Learning and Artificial Intelligence (ML/AI) techniques have become increasingly prevalent in high performance computing (HPC). However, these methods depend on vast volumes of floating point data for training and validation which need methods to share the data on a wide area network (WAN) or to transfer it from edge devices to data centers. Data compression can be a solution to these problems, but an in-depth understanding of how lossy compression affects model quality is needed. Prior work largely considers a single application or compression method. We designed a systematic methodology for evaluating data reduction techniques for ML/AI, and we use it to perform a very comprehensive evaluation with 17 data reduction methods on 7 ML/AI applications to show modern lossy compression methods can achieve a 50-100x compression ratio improvement for a 1% or less loss in quality. We identify critical insights that guide the future use and design of lossy compressors for ML/AI.

The viral awareness and adoption of artificial intelligence foundation models such as OpenAI LP's ChatGPT have created both an opportunity and threat to automation platforms generally and robotic process automation point tools specifically. On the one hand, large language models can reduce complexity and accelerate the adoption of enterprise automation platforms. The flip side is that software robots are designed to improve human productivity through intelligent automation and GPT models could cannibalize some, if not many, use cases initially targeted by RPA vendors. This reality is causing customers to rethink their automation strategies and vendors to evolve their messaging rapidly to position foundation models as an accelerant to their platforms. In this Breaking Analysis, we provide you with a perspective on how foundation models could have an impact on automation platforms. We review Enterprise Technology Research data that quantifies the ascendency of OpenAI.

Quantitative trading is the usage of mathematical models or algorithms to create trading strategies and trade them. Quant trading is usually employed by large institutional traders or hedge funds who employ large teams of PhDs and engineers. Historically the quantitative trading field has been very secretive and ideas which work tend to be guarded very closely by the firms but in the last few years the growth of openly available datasets and access to compute i.e ( in the form of GPUs and cloud) has made quant trading accessible to a larger audience. Each of the above steps involve lot of research and trial and error to get right. Quant trading is a complex field and requires careful and detailed study to be successful. The following are 10 such books which can help one get started on their Quant journey.

It has been less than 18 months since we published our last MAD landscape, and it has been full of drama. When we left, the data world was booming in the wake of the gigantic Snowflake IPO, with a whole ecosystem of startups organizing around it. Since then, of course, public markets crashed, a recessionary economy appeared and VC funding dried up. A whole generation of data/AI startups has had to adapt to a new reality. Meanwhile, the last few months saw the unmistakable, exponential acceleration of Generative AI, with arguably the formation of a new mini-bubble.

An increasing number of Enterprise customers are adopting ML/AI as their core transformational pillars, in order to differentiate, increase revenue, reduce costs and maximize efficiency. For many customers ML/AI adoption can be a challenging endeavor not only because of the broad spectrum of applications ML/AI can support, deciding on which one to prioritize can be a challenge, but because moving these solutions into production require a series of security, access and data assessments and features that some ML/AI platforms might not have. This blog post focuses on how to set up your Cloud foundations to cater specifically to the Vertex AI platform and its configuration to be able to set up proper Vertex AI foundations for your future machine learning operations (MLOps) and ML/AI use cases.

One difference in the nature of Hero methods vs non-Hero methods, is the level of understanding of the problem world and data. Non-Hero development is mature; one can relatively easily write and build to a set of requirements, with much of the difficulty arising from the process and human side of things. Hero development includes this and one additional challenge -- the world and data may be unknown, invisible and always changing. This makes it hard to build to a set of requirements as we may not know what to require until we actually perform some modelling. Sometimes, we may be able to "write requirements" since the objective of the experiment is to make and validate hypotheses about the space.

As more enterprises automate away the tedium faced by in-house departments, a question looms: why haven't in-house legal departments caught up? Internal legal processes for drafting, analyzing, and managing simple legal documents are still manual and tedious. What is stopping legal departments from automating away the pain? As it turns out, a major barrier for adoption lies in the most common means of automation itself: Machine learning. Contract Lifecycle Management (CLM) software streamlines and automates several stages of the contract lifecycle - from the initial drafting stages all the way up to negotiation, signature, and the final expiration of a contract.

Understanding how important representation, role models, and mentoring had been to my own career journey, I started a network to support other Amazon employees looking to pursue a career in machine learning (ML) and artificial intelligence (AI). Open to anyone working at Amazon, the global Women in ML/AI group hosts regular networking events and organizes panel discussions with industry experts on career development. To discuss learnings from our professional journey, I sat down with fellow board members, including senior documentation manager Michelle Luna, senior software development manager Anna Khabibullina and general manager and product lead Shubha Pant. Here are some of the advice we found invaluable when launching and building a career in the field. Luna, Khabibullina, Pant and I are all proof that there are many paths into ML and AI -- from the traditional and linear, to the more unconventional.

Technology has dramatically changed how the financial services industry operates. This has been consistent over many decades; however, recently the pace of change has become exceptionally fast. The fintech market has deployed these technologies to disrupt the broader industry by enhancing the customer experience and changing the traditional customer acquisition model. The next evolution of fintech will focus on the back end and middleware software that powers the financial services industries. What few market participants realize is the shiny front-end customer experience of these new fintechs and neobanks are still often powered by traditional banking systems.

AI ethics is a hot topic these days, so you see all kinds of rhetoric zooming around. Complaints range from "the robots took my job" to "your computer system is just as biased as you are (you jerk)." Why aren't we talking about what makes ML/AI uniquely more dangerous than other technologies? The topics that come up in connection with AI ethics are vital, timely, and necessary. I just wish we wouldn't use the term AI ethics whenever it… isn't even about AI.