Features

Features

Careers in artificial intelligence

• Supervised vs. unsupervised learning: Experts define the gap

  Learn the characteristics of supervised learning, unsupervised learning and semisupervised learning and how they're applied in machine learning projects.  Continue Reading

• Data science vs. machine learning: How are they different?

  Data science and machine learning both play crucial roles in AI, but they have some key differences. Compare the two disciplines' goals, required skills and job responsibilities.  Continue Reading

• Attributes of open vs. closed AI explained

  What's the difference between open vs. closed AI, and why are these approaches sparking heated debate? Here's a look at their respective benefits and limitations.  Continue Reading

• New skills in demand as generative AI reshapes tech roles

  With generative AI adoption on the rise, employers are prioritizing creativity and problem-solving alongside technical skills for roles in software development and data science.  Continue Reading

• The creative thief: AI tools creating generated art

  AI systems such as OpenAI's Dall-E, Midjourney and Stable Diffusion are used to create striking images. But it can be unclear if the images are inspired by others or stolen.  Continue Reading

• The future of data science: Career outlook and industry trends

  The future of data science as a profession is unclear, as new technologies change the responsibilities of data scientists. It may also soon change the nature of the job.  Continue Reading

• Differentiating between good and bad AI bias

  As lawmakers and regulators look at ways to make machine learning models fair, some tech vendors are creating tools that aim to enable enterprises to achieve that purpose.  Continue Reading

• Combating racial bias in AI

  By employing a diverse team to work on AI models, using large, diverse training sets, and keeping a sharp eye out, enterprises can root out bias in their AI models.  Continue Reading

• 4 AI career path trajectories for IT professionals

  As the desire for AI and machine learning in-house skills skyrocket, those looking to break into the market have a variety of career path options, including AI architect and BI developer.  Continue Reading

• How to hire data scientists

  Enterprises tend to want data scientists who have a drive to continue their training, through peer training or online platforms, to keep up with ongoing changes in the field.  Continue Reading

• Data scientists vs. machine learning engineers

  The positions of data scientist and machine learning engineer are in high demand and are important for enterprises that want to make use of their data and use AI.  Continue Reading

• New DataRobot CEO sees bright AI future for the vendor

  New CEO Dan Wright discusses how DataRobot can stay competitive in a crowded AI marketplace, new markets for the vendor, and how DataRobot has tackled the pandemic.  Continue Reading

• Cutting through the fear of how AI will affect jobs through automation

  Dive into Steven Shwartz's recent book, 'Evil Robots, Killer Computers, and Other Myths,' with a chapter excerpt on employment and the future of work.  Continue Reading

• CTO on the need for AI ethics and diversity

  A CTO talks about the importance of diverse data sets when creating AI models and how a lack of diversity can create bias in systems.  Continue Reading

• 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

• 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

• 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

• 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

• 3 in-demand AI skills that boost data scientists' development

  AI encompasses a wide range of disciplines, from advanced math to application development, and building a strong AI team starts with incredibly skilled data scientists.  Continue Reading

• Human-AI collaboration produces top results

  Humans and machines have different -- and often complementary -- strengths and weaknesses. That's why we're not seeing automation leading to mass job losses, at least for now.  Continue Reading

• AI gig economy sets workers and bots on collision course

  The future of work has shifted toward a gig economy, with high-value, short-term workers on demand for organizations. The fast turnover and high volume demand AI to reduce friction.  Continue Reading

• 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

• 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

• 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

• Enterprises deploy AI in mining projects to improve analytics

  By implementing AI in mining processes, enterprises are able to utilize data algorithms and automated machines to preserve the sensitive environment and their human workforce.  Continue Reading

• Debate over AI in the workforce requires a broad view

  Enterprises that are poised to implement AI in the workforce don't have to forfeit human jobs to do so. Read a Dun & Bradstreet analyst's take on the intent of enterprise AI.  Continue Reading

• 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

• Countries need national strategy for AI to stay competitive

  With the Chinese government funding artificial intelligence at an aggressive pace, the U.S. and other countries are facing substantial pressure to step up their investment.  Continue Reading

• Nations sharpen AI strategies as global competition heats up

  The U.S. and China are the leading countries in AI research and development right now, but nations throughout the world are developing strategies to remain competitive.  Continue Reading

• AI and jobs collide as automation looms

  AI automation will eliminate a broad swath of today's jobs over time, but some jobs are likely to disappear sooner than others due to the uneven pace of technology development.  Continue Reading

• Data science tools of the trade fill skills gap

  Data science tools are becoming more intelligent and better at understanding intent and context. But don't stop advertising that data scientist job on the web just yet.  Continue Reading

• AI for business operations starts to offer value

  Businesses are starting to implement AI in operations to smooth out back-office processes and streamline repetitive tasks that currently take too much time for human workers.  Continue Reading