Connected multicloud data platform Teradata announced a new set of analytic integration components for the "everyday AI" platform Dataiku. The new Teradata Plugins for Dataiku are designed to enable analytics and data science teams that use Dataiku to implement a wide range of analytic functions within the Teradata Vantage platform. The upgrades will drive agility for analytics and machine learning initiatives, accelerating time-to-value for joint Teradata-Dataiku customers, Teradata said in a written statement. The integration adds to the existing in-database options available when using Dataiku's solution to design, deploy, and manage AI and applications with Teradata. JC Raveneau, senior director of product management at Dataiku, said, "a common challenge is the scale of data preparation and analytics processes that modern AI and machine learning platforms require. The new Vantage Plugins offers Dataiku users the ability to deliver more value from their analytic workflows with in-database processing."

Teradata announced a new set of analytic integration components for Dataiku, the platform for Everyday AI. The new Teradata Plugins for Dataiku enable analytics and data science teams that use Dataiku to implement a wide range of powerful analytic functions within the Teradata Vantage data and analytics platform. The upgrades drive greater agility for analytics and machine learning initiatives, speeding time-to-value for joint Teradata-Dataiku customers. The enhanced integration adds to the existing in-database options available when using Dataiku's solution to design, deploy and manage AI and applications with Teradata. Many customers struggle with disconnected data and analytics platforms, leading to slower project execution that delays business value.

Covid-19 is the "kryptonite" of AI, breaking its brittle models with outlier data that becomes the new normal, suggests a scientist writing in the Nature Public Health Emergency Collection effort of the National Library of Medicine, NIH. The pre-publication paper is an evaluation of how AI has performed against Covid-19, the main areas where AI has contributed to the right and areas where AI has had little impact. "Its use is hampered by a lack of data, and by too much data. Overcoming these constraints will require a careful balance between data privacy and public health and rigorous human-AI interaction," states the paper, written by Wim Naude, a visiting professor at RWTH Aachen University in Germany. "It is unlikely that these will be addressed in time to be of much help during the present pandemic. In the meantime, extensive gathering of diagnostic data on who is infectious will be essential to save lives, train AI, and limit economic damages," he states.

As covid-19 disrupted the world in March, online retail giant Amazon struggled to respond to the sudden shift caused by the pandemic. Household items like bottled water and toilet paper, which never ran out of stock, suddenly became in short supply. One- and two-day deliveries were delayed for several days. Though Amazon CEO Jeff Bezos would go on to make $24 billion during the pandemic, initially, the company struggled with adjusting its logistics, transportation, supply chain, purchasing, and third-party seller processes to prioritize stocking and delivering higher-priority items. Under normal circumstances, Amazon's complicated logistics are mostly handled by artificial intelligence algorithms.

Hyper Disruption: Today's enterprises are facing hyper disruption – from both external and internal forces – across all industries and at an unprecedented scale. Pervasive Digitization: Most business leaders believe digitization can help address growing customer expectations for rapid and personalized engagement, and are beginning this process by refining business operations. While almost all survey respondents are in the process of digital transformation, some are further along than others. Autonomous Action: Adequately navigating the vast amount of data at our fingertips and leveraging that data to find answers to the toughest business challenges remains the goal for most organizations on the road to pervasive digitization. For many, this requires leveraging artificial intelligence (AI), machine learning (ML) and an autonomous platform.

I've been thinking a lot lately about how technology has become the cure-all elixir, with its increasingly important, and sometimes problematic, role it plays in our lives. This line of thinking was in large part spurred by The Future of Digital Disruption event we co-hosted last month with Oxford University's Saïd Business School. The event was co-moderated by Professor Andrew Stephen from Oxford's Said Business School and Teradata's Martin Willcox, VP Technology (EMEA). Leaders from Audi, Barclays, Kantar, Sony Music, O2 Czech Republic, Facebook, MMA, WPP, Walmart, Teradata and others, as well as leading faculty and researcher's from Oxford's Said Business School Future of Marketing Initiative shared experiences and insights about some of the most complex issues facing leaders today, with a focus on challenges at the intersection of marketing and technology (e.g., analytics, AI, machine learning) and identifying new ways to achieve business growth enabled by technology. The keynote sessions and panel conversations were engaging and varied to encompass the dense topic of how digital disruption will shape the future.

Many argue that the "A" in AI should stand for "augmented" intelligence, not "artificial" intelligence. However, it also can be argued that the "I" in AI isn't necessarily intelligence, either -- at least not without humans providing context and common sense. I recently had the opportunity to chat with Oliver Ratzesberger, president and CEO of Teradata, who points out that "AI is actually very simple math at its core. It is not that you can train an algorithm and and it will warn you that its doing something that may be wrong. Whatever bias was in that training set will come at you full force when that algorithm runs."

Azure Data Box Disk, an SSD-based solution for offline data transfer to Azure, is now generally available in the US, EU, Canada, and Australia, with more country/regions to be added over time. Each disk is an 8 TB SSD that can copy data up to USB 3.1 speeds and support the SATA II and III interfaces. The disks are encrypted using 128-bit AES encryption and can be locked with your custom passkeys. When this feature is enabled, you will be able to copy data to Blob Storage on Data Box using blob service REST APIs. The following Azure IoT Hub Device Provisioning Service features are now generally available: Symmetric key attestation support; Re-provisioning support; Enrollment-level allocation rules; and Custom allocation logic.

At this year's Teradata Analytics Universe event, I shared some of the lessons I have learned over the years from my research and deployment of artificial intelligence (AI) and machine learning (ML) solutions, across multiple organisations, domains and industries. During my talk, "Artificial Intelligence and Machine Learning - Lessons and Opportunities", I explained that data science aims at resolving problems raised by customers, and in order to do that, data scientists use a number of technologies, such as ML, AI and deep learning. The focus is primarily centred on finding the right solutions to arising problems, spanning from the identity fraud to the prevention of equipment failure. To do that, data scientists must be capable of choosing the right tool amongst a myriad of them; because only the right tool will lead to the right solution. Data science s aim is to reach the right solution and not to just develop complex algorithms.

IT media platform CIOReview recently named the 20 Most Promising Big Data Solution Providers – 2018. The listing features providers that are assisting the enterprise with machine learning, artificial intelligence, data governance, cloud computing and real-time analytics. AI in particular is generating much of the interest, and 2017 showed us that big data could present even deeper use cases such as fraud detection and pattern recognition, pushing the market well passed traditional algorithms. At Solutions Review, we track the solution providers that have the biggest impact on the enterprise. As such, we've read through the awards, available here, and selected the solution providers that are most interesting to us.