DataRobot Core incorporates tools for data scientists that the company gained from its acquisition of Zepl in May 2021.
The product offers specialized capabilities that enable users to toggle between the development of customized machine learning models and the use of AutoML for experimentation.
DataRobot’s mission is to help enterprises implement ‘Augmented AI’ by offering products that enable users to combine human intelligence with machine automation. The DataRobot AI Cloud platform facilitates customers’ ability to toggle between automation and human intervention, so that they can adjust their AI strategy as needed throughout the duration of a project’s lifecycle. To support this strategy, the company launched DataRobot Core in December 2021. The platform enables data scientists to use the tools and languages they prefer, to speed up experimentation and accelerate AI deployments. Continue reading “DataRobot Launches Solutions Aimed at Combining Human Intelligence with Machine Automation “→
Despite public apprehension related to the privacy and accuracy of facial recognition, companies offering the technology continue to attract the attention of investors.
Suppliers of facial recognition technology are taking steps to address the ethical concerns raised by civil liberties organizations.
Despite the negative publicity around facial recognition, companies offering the technology continue to attract the attention of investors. This summer, U.S.-based Clearview AI raised $30 million from private investors; Israel-based AnyVision raised $235 million from SoftBank, Eldridge, and others. Both companies have been embroiled in controversy around the use of their technology by law enforcement, but neither seems to be hindered by it. Instead, both are using the opportunity to move the ethics conversation forward. Continue reading “Investment in Facial Recognition Forges Ahead Despite Ethical Concerns”→
Only slightly more than half of the respondents to GlobalData’s recent survey on emerging technologies felt that they fully understood artificial intelligence.
Before decision-makers act on the insights revealed by artificial intelligence, they need to have confidence in its findings, which requires an understanding of how they are obtained.
There is a widely accepted belief that artificial intelligence (AI) holds the potential to significantly alter the way organizations operate, vastly improving business outcomes. Businesses conceptually grasp that the technology’s benefits range from increasing efficiency and productivity to enhancing the customer experience. Yet, AI is still often viewed as a mysterious black box that yields little insight into how its findings are obtained. Without understanding what is happening under the covers, line-of-business leaders can be reluctant to act on the findings. Continue reading “Enterprises Require Tools That Explain AI Findings”→
In mid-May, AWS highlighted its portfolio of AI tools and solutions during its AWS Summit Online for the Americas region and announced the general availability of Amazon Kendra for enterprises.
Tools that support AI model development and management and pre-built solutions that can be easily deployed by developers who aren’t AI experts help streamline AI adoption.
AWS understands the challenges enterprises face when building their own machine learning models. The company notes that when scaling AI adoption, enterprises face wide-ranging complexities that can start as early as the data collection stage and continue throughout the model management lifecycle. At the beginning of a project, organizations face challenges related to data identification, storage, and curation as they pull together disparate data sources. Later, while building and training models, they need to manage numerous other complexities, such as sharing notebooks and pre-trained models. They need to ensure effective collaboration among what can be a growing number of individuals or teams, each with their own specializations. And, since machine learning models aren’t usually perfect the first time, team members need to communicate during the process of model tuning and optimization. They need to manage multiple versions of models, run experimental models in real time, and compare results. Even after deployment, machine learning algorithms need to be managed and monitored for concerns such as data drift, with newer versions deployed as additional data is collected or the factors that impact model results change. Managing these tasks can be challenging, and as AWS rightly points out, tools that help manage the complexities do much to streamline and speed AI deployments. Continue reading “AWS Aims to Make AI More Accessible for Both AI Specialists and Non-AI Experts”→
Many data analytics companies are beginning to embed artificial intelligence (AI) capabilities directly into their software, allowing users to reap the benefits of AI-driven insights without developing the machine learning algorithms themselves.
Domo is taking a different approach from some of its peers by working closely with AWS to add automatic machine learning, recommended actions, and drag-and-drop predictive model deployment in the Domo Business Cloud.
Many businesses are eager to reap the productivity and efficiency-enhancing benefits of AI. They have collected and stored vast amounts of data but face challenges when it comes to uncovering the nuggets of insight that can improve operations, enhance customer service, and speed faster and more informed decision-making. One of the biggest hurdles to AI adoption is a lack of resources. Building, training, tuning, and deploying machine learning models is a lengthy process that requires the expertise of expensive data scientists and AI experts. Many businesses don’t have these resources readily available; nor do they have the time or money to invest in acquiring them. Continue reading “Domo Partners with AI Leader to Help Customers Gain Greater Insights from Their Data”→
Organizations in banking, financial services, and insurance are more likely to prioritize current and future investment in AI than overall survey respondents.
AI-driven solutions can help the sector verify customer identification and assist with fraud detection as well as anti-money laundering and know-your-customer initiatives.
Banking, financial services, and insurance organizations are eager to leverage AI solutions such as chat bots, deep learning, and machine learning. According to GlobalData’s most recent IT Customer Insight survey, organizations in this sector are more likely to be prioritizing investment in AI technology than their counterparts across other vertical industries. As shown below, 63% of respondents in banking, financial services, and insurance currently prioritize investments in AI, compared to only 54% of companies across all vertical markets. Similarly, 64% of financial services, insurance, and banking organizations have prioritized AI for investment in the next two years, versus only 55% of overall survey respondents. Continue reading “Survey Results Indicate Strong Investment in AI by Banking, Financial Services, and Insurance Sectors”→
A draft research paper leaked the news that Google had achieved quantum supremacy.
The accomplishment reinforces Google’s position as a thought leader in the realm of high-performance computing.
Last week, a draft research paper appeared and then was immediately removed, apparently leaking the news that Google had achieved quantum supremacy, meaning it had performed calculations that today’s high-speed computers could not accomplish in a reasonable amount of time. Purportedly, Google’s Sycamore quantum processor, utilizing 53-qubits, performed calculations in 200 seconds that would have taken traditional supercomputers over 10,000 years to complete. The power and future potential of such an achievement are awe-inspiring, even if there are no practical applications today. Continue reading “Google Solidifies Position as a Trailblazer in High-Performance Computing with Purported Achievement of Quantum Supremacy”→
San Francisco’s ban on the use of facial recognition technology by municipal agencies is noteworthy given the city’s high-tech affiliation and AI’s potential applications in public safety.
The safety-enhancing benefits of facial recognition are not resonating; instead, the technology has become a lightning rod for societal concerns related to privacy and inequality.
San Francisco is set to become the first major U.S. city to ban the use the facial recognition technology by municipal agencies. On Tuesday, the San Francisco Board of Supervisors voted in favor of the ‘Stop Secret Surveillance Ordinance,’ outlawing the use of the AI-based technology by city departments. The move is particularly noteworthy because it originates in a part of the U.S. otherwise known for embracing high tech and because it restricts the use of artificial intelligence for public safety, widely considered a top use case for facial recognition technology. However, San Francisco isn’t the only city evaluating restrictions on facial recognition; the issue is top of mind among lawmakers in many regions. Continue reading “Facial Recognition: A Lightning Rod for Societal Concerns in San Francisco”→
IT services players must change their ways to be more effective partners to their clients.
At the NASSCOM Technology & Leadership Forum (NTLF) 2019, held in Mumbai, India in late February, Indian and international industry leaders shared best practices for managing AI’s impact on staff, reskilling teams, developing deeper customer relationships, and cultivating a culture that embraces change.
It’s not only about finding the right technology, curating large amounts of data, or identifying the best use cases. Successful AI depends on changing business processes. We often focus on the change that needs to take place at the enterprise, but what about the changes that IT services providers need to make in order to better serve their customers? IT services players must also change their ways to be more effective partners to their clients. Continue reading “At NASSCOM 2019, Executives Shared Best Practices for Addressing New Opportunities”→
A shortage of skilled AI professionals is one of the biggest hurdles to broader AI adoption by enterprises.
Salesforce is embedding its solutions with Einstein-powered AI to make the technology more accessible to non-data scientists.
Businesses need artificial intelligence (AI) solutions that can be easily adopted and manipulated by line-of-business users. Although many organizations are eager to adopt AI, they are often held back by a lack of access to data scientists that can curate data and develop AI models that address their specific needs. This skills shortage is one of the top hurdles facing organizations when it comes to operationalizing AI. And it is precisely this challenge that Salesforce is looking to address by embedding its solutions with Einstein-powered AI. Continue reading “Operationalizing AI for Broader Adoption”→