Established in 2018, the Machine Intelligence Technology Laboratory comprises of a group of outstanding scientists and engineers, with research centers located in Hangzhou, Beijing, Seattle, Silicon Valley, and Singapore. Machine Intelligence Technology Laboratory is Alibaba's core team responsible for the research and development of artificial intelligence technologies. Relying on Alibaba's valuable massive data and machine learning/deep learning technologies, the lab has developed image recognition, speech interaction, natural language understanding, intelligent decision-making, and other core artificial intelligence technologies. It fully empowers Alibaba Group's important businesses such as e-commerce, finance, logistics, social interaction, and entertainment, and also provides outputs to ecosystem partners to jointly build a smart future. Image Search is an intelligent image search product that enables search by image using image recognition and search functions, based on deep learning and large-scale machine learning technologies.

In this interview, Tamim Saleh cuts through the hype around artificial intelligence with guidance for executives about where and how to employ AI in their businesses. In this episode of our Inside the Strategy Room podcast, senior partner Tamim Saleh cuts through the hype around artificial intelligence (AI) and offers clear guidance for executives looking to make precise strategic decisions about where and how to employ AI in their businesses. Tamim shares insights on the impact of machine vision on AI, the future of voice recognition, and the latest developments in advanced analytics, virtual assistants, and robotics. He outlines the challenges companies face when adopting AI and the steps CEOs can take to overcome them. Tamim is a senior partner in our London office, and he is with me at our Global CFO Forum, where he's speaking about AI and machine learning. Tamim, one of the things you've talked about is the notion of five different developments of AI. Tamim Saleh: Machine learning and AI are limited by the fact that when we input data as humans, first of all we are slow, and we make mistakes. One of the fastest-growing technologies is capturing data through image analytics and cameras. And the beauty of this is, cameras don't make the same mistakes we do, because they capture things the way they are, and they don't see the world the same way that we do. In fact, the spectrum is much wider than what we see. It includes infrared, et cetera.

It is often said that data is the most valuable asset a business can have; the oil of a digital era. But data itself, while interesting, often leaves out a variety of important details – creating a need for analytics. And how we complete these analytics has evolved – and will continue to do so; particularly with the rapid proliferation of AI tools and technologies. How is AI making analytics more powerful? How is AI making analytics easier to produce?

Every company today is a tech company, a maxim that was proven out today when one of the world's oldest and biggest art auction houses acquired an AI startup. Sotheby's has bought Thread Genius, which has built a set of algorithms that can both instantly identify objects and then recommend images of similar objects to the viewer. Sotheby's' said it is not disclosing the value of the deal but said it was non-material to the company. Thread Genius was a relatively young company, founded in 2015 and making a debut last year as part of TechStars New York's Winter 2017 cohort. Co-founders Andrew Shum and Ahmad Qamar, who were also Thread Genius's only two employees, were both engineering alums from Spotify.

Sequential pattern mining techniques extract patterns corresponding to frequent subsequences from a sequence database. A practical limitation of these techniques is that they overload the user with too many patterns. Local Process Model (LPM) mining is an alternative approach coming from the field of process mining. While in traditional sequential pattern mining, a pattern describes one subsequence, an LPM captures a set of subsequences. Also, while traditional sequential patterns only match subsequences that are observed in the sequence database, an LPM may capture subsequences that are not explicitly observed, but that are related to observed subsequences. In other words, LPMs generalize the behavior observed in the sequence database. These properties make it possible for a set of LPMs to cover the behavior of a much larger set of sequential patterns. Yet, existing LPM mining techniques still suffer from the pattern explosion problem because they produce sets of redundant LPMs. In this paper, we propose several heuristics to mine a set of non-redundant LPMs either from a set of redundant LPMs or from a set of sequential patterns. We empirically compare the proposed heuristics between them and against existing (local) process mining techniques in terms of coverage, redundancy, and complexity of the produced sets of LPMs.

Pattern matching and predicting an exigent need in hospitals is a difficult task for skilled medical staffs, but not for AI and machine learning. Medical staffs do not have the luxury of observing each of their patients on a full-time basis. Although incredibly good at identifying the immediate needs of patients in obvious circumstances, nurses and medical staffs do not possess the capabilities of discerning the future from a complex array of patient symptoms exhibited over a reasonable period. Machine learning has the luxury of not only observing and analyzing patient data 24/7, but also combining information collected from multiple sources, i.e. historical records, daily evaluations by medical staff, and real-time measurements of vitals such as heart rate, oxygen usage and blood pressure. The application of AI in the assessment and prediction of imminent heart attacks, falls, strokes, sepsis and complications is currently underway all over the world.

Insurtech startup Hippo Insurance announced this week it has formed a partnership with zesty.ai, Through the new partnership, Hippo is now able to leverage computer vision technology and property attributes zesty.ai AI technology and data will bring our customers real-time insights on their properties, which expedites the application process upfront and helps us identify potential issues on their properties in the future – like brush encroaching on their property fireline, or necessary roof repairs. We're reshaping home insurance into a proactive product by alerting our clients to property issues before they become accidents and we're proud to have partnered with zesty.ai

Human computer interaction facilitates intelligent communication between humans and computers, in which gesture recognition plays a prominent role. This paper proposes a machine learning system to identify dynamic gestures using tri-axial acceleration data acquired from two public datasets. These datasets, uWave and Sony, were acquired using accelerometers embedded in Wii remotes and smartwatches, respectively. A dynamic gesture signed by the user is characterized by a generic set of features extracted across time and frequency domains. The system was analyzed from an end-user perspective and was modelled to operate in three modes. The modes of operation determine the subsets of data to be used for training and testing the system. From an initial set of seven classifiers, three were chosen to evaluate each dataset across all modes rendering the system towards mode-neutrality and dataset-independence. The proposed system is able to classify gestures performed at varying speeds with minimum preprocessing, making it computationally efficient. Moreover, this system was found to run on a low-cost embedded platform - Raspberry Pi Zero (USD 5), making it economically viable.

Medical applications challenge today's text categorization techniques by demanding both high accuracy and ease-of-interpretation. Although deep learning has provided a leap ahead in accuracy, this leap comes at the sacrifice of interpretability. To address this accuracy-interpretability challenge, we here introduce, for the first time, a text categorization approach that leverages the recently introduced Tsetlin Machine. In all brevity, we represent the terms of a text as propositional variables. From these, we capture categories using simple propositional formulae, such as: if "rash" and "reaction" and "penicillin" then Allergy. The Tsetlin Machine learns these formulae from a labelled text, utilizing conjunctive clauses to represent the particular facets of each category. Indeed, even the absence of terms (negated features) can be used for categorization purposes. Our empirical results are quite conclusive. The Tsetlin Machine either performs on par with or outperforms all of the evaluated methods on both the 20 Newsgroups and IMDb datasets, as well as on a non-public clinical dataset. On average, the Tsetlin Machine delivers the best recall and precision scores across the datasets. The GPU implementation of the Tsetlin Machine is further 8 times faster than the GPU implementation of the neural network. We thus believe that our novel approach can have a significant impact on a wide range of text analysis applications, forming a promising starting point for deeper natural language understanding with the Tsetlin Machine.

Facebook's moderators can't possibly look through every single image that gets posted on the enormous platform, so Facebook is building AI to help them out. In a blog post today, Facebook describes a system it's built called Rosetta that uses machine learning to identify text in images and videos and then transcribe it into something that's machine readable. In particular, Facebook is finding this tool helpful for transcribing the text on memes. Text transcription tools are nothing new, but Facebook faces different challenges because of the size of its platform and the variety of the images it sees. Rosetta is said to be live now, extracting text from 1 billion images and video frames per day across both Facebook and Instagram.