Let s immerse on a journey that shapes the future of coffee, transforming one person, one cup and one neighborhood at a time. Primary Responsibilities: • Own the technical relationship and discovery with cross-functional leaders to create product-centric solutions to advance the sustainability cause.

A game designer has sparked controversy after submitting an image created by an AI text-to-image generator to a state art competition and taking home first prize. Jason Allen entered the artwork titled "Theatre d'Opera Spatial" in the "Digital Arts / Digitally-Manipulated Photography" category of the Colorado State Fair fine arts competition but created the piece using a popular text-to-image AI generator named Midjourney. A Twitter post describing Allen's win went viral earlier this week (and was first covered by Vice). The post elicited a strong response, with many users claiming that Allen had been deceptive in submitting the piece, particularly as most of the public is unaware of how text-to-image AI generators work. Allen, though, has defended his actions.

Last month, Facebook parent Meta unveiled an artificial intelligence chatbot said to be its most advanced yet. BlenderBot 3, as the AI is known, is able to search the internet to talk to people about almost anything, and it has abilities related to personality, empathy, knowledge and long-term memory. BlenderBot 3 is also good at peddling anti-Semitic conspiracy theories, claiming that former US President Donald Trump won the 2020 election, and calling Meta Chairman and Facebook co-founder Mark Zuckerberg "creepy". It's not the first time an AI has gone rogue. In 2016, Microsoft's Tay AI took less than 24 hours to morph into a rightwing bigot on Twitter, posting racist and misogynistic tweets and praising Adolf Hitler.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! As technology progresses, business leaders understand the need to adopt enterprise solutions leveraging Artificial Intelligence (AI). However, there's understandable hesitancy due to implications around the ethics of this technology -- is AI inherently biased, racist, or sexist? And what impact could this have on my business?

A growing number of oversight boards and regulatory bodies seek to monitor and govern algorithms that make decisions about people's lives. Prior work has explored how people believe algorithmic decisions should be made, but there is little understanding of how individual factors like sociodemographics or direct experience with a decision-making scenario may affect their ethical views. We take a step toward filling this gap by exploring how people's perceptions of one aspect of procedural algorithmic fairness (the fairness of using particular features in an algorithmic decision) relate to their (i) demographics (age, education, gender, race, political views) and (ii) personal experiences with the algorithmic decision-making scenario. We find that political views and personal experience with the algorithmic decision context significantly influence perceptions about the fairness of using different features for bail decision-making. Drawing on our results, we discuss the implications for stakeholder engagement and algorithmic oversight including the need to consider multiple dimensions of diversity in composing oversight and regulatory bodies.

Text-to-image AI generation tools have entered their wild wild west phase. The sweeping trend which Open AI's DALL.E 2 started with great caution has drastically turned into a world where anything goes. Last week, London and Los Altos-based startup Stability.ai Comparable in quality to DALL.E 2 and Midjourney, the implications of the step taken by Stability.ai Moreover, Stable Diffusion, unlike its predecessors, has next to no restrictions barring users from generating images with inappropriate content or prominent personalities.

We are seeking an Engineering Manager to join Meta Reality Labs, an organization focused on productizing novel technologies in AR and VR devices. In this role, your job will be to both manage and partner with groups working across the full spectrum from research to product development, managing our technical investment portfolio supporting multiple products across different timelines. You will oversee and be responsible for a broad array of state-of-the-art technology areas, spanning Eye Tracking, Computer Vision and Machine Learning, and designed to run on low-power client devices. Prospective candidates should have sufficient technical depth and breadth in the associated set of technologies to make portfolio management, resourcing and roadmap decisions. Minimum Qualifications: • BS degree in Engineering, Physics, Computer Science or equivalent • 2 years of people management experience in multi-disciplinary global teams at the intersection of tech and product, including building performing teams and organization • 2 years of experience supporting an engineering and/or research organization through technical leadership • Demonstrated experience in recruiting and managing technical teams, including performance management • Experience in managing teams productizing Computer Vision or AI/Machine Learning technologies from conception to end • Leadership and interpersonal communication experience in working across many disciplines, driving best engineering practices, and mentoring team members • Technical experience in leading teams/projects in one or more of the technical domains of machine perception (e.g., machine vision, deep learning, sensors and robotics) • Flexibility and resilience in a dynamic environment Preferred Qualifications: • PhD in Computer Vision, Computer Graphics, AI/Machine Learning or related field • Experience managing joint hardware-software development and associated rapid prototyping projects • Experience in leading teams developing technologies such as eye tracking, face tracking or body tracking • Experience in leading teams interfacing with HW teams (e.g., sensors, silicon) in setting requirements and product tradeoffs Facebook is proud to be an Equal Opportunity and Affirmative Action employer.

As required by the?Colorado Equal Pay Transparency Act, Accenture provides a reasonable range of compensation for roles that may be hired in Colorado. Actual compensation is influenced by a wide array of factors including but not limited to skill set, level of experience, and specific office location. For the state of Colorado only, the range of starting pay for this role is {{$61,600 – $97,199}} and information on benefits offered is here.

Almost every company understands the value that artificial intelligence (AI) or machine learning (ML) can bring to their business, but for many, the potential risks of adding AI do not outweigh the benefits. Report after report consistently ranks AI as critically important to C-suite executives. To remain competitive means streamlining processes, increasing efficiency and improving outcomes, all of which can be achieved through AI and ML decisioning. Despite the value that AI and ML bring, a lack of trust or fear that the technology will open businesses to more risk has slowed the implementation of AI/ML decisioning. This isn't wholly unfounded--the risk of biased decisions in highly regulated industries and applications, like insurance eligibility, mortgage lending or talent acquisition, has been the subject of several new laws focused on the "right to explainability."

Generative patent language models can assist humans to write patent text more effectively. The question is how to measure effectiveness from a human-centric perspective and how to improve effectiveness. In this manuscript, a simplified design of the autocomplete function is proposed to increase effectiveness by more than 10%. With the new design, the effectiveness of autocomplete can reach more than 60%, which means that more than 60% of keystrokes can be saved by autocomplete. Since writing patent text does not necessarily start from the beginning to the end, a question is whether the generative model can assist a user no matter where to start writing. To answer the question, the generative models in this manuscript are pre-trained with training data in both directions. The generative models become bidirectional. Since text generation is bidirectional, the calculation of autocomplete effectiveness can be bidirectional and starts from anywhere in the text. After thorough experiments, a key finding is that the autocomplete effectiveness of a model for the same text remains similar no matter where the calculation starts. The finding indicates that such bidirectional models can assist a user at a similar level, no matter where the user starts to write.