AI News, Overview of Artificial Intelligence (Part 2) artificial intelligence

AI predictions for 2020 (Part 2)

There’s something about the start of a new decade that feels important and demands some thoughts about the future.

“When these capabilities are embedded as part of a cloud-delivered HR application, leaders can focus on outcomes they want to achieve instead of the technical development of the capabilities,”

As AI continues to remove many administrative tasks that managers traditionally handled, that means managers will need to find other ways to help their teams.

“AI can help recruiters reduce their unconscious biases during the hiring process, and I believe that in 2020 we will have HR teams much more engaged and believingin diversity with this helpfultechnology,”

The period close and reporting process has long been the bane of finance teams, entailing long hours and eating up days of work every month.

"I predict we will make significant progress in 2020 towardour goal of realizing a continuous close, powered by AI and machine learning.It will lead to simplification, efficiency, and questioning why we do things, and show just how powerful Oracle ERP and EPM Cloud are,”

Al Marciante, senior director EPM Cloud at Oracle, recently presented at Oracle Open World how Oracle EPM Cloud delivers features for optimizing and automating the comprehensive financial close process, covering the account reconciliation/transaction matching, financial consolidation, close calendar, tax provision/reporting, and narrative reporting business processes.

utilizing signal detection, root cause analysis, insight discovery, action recommendation, collaboration and intelligent narrative for true connected planning.

As supplies, materials, equipment, and other costs shift, companies are often challenged to determine their true costs and the optimal pricing for their products and services.

Chatbots and AI enable supply chain professionals to better monitor operations and receive proactive updates in a simple conversational user interface to reduce manual effort and improve accuracy.

Other benefits may include smarter operators, predictive self-maintenance, smart supply chain, more sustainable and optimized procurement, and improved customer experience.

To make these predictions a reality, it’s critical to get C-suite sponsorship to support financial investment and also encourage employees to embrace and get trained on new and innovative technologies.

If you are in a finance or operations leadership position, it is incumbent upon you to get outside your comfort zone and explore how AI and other advanced technologies can improve your organization’s performance.

Removing Bias in AI — Part 2: Tackling Gender and Racial Bias

Chatbots that become racist in less than a day, facial technology that fails to recognize users with darker skin colors, ad-serving algorithms that discriminate by gender and race, an AI hate speech detector that’s racially biased itself.

In part 1 of this article, we covered first steps to removing bias in AI, including recognizing our own biases, building diverse teams, implementing harm reduction in the design and development process, and using tools to measure and mitigate risks.

In part 2 we’ll explore gender and racial bias in particular, which AI often replicates, gain insights and practical tips to start reducing bias in AI experiences based on hands-on research, and explore a real-world project that has been created to end gender bias in AI assistants.

“They were then asked questions about Alexa’s race, political beliefs, and hobbies.” The view that emerged was one of Alexa as a subservient white woman who couldn’t think for herself, apologized for everything, and was pushing a libertarian agenda.

Alexa’s ubiquity means that it has become a socializing force, influencing a child’s mental model on how they perceive female-sounding voices, and establishing a ‘norm’ for how technology is supposed to sound — in this case, female and inferior.”

“If you ask folks at Microsoft, Amazon, or Google, why so many of our voice assistants are female,” explains David Dylan Thomas, “they’ll tell you that according to their research, people are more comfortable hearing certain kinds of assistance or information from women than from men.

But as designers make that choice, they run the risk of reinforcing gender stereotypes, that female AI assistants should be helpful and caring, while machines like security robots should have a male voice to telegraph authority.

Informed by her conversations with over 30 machine learning engineers, creative technologists, and diversity and inclusion thought leaders, Evie Cheung has found that one of the most salient and urgent AI issues is biased algorithms — particularly around the topic of race.

These factors result in products like Facebook’s racist soap dispenser and Google’s image recognition algorithm that classified black folks as gorillas.” Cheung says that we need to acknowledge the glaring truth: history is racist because humans are racist.

If we don’t, we risk scaling the bias that already exists.” Content strategist and co-founder of Rasa Advising, Julie Polk, currently a content lead for AI applications at IBM, has come up with four essential tips you should keep in mind to combat bias in AI: Removing bias in AI and preventing it from widening the gender and race gap is a monumental challenge but it’s not impossible.

Artificial Intelligence Brings Unprecedented Value to Legal Operations, Part 2

This is the conclusion of a two-part guest post on Artificial Intelligence in the legal profession by Sandeep Sacheti, who leads a Wolters Kluwer team that specializes in applying the technology in a context that serves large companies and law firms.

AI in Legal As you can imagine, the business of law generates a huge amount of data – often in the form of transcripts and communications between people – and requires frequent execution of repetitive tasks in addition to the highly skilled legal work.

The combination of machine learning and natural language processing is perfectly positioned to help analyze large volumes of legal documents, and find associations, patterns and trends.

AI is an important part of making this possible precisely because its value lies in automating the mundane repetitive tasks while enabling human experts to improve results beyond what machines or people could do alone.

Leveraging this data in combination with machine learning and natural language processing allows us to build truly unique solutions that dramatically improve the efficacy of the legal bill review processes.

 We use AI to get more out of these unique data assets, join them with our experts, then integrate the resulting tools directly into our market-leading platforms, which leads to a truly impressive increase in value for clients.

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