Let’s talk about “full organizational autonomy”.
Have you ever stopped to think about what organizations might look like once people are no longer required to manage or operate them? The organizational constraints imposed by the human desire to create offices have been the prevalent mindset of most businesses for over a century. With the desire to reach full autonomy, organizations are rethinking office spaces and focusing their time on automation, software, and robots.
Organizations have been designed around the way people communicate and make decisions. With the rise of remote work, the cost of communication dropping to zero, and the…
Developing machine learning systems is painfully slow. Machine learning (ML) models take months to develop and deploy. By the time ML models are deployed into production, development velocity is at a crawl. Let’s understand why.
Developing ML systems is a complex endeavour. Machine learning practitioners (researchers, developers, engineers, data scientists) are called in to save the day. Yet despite best intentions, machine learning teams struggle to operationalize and productionize models. ML systems in production are influenced by four factors: developer velocity, complexity, technical debt, and workflows.
As developers write “crappy code”, the development velocity decreases over time. Developer velocity is…
Apps promise to give us superpowers. Browser plug-ins, add-ons, or extensions promise to make us more productive. Operating systems and software, in general, aim to solve our problems. By ourselves, we feel productive. When we join teams, our productivity plummets.
Dropbox and Slack promised to “solve work” for everyone. Launched in 2007, Dropbox aimed to capture and manage company documents and become the company’s layer of work. Six years later, Slack promised messaging would become the center of work for distributed teams around the world.
What’s striking is both products failed to fulfill the core requirement of distributed teams —…
Developing and maintaining artificial intelligence (AI) and machine learning (ML) systems takes time and costs money. From prototype to production, there are visible and hidden costs of AI systems or ML models. Understanding real costs (or true costs) of machine learning technologies allows executives and decision-makers to make better strategic decisions.
There are many costs in developing and deploying machine learning models. Some costs are more visible, such as research costs, development costs, and production costs. Other costs are less visible, such as opportunity costs.
The real costs of ML systems include:
The coronavirus disease 2019, also known as COVID-19, is causing havoc on the society and economy. As of July 2, 2020, more than 10 million have been infected by COVID-19 and more than 500 thousand have died. As individuals and companies brace for an economic recession, they evaluate their budgets and cut costs in response to this global pandemic. And while financial measures are a good place to start, it is not enough.
Intelligence technologies will help humanity deal with COVID-19. …
Every year, more than 50 thousand people die from earthquakes. Since 1990, earthquakes have been responsible for more than 800 thousand deaths and have left 17+ million people homeless. The annual damage resulting from earthquakes is estimated to be USD 35 Billion.¹ Given the massive impact on human life and the economy, can we find a solution that allows humanity to better deal with earthquakes? Is it possible to predict earthquakes? At Produvia, we believe earthquakes can be predicted. We believe that earthquake magnitude and location can be predicted with 99% accuracy.
In this blog post, we will explore how…
At Produvia, we produce intelligent software. We also write letters about artificial intelligence (AI) to founders, executives, and decision-makers from all industries. These letters are meant to inspire and motivate companies, government agencies, and countries on the topics of AI, machine learning, and deep learning technologies.
We believe that artificial intelligence technologies will fundamentally change how genomics, biotechnology, and life sciences startups and companies turn data into actionable insights.
Before we talk about artificial intelligence, it is important to understand the genomics industry first.
The global genomics industry is worth $16.4 Billion USD as of 2018 and is expected to…
Can cars or bikes drive themselves? Can ships or vessels steer themselves? Are drones or airplanes able to fly themselves? In this blog post, we explore how artificial intelligence is solving these questions for the aerospace industry.
For decades, commercial automotive companies built software to assist drivers in getting from one location to another. Driving assistance technologies such as emergency brake assists (EBAs) helped drivers deal with sudden breaking scenarios. Global positioning systems (GPSs) helped drivers navigate the streets with step-by-step driving directions.
For decades, commercial aerospace companies built systems to assist pilots in transporting passengers from one destination to…
The future of medical diagnosis is brighter than ever before. Imagine what would happen if the medical industry could predict strokes or heart failures? This is possible today, thanks to artificial intelligence technologies. Mayo Clinic recently published research  in the latest issue of The Lancet, which showed that machine learning models can identify individuals with atrial fibrillation, or abnormal heart rhythm, with an accuracy of 79–83%. We will dive into this research to understand what the team did well, what they can improve upon, what is the timeline for seeing this technology in consumer’s hands,
Let’s analyze what the…
Update Dec 20, 2020: Produvia now offers custom AI strategies! Scroll to the bottom to learn more.
Your company needs an artificial intelligence strategy to become competitive in a data-driven world.
At Produvia, we believe that businesses, governments, academic institutions, non-profit organizations, national and international research consortia must define AI Strategy to not only survive against competitors but to become market leaders.
We also believe that organizations that create an AI Strategy will establish market dominance in the next 5–10 years.
Follow this document to create your own AI Strategy. Message Produvia to help your team define one.