Mediaboss Marketing

A chilled CBD-infused Labatt Breweries beverage is coming to a market near you this December.

Fluent Beverage Company, the joint-partnership between the massive brewer Anheuser-Busch Inbev NV (EBR:ABI) and global cannabis pioneer Tilray Inc. (NASDAQ:TLRY), announced this week it will commercialize a non-alcoholic, CBD-infused beverage for Canadians likely hitting markets in December 2019.

Beer drinkers will know Anheuser-Busch by its Canadian subsidiary Labatt Breweries, which employs over 3,400 canucks and brews Budweiser, Kokanee, Stella Artois, Corona, Palm Bay and Mikes Hard Lemonade, to name a few.

The joint venture was announced in December 2018 when High Park, a wholly-owned subsidiary of Tilray, and Labatt partnered to research a non-alcoholic drink containing weed cannabinoids tetrahydrocannabinol (THC) and cannabidiol (CBD).

Each company is investing up to $50 million in the partnership, according to Benzinga.

The companies need more time to research beverages containing THC and will only be providing CBD-drinks in December, Fluents chief executive Jorn Socquet told the Canadian Press.

THC, the intoxicating compound in cannabis, is unstable and degrades too quickly for a reasonable shelf life whereas CBD, the non-intoxicating compound, remains potent and stable for longer, said Socquet.

What the drink will actually look like, taste like, or smell like isnt being revealed, but Socquet told the Canadian Press the non-alcoholic CBD-infused drink will likely be sparkling, slightly sweet and tea-like.

The partnership between Labatt and Tilray comes after two similar beer and weed partnership announcements from August 2019.

Molson Coors Brewing Co. (TSX:TPX.B) and Quebec-based HEXO Corp. (NYSE:HEXO) are partnering to get cannabis-infused non-acloholic drinks to Canadians, and Constellation Brands Inc.(NYSE:STZ)(NYSE:STZ.B) bought a 38 per cent majority share of Canopy Growth Corp. (NYSE:CGC)(TSE:WEED) in August to invest in a similar venture.

Canadians wont be able to crack a cold CBD one till the government passes the second wave of cannabis legalization, set for October 17 which will legalize beverages, edibles, vapes and topicals. Even then consumers will have to wait 60 days while companies give a mandatory notice to Health Canada before drinks sales kick off.

If everything goes according to plan, expect the tsunami of CBD-drinks to hit one week before Christmas.

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Finlay Minerals to use machine-learning to increase exploration success in British Columbia project - Mugglehead

HOLMDEL, N.J., June 13, 2023 /PRNewswire/ -- Over 67 percent of CISOs plan to embrace new technology including machine learning tools to detect ransomware activity over the next year, research conducted by Evaluator Group determined, with earlier detection of ransomware corruption and support for faster discovery of the last clean backup the top analytics requested.

"Machine learning and analytics are critical in the race against cyber criminals"

Evaluator Group conducted a survey of 163 CISOs to define the top data management challenges, at the behest of Index Engines, whose CyberSense software detects signs of data corruption due to ransomware and facilitates an intelligent and rapid restoration.

"Machine learning and analytics are critical in the race against cyber criminals and CISOs have realized this," said Jim McGann, VP of Business Development and Marketing at Index Engines. "Ransomware attacks are getting more sophisticated, evading thresholds and metadata-level security tools. Machine learning and analytics can observe data, look deep into files and make deterministic decisions on whether it's been corrupted by ransomware or give you confidence that it's clean for recovery."

CISOs struggle to detect attacks and find the last known good copy of data for recovery, the study found, along with bare minimum recovery expected to take hours with full recovery expected to take weeks or months often resulting in data that is forever lost due to malicious corruption.

Currently, security professionals lack in-house ability to use deep forensic analysis to determine what happened and how to recover intelligently, the report stated. Only 11% of respondents indicated they have all the capabilities they need from their current vendors.

Two-thirds of the respondents said they plan to add data analytics and/or machine learning tools to detect suspicious activity over the next year, the report showed. More than half said they planned to add data loss prevention software and tools to continuously monitor for malicious software. Rounding out the top five choices were audit data for sensitive content (48%) and data forensics analysis for post-ransomware attack (47%).

Budgets are increasing to support the increasing sophistication of ransomware attacks, the report showed, with 84% reporting their cyber security budget is increasing this year, with 49% of budgets increasing up to 10%. Only 12% said it would increase more than 25%, the same number who said there would be no change. Only 4% said their cybersecurity budget is decreasing.

When asked what they wanted most for cyber resiliency analytics, 71% of respondents said "earlier detection of a cyberattack," with 43% listing "faster identification of last known good recovery point" and 41% selecting "increased confidence that malware was eradicated from the environment."

"Organizations need features such as anomaly detection and the ability to find the last known good copy of data following an attack to fully recover," Evaluator Group senior analyst Dave Raffo said. "Data forensics tools and processes that focus on analyzing, identifying, monitoring and reporting on digitally stored data can help facilitate successful data recovery."

To read the full report, go to: https://go.indexengines.com/eg_data_management_challenges_CISO

SOURCE Index Engines

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The AI and machine learning (ML) craze taking tech by storm is a gold rush. And as in a traditional gold rush, there are plenty of picks and shovels to be sold.

For large language models and other cutting-edge AI models, the tools come in the form of specialized chips for more efficient computing. Chipmaker Nvidia was propelled briefly to a trillion-dollar market cap due to interest in its AI-focused graphics cards. Startups around the world are designing their own hardware that is optimized for AI and ML applications.

The market map below outlines the global AI and ML VC ecosystem and where the capital is going. Explore the AI and ML semiconductors segment by clicking on the blue tile below.

Notable deals include Moore Threads, an AI chip startup that raised $213.2 million in venture funding, and Bolttech, an insurtech startup using AI to automate processes, which raised a $300 million Series B.

Almotive, a startup creating automated driving systems, was acquired by the auto conglomerate behind Fiat and Chrysler Stellantis for an undisclosed amount in December. ECARX, a startup developer of AI-centric chips acquired COVA Acquisition for $300 million and went public in December.

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Market map: Investors bet on the chips powering AI and machine ... - PitchBook News & Analysis

Exploring the Impact of Machine Learning on Finance and Banking Transformation

Machine learning, a subset of artificial intelligence, has been making waves in various industries, and the finance and banking sectors are no exception. Financial institutions have been quick to recognize the potential of this technology, as it can provide them with a competitive edge by enabling them to make more informed decisions, streamline operations, and improve customer experiences. As a result, machine learning is rapidly changing the face of finance and banking, transforming the way these industries operate and reshaping their future.

One of the most significant impacts of machine learning in finance and banking is the ability to analyze vast amounts of data quickly and accurately. Financial institutions generate and process massive amounts of data daily, including customer information, market trends, and transaction records. Machine learning algorithms can sift through this data, identify patterns and trends, and make predictions based on the analysis. This capability allows banks and financial firms to make more informed decisions, such as identifying potential investment opportunities, detecting fraudulent activities, and managing risk more effectively.

Risk management is a critical aspect of finance and banking, and machine learning is playing a vital role in enhancing this function. Traditional risk assessment methods rely on historical data and human judgment, which can be time-consuming and prone to errors. Machine learning algorithms, on the other hand, can analyze large datasets in real-time, identifying potential risks and suggesting appropriate mitigation strategies. This not only improves the accuracy of risk assessments but also enables financial institutions to respond more quickly to emerging threats.

Fraud detection is another area where machine learning is making a significant impact. Financial fraud is a growing concern, with cybercriminals constantly developing new tactics to exploit vulnerabilities in banking systems. Machine learning algorithms can help detect and prevent fraudulent activities by analyzing transaction data for unusual patterns and flagging suspicious activities for further investigation. This proactive approach to fraud detection not only helps protect financial institutions and their customers from losses but also enhances trust in the banking system.

Machine learning is also transforming the customer experience in finance and banking. By analyzing customer data, financial institutions can gain insights into individual preferences and behaviors, enabling them to offer personalized products and services. For example, machine learning algorithms can help banks identify customers who may be interested in a particular investment product or who may be at risk of defaulting on a loan. This targeted approach to marketing and customer service not only improves customer satisfaction but also helps financial institutions optimize their resources and increase revenue.

In addition to these applications, machine learning is also being used to streamline operations and improve efficiency in finance and banking. For instance, machine learning algorithms can automate routine tasks, such as data entry and report generation, freeing up employees to focus on more strategic activities. Furthermore, machine learning can help optimize trading strategies, portfolio management, and asset allocation, leading to better investment performance and reduced costs.

Despite the numerous benefits of machine learning in finance and banking, there are also challenges to overcome. Data privacy and security concerns are paramount, as financial institutions must ensure that sensitive customer information is protected while leveraging machine learning capabilities. Additionally, there is a need for skilled professionals who can develop and implement machine learning algorithms, as well as a need for ongoing education and training to keep up with the rapidly evolving technology.

In conclusion, machine learning is revolutionizing the finance and banking sectors, offering significant benefits in terms of data analysis, risk management, fraud detection, customer experience, and operational efficiency. As financial institutions continue to embrace this technology, we can expect to see even more innovative applications and transformative changes in the industry. However, it is crucial for these institutions to address the challenges associated with machine learning, ensuring that they can harness its full potential while maintaining the trust and security of their customers.

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How Machine Learning is Changing the Face of Finance and Banking - CityLife

Machine Learning: How AI Learns and Improves The Online Casino Experience

Artificial Intelligence & Machine Learning

Unbeknownst to many, machine learning and AI have seamlessly infiltrated our daily routines. Whether its targeted ads on social media, tailored content on streaming platforms, facial recognition for device access, or the efficiency of banking and transportation apps, AI has become an indispensable part of our lives. Its pervasive presence across industries has made it difficult to envision a world without its transformative capabilities.

Machine Learning (ML) is definitely transforming the online gaming industry and enhancing the overall player experience. These technological advancements have completely transformed the operations of the best online casinos as a result. For casino enthusiasts, these algorithms produce personally tailored, safe, and compelling experiences by studying player data and real-time optimization. We will further delve into the inner workings of AI algorithms, exploring how they learn, adapt, and optimize the online casino experience for players worldwide.

In the glorious virtual world of online casinos, AI and ML have revolutionized how player data is collected, analyzed, and utilized to boost the overall casino experience and online gaming world. With help from the new wave of AI power, machine learning algorithms analyze players data to understand player behavior, preferences, and gaming patterns.

The AI algorithms have the amazing capacity to learn and develop over time. This results in unprecedented levels of customized gaming by delving into player data to gain valuable insights. Data collection starts from the first moment a player enters an online casino, where every click and action is recorded. AI algorithms process the players game selection, betting patterns, or time spent on different activities to provide more specialized content in the future.

AI algorithms never stop learning and adapting, and they are always one step ahead in predicting player preferences and behaviors. Due to that, they are always able to offer up-to-date recommendations specially tailored to each player. By having such a high level of personalization in the online gaming industry, players have increased their loyalty, engagement, and interest in the digital gaming realm.

It makes the whole user experience much more friendly, as players dont have to spend hours searching through gaming websites. Instead, players now have nicely tailored game recommendations, promotions, and bonuses right on sight. Gaming enthusiasts worldwide love the personalization of the online casino experience that has been taking over in the past couple of years.

AI algorithms play an important role in detecting fraudulent activities within the online casino environment and preventing them. These algorithms can identify suspicious behaviors, such as irregular betting patterns or unusual account activity, by analyzing player data in real time.

This proactive approach helps maintain a secure and fair gaming environment for all players, safeguarding against potential fraud and ensuring trust in the online casino platform. AI uses sophisticated anomaly detection techniques by comparing individual player activities to establish and flag patterns that indicate fraudulent behavior. By having a monitoring platform, AI allows timely intervention to protect both the players and the casino itself.

As previously discussed, AI algorithms possess the ability to constantly learn from player interactions and adapt the gaming experience accordingly. By leveraging real-time data analysis and employing machine learning techniques, these algorithms can effectively respond to shifting trends and player preferences, resulting in a highly personalized and captivating casino experience.

Game choices, betting patterns, and session durations are only a part of the data used to create player profiles and understand individual preferences. As more data is gathered, the algorithms continuously learn and update these profiles, enabling them to make real-time adjustments to the gaming experience.

The ability to adapt and optimize in real time is one of the most fascinating things about AI. As player interactions and preferences evolve, the algorithms show their dynamic nature and adjust their recommendations, promotions, and gameplay features. This real-time adaptation creates a responsive and personalized experience, enhancing player satisfaction and engagement.

AI algorithms optimize various aspects of the casino experience, including game mechanics, odds calculation, and payout structures. They can identify patterns and preferences, allowing casinos to refine their offerings. This optimization results in more engaging games, with mechanics that resonate with players and rewards that feel fair and welcoming. Furthermore, through the application of reinforcement learning techniques, AI algorithms can dynamically adjust game difficulty levels based on player performance, maintaining a perfect equilibrium between challenge and enjoyment, while the integration of ChatGPT with online casinos, revolutionizes player interactions, offering dynamic and personalized conversations that elevate the overall gaming experience.

There are divided opinions on this topic, some being absolutely fascinated by the rise of AI and some being frightened. However, it cannot be disputed that AI has already brought so many advantages to almost every industry. Proof of that is the online casino industry that has skyrocketed with the use of AI.

The gaming experiences, thanks to machine learning and AI algorithms, have become much more personalized and exciting and offer real-time casino games. As technology advances, the role of AI in adaptation and optimization will continue to evolve, providing even more rewarding, tailored, and enjoyable online casino experiences.

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Machine Learning: How AI Learns and Improves The Online Casino ... - Rebellion Research