Offline multi-agent reinforcement learning (MARL) is an emerging field with great promise for real-world applications. Unfortunately, the current state of research in offline MARL is plagued by inconsistencies in baselines and evaluation protocols, which ultimately makes it difficult to accurately assess progress, trust newly proposed innovations, and allow researchers to easily build upon prior work. In this paper, we firstly identify significant shortcomings in existing methodologies for measuring the performance of novel algorithms through a representative study of published offline MARL work. Secondly, by directly comparing to this prior work, we demonstrate that simple, well-implemented baselines can achieve state-of-the-art (SOTA) results across a wide range of tasks. Specifically, we show that on 35 out of 47 datasets used in prior work (almost 75\% of cases), we match or surpass the performance of the current purported SOTA.

Neural networks, or deep learning, is a capability that is changing the way people live and work. From language translations to medical diagnosis to speech recognition to self-driving cars, deep learning is in the fabric of a technology revolution. But what is deep learning, and how much knowledge does a nontechnical or computer science stakeholder need to have to contribute to or run projects, or to spot opportunities for applications? How do healthcare executives know the potential data objectives faced can be addressed with deep learning? To add more complexity, the marketplace is filled with content and claims that will confuse even the most ardent expert.

Dispelling some of the myths about artificial intelligence and the impact it may have on companies will be the focus of keynote speaker Josh Comrie at the PwC Herald Talks: Business & Bots event this week. Comrie, chief executive and co-founder of chatbot developer Ambi, has about 20 years of experience in technology, taking relatively complex constructs and simplifying and articulating them for other people. Comrie will take what he calls one of the most exciting but also misunderstood evolutions of technology and present it so that people can understand the opportunities, ramifications and some of the pitfalls. "I think the misunderstanding stems from a variety of different information sources and that misinformation has led people to then have concerns and fears. It's the uncertainty that tends to give people fear," Comrie said.

Nevertheless, a growing number of AI-based software tools are becoming increasingly available for business leaders. Many organizations create smart business applications developed on top of tools that Google, Apple, Amazon, and other tech corporations create. Amazon's Alexa has already solved a complex problem of speaker-independent voice recognition, and its noise canceling technology allows using voice commands in noisy places. In a business setting, Alexa is widely used for a wide range of tasks on command, e.g. to start a meeting, control the equipment in a conference room or notify an IT department of an equipment issue. Whereas AI platforms such as IBM Watson can help you easily integrate AI into your application to store, train and manage your data in the secure cloud.

It's easy to see why organizations are looking to the potential of artificial intelligence to harness big data. From self-driving cars, robotic hotel concierges, and intelligent delivery drones to unlocking industry-specific insights and actions to outperform the competition, the combination of AI and big data can be a remarkable game changer. That's why companies have doubled down on investing dollars and resources into these types of initiatives. But what is often overlooked is that AI and more specifically machine learning, can hamper your efforts especially if combined with big data. Because AI is actually not intelligent, in that it still suffers from the garbage-in-garbage-out problem!

Artificial intelligence (AI) research has a long history, but even before computers existed Hollywood has been implanting a fear of intelligent machines into the public psyche, with more than sixty films to date depicting stereotypical threats to humankind. But only in the last few years has AI featured as a regular news story, invariably illustrated by images of suit-wearing robots seated around a board table. As intelligent machines have become mainstream, all kinds of media outlets serve up stories - some of them post-truth - about how robots will replace humans across a wide range of sectors: thanks to their superior speed and intelligence many current jobs will become obsolete. It will happen fast - within the next generation, according to those seeking to grab our attention, leaving humans to find alternative employment, if they can. As soon as 2021, robots will eliminate 6% of all US jobs, according to market research company Forrester while the World Economic Forum (WEF) predicts a loss of 7 million jobs within four years.