Features
Features
AI business strategies
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Diverse talent pools and data sets can help solve bias in AI
Bringing historically underrepresented employees into critical parts of the design process while creating an AI model can reduce or eliminate bias in that model. Continue Reading
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The power and limitations of enterprise AI
A panel at CES 2021, held virtually this year, discusses the areas in which modern-day AI and automation shine, and where they still struggle. Continue Reading
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KDD in data mining assists data prep for machine learning
While data scientists are often familiar with data mining, the deeper knowledge discovery in databases (KDD) procedure can help prepare data to train machine learning algorithms. Continue Reading
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Nuance CTO: Conversational AI is the 'next big step'
Conversational AI has steadily grown more advanced over the past several years. Nuance CTO Joe Petro explains why the vendor is refocusing on the technology. Continue Reading
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How AI adoption by industry is being impacted by COVID-19
While COVID-19 has impacted budgets and businesses plans, some industries are seeing improved processes and consumer relationships due to new investments in AI and automation. Continue Reading
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9 data quality issues that can sideline AI projects
The quality of your data affects how well your AI and machine learning models will operate. Getting ahead of these nine data issues will poise organizations for successful AI models. Continue Reading
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Reality check: Analysts check in on the AI hype cycle
AI applications still come with significant hype, but with a targeted approach, organizations can get the most out of their applications. Continue Reading
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Why AI literacy is critical, even for non-technical employees
To successfully deploy and manage AI projects and build a vision of a digital workplace, businesses need to ensure a basic level of AI competency across all employees. Continue Reading
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How to avoid overfitting in machine learning models
Overfitting remains a common model error, but data scientists can combat the problem through automated machine learning, improving AI literacy and creating test data sets. Continue Reading
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8 examples of AI personalization across industries
Through AI content personalization, organizations can build unique profiles of users and customers and tailor their products, advertisements and services to better fit them. Continue Reading
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AI might not have rights, but it could pay taxes
Tax, liability and patent laws can't handle AI systems, which have grown steadily smarter. As AI becomes ubiquitous, the legal system may need to change to accommodate it. Continue Reading
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COVID-19 to drive more AI in retail for small retailers
Retailers were hit hard by the COVID-19 economic fallout, especially small, local businesses. Amid a shift online, AI could help some retailers adjust to the new reality. Continue Reading
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Establishing AI governance in a business
It's hard to ethically manage data for AI models, but AI governance, as well as a strong ethics framework, can help enterprises effectively manage data and models. Continue Reading
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AI vs. predictive analytics debate shows powerful combination
Artificial intelligence and machine learning, when combined with predictive analytics, allow companies and organizations to get the most out of their data. Continue Reading
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Enterprise and home find use for intelligent virtual assistants
Intelligent virtual assistants have the capacity to augment employees, as well as improve convenience in homes, but only time will see their limitations resolved. Continue Reading
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Application of AI in robotics boosts enterprise potential
The combination of AI and robotics has allowed companies to move past automation and tackle more complex and high-level tasks with their robots. Continue Reading
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The deepfake 2020 election threat is real, but containable
Disinformation could harm the 2020 presidential election, and technology simply isn't advanced enough to detect manipulated content, especially deepfakes. Continue Reading
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3 keys for the implementation of AI in the enterprise
Organizations need to focus on diversity, proper scaling and augmentation capabilities when implementing artificial intelligence in order to keep the process pain-free. Continue Reading
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Combining AI and predictive analytics crucial for the enterprise
Predictive analytics, when combined with artificial intelligence, can assist organizations with their risk management, as well as their planning and optimization. Continue Reading
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It's time to address AI ethics
Enterprises need to focus on creating and adopting AI ethical guidelines, especially for emerging technologies such as facial recognition and home assistants. Continue Reading
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Future of autonomous vehicles depends on driver attitudes
Getting the public behind the idea of an autonomous vehicle means peeling back the black box nature of AI and proving the safety of self-driving technology. Continue Reading
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AI in retail has helped retailers during COVID-19
During the Ai4 2020 virtual conference, a panel of retail experts discusses how AI and analytics have helped retailers deal with the economic fallout of the COVID-19 pandemic. Continue Reading
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AI, robotics help businesses pivot supply chain during COVID-19
Big enterprises such as Wayfair, UPS, Unilever and Siemens move to automate more of their supply chains with AI as the coronavirus pandemic disrupts business operations. Continue Reading
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Machine learning limitations marked by data demands
Machine learning has impressive capabilities in the enterprise, but with high-data requirements and struggles with explainability, it remains unable to reach widespread use. Continue Reading
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4 ways AI and digital transformation enable deeper automation
Organizations that are going beyond the enterprise adoption of digitization are entering a new wave of AI-enabled digital transformation. Continue Reading
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Reimagining creativity and AI to boost enterprise adoption
AI has yet to reach the point of creativity but continues to advance, while assisting humans in the production of their own creative works and improvement of their organizations. Continue Reading
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The state of AI in 2020 likely sees more adoption
AI adoption has appeared to grow this year, as organizations deploy automation to augment dwindling workforces and help deal with growing demand during the COVID-19 pandemic. Continue Reading
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AI in tax preparation gets a boost from classification tech
Tax filers and tax collectors are using AI tools to make the process of paying and collecting taxes simpler. The data-rich, complex processes of tax collection are an ideal use case for AI. Continue Reading
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Accelerating digital transformation through remote work
As working remotely became a near-global mandate, companies have been thrust headfirst into a digital transformation. AI is helping to smooth the journey. Continue Reading
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Deep learning's role in the evolution of machine learning
Machine learning has continued to evolve since its beginnings some seven decades ago. Learn how deep learning has catalyzed a new phase in the evolution of machine learning. Continue Reading
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Machine learning for fraud prevention keeps TrafficGuard agile
TrafficGuard uses machine learning to prevent ad fraud but has faced the challenges that come along with it. Full-scale commitment and investment have eased those obstacles. Continue Reading
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Contact tracing apps seem effective, but have privacy concerns
Contact tracing mobile applications appear to offer an easier, safer way to track where an infected person has been, but technology could cross a data privacy boundary. Continue Reading
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Enterprises should not neglect AI digital transformation
Enterprises should focus on automation to augment their workforces as they recover from the COVID-19 economic downturn, and not lose sight of larger digital transformation projects. Continue Reading
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AI's impact on business: The quest to make money
'Bionic' companies have cracked the code on using AI to make money. Here is what IT and business leaders need to do to maximize AI's impact on the business. Continue Reading
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AI and DevOps team up in remote work model
Communication and network security are increasingly difficult in DevOps teams as the workforce goes digital. Using AI and machine learning helps companies adjust to the remote work model. Continue Reading
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AI in digital transformation vital for enterprises
Without using AI, enterprises will not be able to compete effectively, according to the CEO of Box. Companies need to infuse AI into their digital transformation efforts now. Continue Reading
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Value of AI in capitalizing on big data, expanding automation
The strategic value of AI in business is growing as companies learn how to use AI technologies to capitalize on big data, drive automation and better serve customers. Continue Reading
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Safety vs. privacy in the age of coronavirus raises tech questions
To slow the spread of the coronavirus, governments and organizations are using contact tracing and thermal imaging for fever detection, but these methods carry privacy concerns. Continue Reading
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Here's how one lawyer advises removing bias from AI
Avoiding bias in AI applications is one of the central challenges in using the technology. Here's some advice on deploying AI technologies in a way that is fair. Continue Reading
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AI for supply chain can help companies handle disruptions
Even faced with the unpredictability of the coronavirus pandemic, enterprises can generate some stability using AI and machine learning in their supply chains. Continue Reading
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AI and RPA are here to stay
The coronavirus pandemic has caused enterprises to rely more on AI technologies as they are forced to lay off employees or temporarily close their offices. Continue Reading
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How to identify projects that create AI business value
When AI projects lead with technology, they rarely have a business impact. Instead, business leaders should target projects to make a meaningful improvement in processes. Continue Reading
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Science fiction vs. reality: A robotics industry overview
Robots have made their way into industrial, manufacturing and military settings, but the robots of science fiction remain a long-term goal rather than a reality. Continue Reading
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AI and chatbots: Conversational app platforms are maturing
Chatbots have long been reliant on pre-written responses to common questions. With the incorporation of AI, however, chatbots can take an important step forward. Continue Reading
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Data Science Central co-founder talks AI, data science trends
In a Q&A, Vincent Granville, executive data scientist and co-founder of Data Science Central, discusses how AI has changed the data science field and the ways in which it will continue to do so. Continue Reading
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Advanced AI in financial services boosts fraud detection, efficiency
Financial firms plan to invest more into R&D on AI and plan to deploy advanced AI, like deep learning, within the next two years, according to a new survey. Continue Reading
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How stock market prediction using AI impacts the trading floor
Robo-advisors and stock monitoring bots are changing the way traders and investors take on the stock market. Continue Reading
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The future state of machine learning needs improved frameworks
Utilizing machine learning in the collection and processing of data would most likely lead to more widespread adoption of AI based on the technology. Continue Reading
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Employee AI readiness is fairly low
Enterprise employees are largely lacking in AI skills, and enterprises need to work to reskill or upskill employees to improve their skills and help reduce AI job loss fears. Continue Reading
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How and why are our devices listening to us?
Consumers are utilizing digital voice assistants and smartphones but may not realize how frequently companies listen in and sift through all the data these devices create. Continue Reading
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5 mistakes that disrupt data science best practices
Through asking questions and understanding the real-world issues other parts of the company face, data scientists polish their enterprise contribution. Continue Reading
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Companies focused on building sustainable AI in 2020
Fewer companies are deploying AI enterprise-wide in 2020 because of universal underestimation of investments and transformation required to have a rounded AI strategy. Continue Reading
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The complex nature of regulating AI
Regulating AI is a difficult task because the technology changes rapidly. Governments must be able to employ preventative regulation to prevent any misuse. Continue Reading
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Engage employees for successful enterprise AI strategy creation
Existing employees are in the best position to suggest process improvements through automation, and executives need to utilize the employee experience to drive AI strategy. Continue Reading
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Machine learning ops to lead AI in 2020
The increased usage of pre-trained models, machine learning ops coming to the fore and increased transparency are all poised to lead the new year in AI. Continue Reading
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Supervise data and open the black box to avoid AI failures
As AI blooms, marketers and vendors are quick to highlight easy positive use cases. But implementation can -- and has -- gone wrong in cases that serve as warnings for developers. Continue Reading
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Tailored content heads machine learning in digital marketing
Consumers are no longer engaged with content alone -- companies need to create a robust digital marketing strategy personalized to each consumer. Machine learning is here to help. Continue Reading
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How Getty Images reduces bias in AI algorithms to avoid harm
In applications from internal job recruiting to law enforcement technology, AI bias is a widespread issue. Here's what enterprises can do to reduce bias in training and deployment. Continue Reading
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Fintech and retail lead the fray in AI adoption by industry
Though AI enhances and drives the financial and manufacturing industries, others remain wary of the investment capital and research needed to insert AI into their enterprise. Continue Reading
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Government AI strategy includes tech transfer to private sector
The Department of Energy's chief commercialization officer works with the DoE's national laboratories on transferring AI and other technologies to the private sector. Continue Reading
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Ethical concerns of AI call growing adoption into question
AI tools are getting easier to use every day, putting powerful tools into the hands of potentially malicious users. The time to think about the ethics of AI advances is now. Continue Reading
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John Deere's software and AI journey
John Deere began a software journey more than a decade ago, hiring technology teams and developing new technologies, including AI, to help drive innovation in its machinery. Continue Reading
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Clashes between AI and data privacy affect model training
Enterprises' lax data rules reveal weaknesses around AI and model training -- particularly machine learning's reliance on unrestrained big data collection. Continue Reading
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U.S. spends more on AI as AI in China continues to grow
Research and development of AI in China continue to grow. Meanwhile, the U.S. plans to up its spending on non-defense AI projects by close to $1 billion in 2020. Continue Reading
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Use of AI in business requires education, understanding
AI adoption requires business leaders to have a clear understanding of the technology and its capabilities, as well as how AI can help automate and aid specific functions. Continue Reading
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Using enterprise intelligent automation for cognitive tasks
RPA is no longer comprised of simple chatbots or repetitive programmed tasks. Enterprises are looking at RPA to move up the ladder of cognitive automation. Continue Reading
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Enterprises work toward AI trust and transparency
If ignored, a lack of trust in AI algorithms could diminish user adoption. To remedy this risk, enterprises are working to make their applications more transparent and explainable. Continue Reading
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The future of data science and AI points to automatic tools
The relationship between data scientists and companies using AI is evolving rapidly, shifting from a focus on trained professionals to experienced employees with automated tools. Continue Reading
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Enterprises need to create an AI culture for success
Enterprises can resist using AI because of the cultural changes employees feel it will bring, including changes to employee job descriptions and elimination of outdated jobs. Continue Reading
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Analyst, author talks enterprise AI expectations
The author of the upcoming book about enterprise AI talks about realistic AI deployment, dispelling some of the AI hype myths that can be harmful to enterprises. Continue Reading
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AI for digital marketing heavily used in the gaming industry
AI in gaming has evolved beyond automating character and fictional world development. Gaming companies are now using AI to better market to existing and potential players. Continue Reading
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Enterprise consumer relationships are building trust in AI
Transparency is an increasingly important component of consumer trust. If you want to win over consumers whose data is being collected, start with explainability and collaboration. Continue Reading
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AI and big data go perfectly together -- sometimes
The combination of big data and AI tools enables new forms of analytics and automation, but use of the technologies in enterprise applications is still evolving. Continue Reading
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AI acquisitions lead to consolidation
Analytics and AI startups emerge regularly and grow quickly. Frequent acquisitions, however, seem to be creating a more consolidated industry, cementing some vendors at the top. Continue Reading
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AI in professional services revolutionizes white-collar jobs
Professional services and consulting firms are adopting AI at a rapid rate, even though these types of jobs, which mainly focus on interpersonal interaction, may not seem like strong targets for automation. Continue Reading
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How data privacy and marketing coexist when influencing the public
The author of a new book on the intersection of advertising and marketing with AI and data privacy talks about influencing the public with technology. Continue Reading
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How to recycle data from AI for employee engagement efforts
By reusing the data collected for AI algorithms and the insights they generate, enterprises can boost employee performance and improve business processes. Continue Reading
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Human rights advocate talks GDPR, AI and data privacy laws
Human rights advocate Bjørn Stormorken talks about the importance of data privacy laws, and why stronger laws and more data literacy are necessary today. Continue Reading
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Top computer vision retail uses and how they enhance shopping
Computer vision technology can help retailers make shopping in stores faster and easier for customers, while also improving checkout accuracy and theft prevention. Continue Reading
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AI for knowledge management boosts information accessibility
Integrating AI for knowledge management systems builds intelligent searches. Enterprises benefit from streamlined, smart content platforms that make information accessible. Continue Reading
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AI vendors attack data scientist shortage with trainings
Internal data science training programs have helped vendors when colleges and universities have failed. Training is helping to fix the data scientist shortage. Continue Reading
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Clean data for machine learning is key to successful AI
Many enterprises think their AI projects should start with massive amounts of data. But clean data for machine learning should be their first step toward AI. Continue Reading
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AI bias, for good or ill
Bias in AI algorithms can produce harmful results, but it can also help train models. This third part of a three-part series on AI ethics explains the pros and cons of AI bias. Continue Reading
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Wield AI to personalize consumer products and aid logistics
Companies using AI to sell consumer products seek brand loyalty by using personalization, online sales capabilities and optimized inventory management Continue Reading
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How to address the hidden risks of algorithmic decision making
From biased customer interactions to harmful treatment recommendations, some risks of automated decisions have yet to be resolved. A Wharton School professor has a way out. Continue Reading
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3 ways to create an AI ethics framework for responsible tech
AI can often reflect the biases and limits of its human developers. Experts say diversity, review boards and a strong AI ethics framework will lead the way toward ethical AI. Continue Reading
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Data scientists urged to take AI security threats more seriously
AI security hasn't been the top concern of most data scientists using machine learning. But as these systems move closer to the core of the business, security is becoming critical. Continue Reading
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Address anonymity and data privacy in chatbot security
A key to successful enterprise chatbot security is to program your chatbot to recognize personal or sensitive information and treat it accordingly. Continue Reading
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Enterprises need to develop an AI strategy now
Business leaders who approach AI as a future concern are likely to miss the boat on this influential technology. The time to explore AI implementation is now. Continue Reading
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AI in accounting boosts compliance and fraud detection
Accounting and finance teams are using AI tools to speed document review and other error-prone processes, which gives a boost to fraud detection and compliance efforts. Continue Reading
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Enterprise AI tools help cultivate better business health
AI technologies can give companies a business boost, enough so that the number of available jobs for humans may increase instead of declining due to more automation. Continue Reading
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AI benefits people, some more than others
The risks and benefits of AI are not always clear to the average consumer, who might experience the risks more than the benefits, as opposed to the tech giants that create AI. Continue Reading
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Trump's AI executive order glosses over data privacy, funding
Trump released his AI executive order two weeks ago, and researchers are continuing to analyze its potential impact on the industry. One thing is certain: AI is about to change. Continue Reading
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AI regulation stirs as unrestricted AI booms in China
Governments need to start regulating AI as the technology advances, experts say in part one of a three-part series on AI ethics issues around regulation, control and bias. Continue Reading
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More AI in retail stores means more personalization
Cheaper and more easily accessible AI developer tools are part of the reason why traditional retailers are better at marketing personalized experiences to customers. Continue Reading
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Recruiting data scientists for AI and machine learning
When hiring data scientists, be sure to include your data science team in the interview process, and strive to build a data-literate HR department. AI tools may also help. Continue Reading
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New deep learning techniques take center stage
The days of simple linear regressions for machine learning are giving way to more powerful deep learning techniques that point the way toward general AI capabilities. Continue Reading
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Footasylum drives growth with AI in retail business tools
AI-driven marketing tools from Peak, an AI and analytics services vendor, has helped apparel seller Footasylum better personalize marketing to customers. Continue Reading
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AI in 2019 will be all about bots and pre-trained models
2019 promises to be a big year for AI, as we're likely to see some trends -- such as adoption of virtual assistants and strong venture capital funding -- continue and others emerge. Continue Reading
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AI winter is coming? Not this time, Tom Davenport says
A new AI winter, or downturn, is unlikely, even if the current slew of inflated expectations surrounding AI predicts inevitable disappointment, analytics expert Tom Davenport says. Continue Reading