Richard Lee, Director, Core Technology and Capabilities, ACD/Labs
Pharmaceutical companies are under constant pressure to innovate and bring new drugs to market efficiently and cost-effectively. However, the drug discovery and development process is complex with challenges that can significantly slow progress. One of the most promising avenues to address these challenges is the integration of machine learning (ML) and artificial intelligence (AI) into research and development (R&D). Despite the potential, implementing these technologies is not without its hurdles.
One of the most significant challenges pharmaceutical companies face when implementing ML and AI in R&D is managing the sheer volume and diversity of data generated by modern scientific instrumentation. Drug discovery involves complex datasets from techniques such as liquid chromatography, mass spectrometry, and nuclear magnetic resonance (NMR) spectroscopy. This data must be efficiently captured, organized, and interpreted before it can be used in ML/AI models.
Data heterogeneity, assembly, and qualityA key issue within this challenge is data heterogeneity. Data generated by different instruments and experiments often come in various proprietary formats across vendors. Integrating these disparate datasets into a coherent format usable by ML/AI models requires significant preprocessing. This preprocessing can involve normalization, standardization, and the translation of data into a common format, which is not only time-consuming but also prone to errors if not handled meticulously.
The integration and assembly of data represents a significant challenge within the pharmaceutical industry. Analytical data, in isolation, is insufficient to provide the full context of a chemical experiment. It is frequently the aggregation of analytical data, coupled with comprehensive experimental details, that is necessary to present a complete and coherent understanding of a chemical study.
Moreover, the quality of data is another critical concern. ML and AI models are only as good as the data used to create the model. Data collected from experiments can contain missing metadata, or outliers, all of which can skew the results of ML/AI models if not addressed. Ensuring data quality through rigorous validation, cleaning, and curation processes is essential to build reliable models. However, this task is often resource-intensive and requires expertise in both domain knowledge and data science.
Data accessibility and integrationOnce data is standardized and cleaned, another challenge arises in making it accessible and integrable across different systems. Pharmaceutical companies often operate with legacy systems and siloed data repositories, making it difficult to create a unified data environment. The integration of structured data from various sources, such as experimental data, is essential for training comprehensive ML models.
Lowering the barrier to ML/AI integrationEven when pharmaceutical companies have structured data, the next challenge lies in the expertise required to develop and implement ML/AI models. Many organizations lack the specialized skills needed to create ML/AI models. This skills gap can be a significant barrier to adopting advanced technologies, as it necessitates either building a specialized team or relying on external consultants, both of which can be costly and time-consuming.
ACD/Labs and ML/AI in pharmaceutical R&DThe challenges pharmaceutical companies face in implementing ML and AI are significant, but not insurmountable. ACD/Labs' provides technologies that lay the foundation for data to be accessible by ML/AI applications. The Spectrus platform allows an organization to standardize and assemble analytical data with chemical context. This can be done through automation services, including data marshalling, format standardization, data processing, and data assembly. The Spectrus platform can also integrate with other informatics systems in IT ecosystems through its extensive APIs.
Moreover, ACD/Labs has been providing predictive analytical modules such as NMR spectral and physiochemical property prediction based on ML. These modules are considered the gold standard in the chemical informatics industry. Recently, ACD/Labs' Katalyst D2D, the high throughput chemistry application, has been integrated with open source ML module Experimental Design via Bayesian Optimization (EDBO) to enhance and accelerate screening experiments. EDBO is a powerful algorithm that optimizes chemical reactions by iteratively suggesting new conditions based on prior experimental results. By embedding this ML capability directly into Katalyst, ACD/Labs lowers the barrier to entry for pharmaceutical companies, enabling them to leverage AI-driven optimization without needing deep expertise in machine learning.
In support of other ML/AI frameworks and platforms, ACD/Labs' has adopted a collaborative approach by partnering with leading ML/AI companies such as Atinary. Atinary specializes in AI-driven experimental design and optimization, and their collaboration with ACD/Labs brings additional AI methodologies into ACD/Labs' software suite. This partnership enables ACD/Labs to offer pharmaceutical companies more comprehensive and sophisticated ML/AI solutions, ensuring that these technologies are seamlessly integrated into existing R&D workflows.
By providing out-of-the-box solutions like the EDBO-enhanced Katalyst and collaborating with AI leaders like Atinary, ACD/Labs is empowering pharmaceutical companies to overcome the hurdles of ML/AI implementation. This approach not only accelerates drug discovery and development but also drives innovation and efficiency across the R&D process.
Palantir Was Recognized As A Leader In AI/ML Platforms.
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Palantir has been recognized as a leader in artificial intelligence and machine learning platforms.
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Palantir Technologies Inc., a leading provider of AI software platforms, said on Thursday that it had been recognized as a leader in artificial intelligence and machine learning (AI/ML) software platforms by renowned research and advisory firm Forrester.
Palantir was among the select companies that Forrester invited to participate in "The Forrester Wave™: AI/ML Platforms, Q3 2024" report, and was cited as a leader in this research, with the highest ranking for Current Offering, the company said.
As stated in the report, "Palantir has one of the strongest offerings in the AI/ML space, with a vision and roadmap to create a platform that brings together humans and machines in a joint decision-making model. Its approach is to use its data pipelining capabilities and differentiated ontology to support the basis of its AI platform (AIP) offering… Palantir is quietly becoming one of the largest players in this market, seeing a consistent sustained growth rate in the past half decade by making its platform more accessible to users, investing in customer success, and embracing the support of multirole AI teams."
Akshay Krishnaswamy, Palantir's Chief Architect, said, "Palantir AIP powers the most demanding use-cases across the public and private sector, and is uniquely designed to connect AI directly into frontline operations. We believe that being named a Leader in this Forrester Wave evaluation validates our investments across model-agnostic Generative AI infrastructure, multimodal guardrails for human-AI teaming, the decision-centric Ontology — and the full range of other capabilities needed to take enterprises from AI prototype to production."
From public health to battery production, organizations depend on Palantir to safely, securely, and effectively leverage AI in their enterprises — and drive operational results, Palantir said.
Sompo Enhances Digital Transformation with Palantir's AI SolutionsOn Aug 23, 2024, Palantir said that Sompo, a subsidiary of the company responsible for the insurance and reinsurance operations of Sompo Holdings Group outside of Japan, has formed a partnership with its company. This initiative plans to invest over the next three years in a data integration and AI solution to drive digital transformation at the insurer, which is among the top five in the Corporate and Agribusiness insurance segment in Brazil.
Rodrigo Caramez, Sompo's Chief Strategy Officer, said: "Historically, the search for efficiency involved automating simple, repetitive, and high-volume processes. This still applies, but with the new technological frontier applied to the use of data and AI, and the availability of simpler tools with natural language, we can expand this search for efficiency to the organization's highly specialized core processes. The challenge now is to ensure that the team is involved and prepared for this new moment."
Tiago Fetter dos Santos, Head of Financial Services & Government in Brazil for Palantir Technologies, said: "We are proud to partner with Sompo on their Data Analytics and AI journey and support their digital transformation to optimize critical processes for the benefit of the business and its customers. The impressive results that we are already delivering together underline our conviction that the company's bold vision, combined with the adoption of Palantir's software, will give them a technology-driven competitive advantage in the Brazilian market."
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