The machine learning and AI-powered tools being deployed in response to COVID-19 arguably improve certain human activities and provide essential insights needed to make certain personal or professional decisions; however, they also highlight a few pervasive challenges faced by both machines and the humans that create them. Nevertheless, the progress seen in AI/machine learning leading up to and during the COVID-19 pandemic cannot be ignored. This global economic and public health crisis brings with it a unique opportunity for updates and innovation in modeling, so long as certain underlying principles are followed. Here are four industry truths (note: this is not an exhaustive list) my colleagues and I have found that matter in any design climate, but especially during a global pandemic climate. When a big group of people is collectively working on a problem, success may become more likely.
How to pick a cloud machine learning platform? To create an effective machine learning and deep learning model, you need more data, a way to clean the data and perform feature engineering on it. It is also a way to train models on your data in a reasonable amount of time. After that, you need a way to install your models, surveil them for drift over time, and retrain them as required. If you have invested in compute resources and accelerators such as GPUs, you can do all of that on-premises.
For several years, there has been a lot of discussion around AI's capabilities. Many believe that AI will outperform humans in solving certain areas. As the technology is in its infancy, researchers are expecting human-like autonomous systems in the next coming years. OpenAI has a leading stance in the artificial intelligence research space. Founded in December 2015, the company's goal is to advance digital intelligence in a way that can benefit humanity as a whole.
Though banks don't create AI strategies, they are increasingly using artificial intelligence and machine learning in their day-to-day business. We frequently work with them on ideation workshops, PoC, and solution implementation. Santander Consumer Bank, for example, is running workshops and researching how to use machine learning to boost the sustainability of loan portfolios. Besides credit risk modeling, there is already an impressive range of use cases for AI in banking. It covers everything, from customer service to back-office operations.
After decades in research labs, machine learning is now getting enormous attention for real-world applications that harness the technology's formidable power to discern patterns in huge quantities and types of data at high speed: fraud detection, customer 360, facial recognition, workflow management, shopping personalization and much more. The payback of such initiatives can be big. But even greater opportunities lie in creating advanced analytic systems that use machine learning's unmatched ability to see, organize and leverage insights from ever-growing mounds of data to unlock the deep, transformative potential of Big Data and the Internet of Things. To get to the next level of machine learning, companies must develop a sound business case; implement machine learning algorithms for speed at scale; use systems equipped with processors with multiple integrated cores, faster memory subsystems, and develop architectures that can handle massive amounts data in real time. For many organizations, it is an ideal time to build on or begin machine-learning experience, deepen knowledge, and reap the benefits and competitive advantages this sophisticated data analytics technology can provide.
Machine learning (ML) is quickly becoming a mainstay of the enterprise business world, yet entrepreneurs and small-business owners may shy away from investing in it. While you may not fully understand the ins and outs of ML or how it can benefit your small business, you can still make effective use of the technology without being an expert in it. We asked a panel of Forbes Technology Council members to share some smart ways entrepreneurs and small-business owners can leverage ML. Most ML models will require tons of data (the majority of them require supervised learning), which translates into a large effort that most entrepreneurs and small-business owners can't sustain. One approach is to leverage SaaS/PaaS services, such as the AWS portfolio of pre-trained artificial intelligence (AI) services: Comprehend, Rekognition, Lex, Personalize, Translate, Polly and others, each tailored to a specific domain.
With a large percentage of the global workforce based remotely for the foreseeable future, more business than ever is being conducted over email. And while this modern convenience has been critical to the continued operation of many businesses in the current health crisis, it has also presented those businesses with new data security challenges. The unfamiliar environment of remote work -- not to mention its potential distractions, like children and pets -- leaves employees more vulnerable to misdirected emails and other mistakes that can lead to accidental data breaches. Scams aimed at both individuals and organizations (even healthcare facilities on the front lines of the pandemic have not been immune to their efforts) have also risen, attempting to capitalize on the situation. Accidental breaches are notoriously difficult to combat because they can be caused by something as simple as a typo in an email address.
Financial crime as a wider category of cybercrime continues to be one of the most potent of online threats, covering nefarious actives as diverse as fraud, money laundering and funding terrorism. Today, one of the startups that has been building data intelligence solutions to help combat that is announcing a fundraise to continue fueling its growth. Ripjar, a UK company founded by five data scientists who previously worked together in British intelligence at the Government Communications Headquarters (GCHQ, the UK's equivalent of the NSA), has raised $36.8 million (£28 million) in a Series B, money that it plans to use to continue expanding the scope of its AI platform -- which it calls Labyrinth -- and scaling the business. Labyrinth, as Ripjar describes it, works with both structured and unstructured data, using natural language processing and an API-based platform that lets organizations incorporate any data source they would like to analyse and monitor for activity. It automatically and in real time checks these against other data sources like sanctions lists, politically exposed persons (PEPs) lists and transaction alerts.
Artificial Intelligence is a hot topic. Applications based on machine learning make the news on a near-daily basis. Smart police cameras steered by algorithms can register drivers holding cell phones with great precision. Algorithms can dynamically determine the real-time prices for taxi rides, hotel rooms, airplane seats, and so on. High-frequency traders are getting rich as they sleep by letting their secret algorithms do the work.