Strategies for orchestrating the interactions between multiple agents, both human and artificial, can wildly overestimate performance and underestimate the cost of orchestration. We design a framework to orchestrate agents under realistic conditions, such as inference costs or availability constraints. We show theoretically that orchestration is only effective if there are performance or cost differentials between agents. We then empirically demonstrate how orchestration between multiple agents can be helpful for selecting agents in a simulated environment, picking a learning strategy in the infamous Rogers' Paradox from social science, and outsourcing tasks to other agents during a question-answer task in a user study.

My friend walks into the village hall, scene of my son's third birthday party, a mixture of panic and incredulity creeping across his face. "I didn't realise we were dressing up," he says, taking in my outfit. I'm wearing a mint-green tulle midi dress with sheer sleeves that balloon precociously and a tiered skirt that puffs out in such a way as to give me the appearance of either a Quality Street or a three-year-old at her own birthday party. It's not, if I'm entirely honest, the most practical of outfits for serving chocolate cake to 18 sticky-handed toddlers but, as I blurt out to my friend, keen to dispel any confusion, the avant-garde look wasn't actually my choice: it was AI's. My wardrobe is my identity, my refuge, my hobby, my happy place. Or, at least, it was.

There's nothing more romantic than having an AI-powered bot write your vows for you. Earlier this month, wedding planning platform Joy launched a new OpenAI-powered "Wedding Writer's Block" tool that uses AI technology to generate a draft for one of the most important speeches of your life. The AI assistant is designed to help write vows and wedding toast speeches, among other "wedding-related wordage," the company claims, like a love story for your wedding website or thank-you notes, or if you're stuck on how to politely decline a wedding invite. There are also several different tones that the draft can be written in. For instance, if you want to sound like William Shakespeare or maybe a pirate for some reason.

Artificial intelligence (AI) made a grand entrance into cyberspace with a promise to enhance how people interact with data. More so, it raises hopes of providing a stronger cybersecurity framework. Several years down the line, people are yet to get the most out of AI technology in cybersecurity. In some instances, it complicates data management and raises some security concerns. Artificial intelligence hardly goes unmentioned in discussions about cybersecurity.

Be it for on-road GPS navigation or voice-assisted speakers, speech-activated devices are gaining more prominence in today's digital world. Globally, the market for speech and voice recognition is estimated to be valued at $1.38 billion (in 2021) and is projected to grow to $3.89 billion by 2026. So, how do machines recognize spoken language or sounds? A subset of data labeling or annotation, audio annotation can be performed on different types of voices and is an integral part of natural language processing (NLP). With more companies deploying NLP, the global NLP market was over $12 billion in 2020, and is projected to grow at an annual rate of 25% to $43 billion by 2025.

According to the "AI adoption in the enterprise 2020" survey by O'Reilly, 85% of organizations are using or evaluating to use AI. Other statistics on AI adoption clearly show that there is a significant interest in AI and ML in businesses as AI/ML provide numerous benefits through a diverse set of applications. However, successful applications require expertise in areas such as data processing and model building, and not every business has the resources to hire, train, and maintain in-house teams of machine learning professionals. Outsourcing machine learning projects to ML outsourcing companies or consultants is an alternative approach to implementing ML applications to your business. One of the most common questions faced by businesses that are planning to embark on a machine learning application is whether to implement it with an in-house team or outsource their ML project to an external AI/ML company.

Okay, let's get to it. It takes time, it can be tedious, and it's something I just don't enjoy. I know many photographers feel the same. Of course, you can outsource it, but that can quickly become expensive. But what if we could program AI (artificial intelligence) to understand what we like in terms of aesthetics and never have to waste time moving sliders again?

Building products that use machine learning or artificial intelligence comes with significant challenges. AI-driven products are not deterministic – they make mistakes, and they behave differently in seemingly similar situations, which is something users are not typically comfortable with. They might also make recommendations that a user disagrees with or didn't expect. Not only is this a risk for the user – they might choose to ignore all the AI features as a result – but it could lead to experiences that make the user decide against using the product again. In this article, we explore three major types of ML-driven products and provide five design considerations for ML product managers.

These days it's hard to find a public company that isn't talking up how artificial intelligence is transforming its business. From the obvious (Tesla using AI to improve auto-pilot performance) to the less obvious (Levis using AI to drive better product decisions), everyone wants in on AI. To get there, however, organizations are going to need to get a lot smarter about data. To even get close to serious AI you need supervised learning which, in turn, depends on labeled data. Raw data must be painstakingly labeled before it can be used to power supervised learning models.

Accountants can build the kinds of skills needed to stay relevant in a rapidly automating world that's increasingly taking advantage of technologies such as robotic process automation and artificial intelligence to handle routine financial tasks, according to Institute of Management Accountants president and CEO Jeff Thomson. "It is a bit of a race where technology capability is really moving at warp speed, but it's not clear that the profession's ability to upskill and transform itself is moving at warp speed," Thomson told Accounting Today. "Therefore it's a race for relevance, creating the story, and telling the story of our profession because we want to attract technologists into our profession." He believes the accounting profession will need to do a better job of competing for students and finding ways to attract them to the profession, given its reputation for routine work that is increasingly being automated away. "We get to work with the latest technologies, but not if we don't make that part of our profession," said Thomson.