Project Overview - Kadustra's data science team embarked on a project to brew beer using AI, aiming to improve upon existing data-driven brewing approaches [2][3] - The project involved two iterations: the first with the Kadustra team and the second with H colleagues and students, each with different goals and AI approaches [2][10] AI and Data Science Application - The team used an XGBoost model to predict beer scores based on recipes from Brewok and Untappd scores [3][4] - For the second iteration, a Bayesian approach and a hop prediction model were used to refine flavor profiles and hop selection, addressing the challenge of over 10 billion options [16][17] - AI was also used for label design (DALL-E) and text generation (GPT), including embracing "hallucinations" [6] Results and Lessons Learned - The first beer achieved a predicted Untappd score of 43%, but the actual score was 34%, indicating room for improvement in taste [8][9] - The second beer, "the honzy pie," achieved a predicted Untappd score of 38%, with an actual score of 36%, considered a success [19] - The project demonstrated that AI can be used in brewing, but craftsmanship and local ingredients are also crucial for success [8][10] Marketing and Publicity - The beer brewing project was used as a team-building activity and a giveaway at an induction event for a new professor [1][10] - The project generated publicity, including coverage on television and in newspapers [1][19] Educational and Recruitment Aspects - The project aimed to attract students to study EI (Artificial Intelligence) at the Han university [2][20] - The university offers a master's program in applied data science and AI, with limited spaces available in September and more spaces in February [21]

Good morning everybody. During our school days, all that we did was we were just provided good food and then we just went to school, played and uh we spent a lot of our time playing and all that we need to do is just survive to get some good marks so that uh we are able to clear that 40 50 marks and then when we move to a middle school or little higher our all our dreams were only to get a good group uh normally a max physics, chemistry, biology group and then only when we went to class 11 and 12 we were ju ...

"AWS Certified AI Practitioner Early Adopter"I am a DevOps Engineer for a major, wholly owned subsidiary of a large-cap Fortune 500. I am a true subject-matter expert on the actual buildout, deployment, and maintenance of AI tools and applications. I have increasingly deep knowledge on the science behind generative AI systems as a result of first-hand experience with machine learning algorithms, model training, and model deployment.I am currently in the process of obtaining more advanced AWS machine learnin ...

Model Training Strategy - The initial approach of capturing user images and training a binary classifier for face unlock is flawed due to the need for on-device training and the difficulty of obtaining "Class 0" samples [1][2] - A Siamese Network trained via Contrastive learning offers a more suitable solution for face unlock systems [2] - Contrastive learning maps data points to a shared embedding space, where low distance indicates similarity and high distance indicates dissimilarity [3] - The system creates a dataset of face pairs, labeling pairs of the same person as 0 and different people as 1, then trains a supervised model [3] - A neural network generates embeddings for each image, and the distance between embeddings is minimized for similar faces and maximized for dissimilar faces using contrastive loss [4] - The contrastive loss function, L = (1-y)*D^2 + y*max(0, margin-D)^2, guides the model to produce low distances for similar inputs and high distances for dissimilar inputs [5] Face Unlock System Implementation - During setup, the user's facial data generates a reference embedding, and subsequent unlocks compare new embeddings against this reference embedding without further training [6] - New identities can be added by creating additional reference embeddings [6] - During unlock, the incoming user's embedding is compared against all reference embeddings [7]

Company Overview - Rail Vision (RVSN) is valued at a market cap of $27 million and specializes in developing railway detection systems aimed at enhancing operational safety, efficiency, and predictive maintenance [1] - The company utilizes specialized cameras and onboard computers to monitor train operations, prevent collisions, and reduce downtime, while also offering cloud-based data services and geographic mapping systems to railway operators globally [1] Recent Developments - Rail Vision has signed a term sheet to acquire 51% of Quantum Transportation, a startup focused on error correction technology for quantum systems, marking an unusual move into quantum computing [2] - The acquisition deal involves issuing shares worth approximately 5% of Rail Vision's capital to certain shareholders of Quantum Transportation and providing a convertible loan of up to $700,000 at an 8% annual interest rate to support operations [3] Technology and Applications - Quantum Transportation has developed a patented machine learning decoder that addresses noise in qubits, a significant challenge in quantum computing, which can assist smaller quantum companies in testing error correction methods [4] - Rail Vision plans to leverage this intellectual property for railway applications, including predictive maintenance and autonomous operations [4] Financial Performance - Rail Vision reported mixed financial results for the first half of 2025, ending Q2 with $22.3 million in cash, an increase from $17.2 million in 2025, providing a runway for growth strategy execution [5] - Revenue for the first six months of 2025 decreased by 69% year-over-year to $237,000 from $761,000, attributed to timing issues with revenue recognition rather than lost business [6] - The majority of revenue was generated from additional MainLine system installations for Israel Railways and servicing existing customers [6] - Research and development spending increased to $3.2 million from $2.5 million, driven by higher headcount, salary increases, and currency headwinds due to the weakening of the dollar against the Israeli shekel [7] - General and administrative costs rose to $2.5 million from $2.1 million, influenced by one-time employee bonuses and stock-based compensation [7]

Core Insights - Western Digital Corporation (WDC) is experiencing significant growth due to increasing demand for high-capacity storage driven by cloud computing and AI [1] - The company is focused on innovation, delivering high-capacity drives with enhanced performance and energy efficiency [1] Group 1: Financial Performance - In the fiscal fourth quarter, WDC shipped 190 exabytes of storage, a 32% increase year over year, with strong demand for nearline drives and significant growth in 26TB CMR and 32TB UltraSMR products [2] - For the first quarter of fiscal 2026, WDC anticipates non-GAAP revenues of $2.7 billion, representing a 22% year-over-year increase, and non-GAAP earnings of $1.54 [5] Group 2: Product Development and Innovation - WDC's ePMR and UltraSMR technologies are crucial for success in the data center market, with plans to advance to next-generation HAMR drives, expected to ramp up in the first half of 2027 [3] - The company is also focusing on infrastructure solutions for AI/ML and software-defined storage, targeting hyperscale cloud service providers [4] Group 3: Market Position and Competition - WDC faces competition from companies like Seagate, Pure Storage, Hitachi, Samsung, and Intel in the storage market [6] - Despite strong growth, customer concentration and a leveraged balance sheet are concerns for the company [6] Group 4: Stock Performance and Valuation - Over the past year, WDC shares have increased by 97.7%, outperforming the Zacks Computer-Storage Devices industry, which grew by 35.1% [10] - WDC's shares are trading at a forward price/earnings ratio of 19.87X, lower than the industry's 22.68X [11] - The Zacks Consensus Estimate for WDC's earnings for fiscal 2026 has been revised up by 1.85% to $6.62 per share [12]

Key Points CoreWeave stands out with its cloud servers designed specifically for AI workloads. Nvidia, OpenAI, and Meta Platforms each made multibillion-dollar deals with CoreWeave in September. Its considerable losses could overshadow the company's massive revenue growth. 10 stocks we like better than CoreWeave › CoreWeave (NASDAQ: CRWV) stock has logged considerable gains in September. It has benefited from high-profile deals. The first was with Nvidia, in which the chip giant agreed to purchas ...

Key Points Maxar Technologies got taken private and off the stock market more than two years ago. Now, it's losing its name as private equity company Advent rebrands the two halves of what used to be Maxar. Say so long to Maxar. And say hello to "Vantor" and "Lanteris." These 10 stocks could mint the next wave of millionaires › 2022 was the end of an era for space investors. Five years prior, Canadian space stock MacDonald, Dettwiler and Associates (MDA) had acquired DigitalGlobe, America's lead ...

Core Insights - Centersquare, owned by Brookfield Infrastructure, has completed a $1 billion acquisition of ten data centers in the US and Canada, expanding its portfolio to 80 facilities [1][2] - The acquisition includes eight colocation facilities located in Dallas, Tulsa, Nashville, Raleigh, Toronto, and Montreal, enhancing Centersquare's power and space capacity for high-density computing workloads [2][3] - The move is driven by increasing demand for scalable, energy-efficient, and resilient data center infrastructure, particularly due to the rise of AI adoption [3][4] Company Strategy - Centersquare is focusing on investments that support GPU-intensive workloads necessary for generative AI, machine learning, and advanced analytics [4] - The company is positioning itself to capture the growing demand for trusted, high-performance infrastructure by adding capacity in strategic markets [3][4] - Centersquare has experienced growth in its customer and revenue base over the last two years, developing a strong cost-efficient operating platform [5] Market Context - The acquisition comes at a time when enterprises are shifting off-premises, reevaluating public cloud strategies, and adopting AI-driven architectures that require higher power densities [4] - Centersquare operates colocation facilities across key North American and UK markets, providing infrastructure services to sectors such as technology, financial services, healthcare, and media [5]

TOKYO, Oct. 06, 2025 (GLOBE NEWSWIRE) -- Leading semiconductor test equipment supplier Advantest Corporation (TSE: 6857) will feature its latest semiconductor test solutions at SEMICON West 2025, which will be held from Oct. 7-9 at the Phoenix Convention Center in Phoenix, Ariz. Advantest will highlight its broad portfolio of leading-edge test technologies for applications, including AI and high-performance computing (HPC), 6G, automotive and memory. Additionally, the company is a proud sponsor of this year ...