Welcome to our weekly selection of digital innovation news. Based on our opinionated token based selection algorithm we present some top innovation news to get you thinking, debating and collaboration on making our world better.
1 Applying machine learning to challenges in the pharmaceutical industry
The consortium aims to break down the divide between machine learning research at MIT and drug discovery research — bringing MIT researchers and industry together to identify and address the most significant problems. MIT continues its efforts to transform the process of drug design and manufacturing with a new MIT-industry consortium, the Machine Learning for Pharmaceutical Discovery and Synthesis. MIT principal investigators for the consortium span different areas and departments, bringing expertise in machine learning, chemistry, and chemical engineering. The summit gathered MIT researchers with leaders of technology, biotech, and regulatory agencies to engage in ways digital technologies and artificial intelligence can help address major challenges in the biomedical and health care industries. I am continuously learning from them and getting new problems to think about.” Barzilay says that one of the goals of the consortium is to establish evaluation standards and create benchmark datasets for assessing the accuracy of machine learning methods.
2 Building AI systems that make fair decisions
In machine learning, we typically use historical data and train a model to detect patterns in the data and make new predictions. Suresh studies the societal implications of automated systems in MIT Professor John Guttag’s Data-Driven Inference Group, which uses machine learning and computer vision to improve outcomes in medicine, finance, and sports. My co-organizer and I tried to keep our goals of accessibility and inclusivity at the forefront when making decisions about the course. This involves both detecting bias or underrepresentation in the data as well as figuring out how to mitigate it at different points in the machine learning pipeline. Even if the data isn’t biased, if we just have way less data on a certain group, predictions for that group will be worse.
3 Auto-tuning data science: New research streamlines machine learning
“A small- to medium-sized data science team can set up and start producing models with just a few steps,” Veeramachaneni says. To get to these data-driven solutions, though, data scientists must shepherd their raw data through a complex series of steps, each one requiring many human-driven decisions. ATM can run on a single machine, local computing clusters, or on- demand clusters in the cloud, and can work with multiple data sets and multiple users simultaneously. On this platform, data scientists work together to solve problems, finding the best solution by building on each other’s work. They have also included provisions that allow researchers to integrate new model selection techniques and thus continually improve on the platform.
4 A Framework for Validating Models of Evasion Attacks on Machine Learning, with Application to PDF Malware Detection.
Machine learning (ML) techniques are increasingly common in security applications, such as malware and intrusion detection. Central to our inquiry is the following fundamental question: are feature space models of attacks useful proxies for real attacks? However, there is increasing evidence that machine learning models are susceptible to evasion attacks, in which an adversary makes small changes to the input (such as malware) in order to cause erroneous predictions (for example, to avoid being detected). Evasion attacks on ML fall into two broad categories: 1) those which generate actual malicious instances and demonstrate both evasion of ML and efficacy of attack (we call these problem space attacks), and 2) attacks which directly manipulate features used by ML, abstracting efficacy of attack into a mathematical cost function (we call these feature space attacks). In the process of answering this question, we make two major contributions: 1) a general methodology for evaluating validity of mathematical models of ML evasion attacks, and 2) an application of this methodology as a systematic hypothesis- driven evaluation of feature space evasion attacks on ML-based PDF malware detectors.
5 Solving global business problems with data analytics
If you open any business journal you will see references to data science and data analytics,” Simchi-Levi says. He has founded three companies in the fields of supply chain and business analytics: LogicTools, a venture focused on supply chain analytics, which became a part of IBM; OPS Rules, a business analytics venture that was acquired by Accenture Analytics; and Opalytics, which focuses on cloud computing for business analytics. First, they utilize machine learning to combine internal historical data with external data to create a complete profile of consumer behavior.
6 Google Duplex: An AI System for Accomplishing Real-World Tasks Over the Phone
Duplex asking for holiday hours: For users, Google Duplex is making supported tasks easier. Today we announce Google Duplex, a new technology for conducting natural conversations to carry out “real world” tasks over the phone. Google Duplex takes a step in this direction, making interaction with technology via natural conversation a reality in specific scenarios. The Google Duplex technology is built to sound natural, to make the conversation experience comfortable. To obtain its high precision, we trained Duplex’s RNN on a corpus of anonymized phone conversation data.
7 Securing Majority-Attack In Blockchain Using Machine Learning And Algorithmic Game Theory: A Proof of Work.
Recently we could see several institutions coming together to create consortium based blockchain networks such as Hyperledger. Although for applications of blockchain such as Bitcoin, Litcoin, etc. the majority-attack might not be a great threat but for consortium based blockchain networks where we could see several institutions such as public, private, government, etc. are collaborating, the majority-attack might just prove to be a prevalent threat if collusion among these institutions takes place. This paper proposes a methodology where we can use intelligent software agents to monitor the activity of stakeholders in the blockchain networks to detect anomaly such as collusion, using supervised machine learning algorithm and algorithmic game theory and stop the majority-attack from taking place.
8 Try Before You Buy: How to Design Information Systems to Enhance Consumer Willingness to Test Sustainable Innovations
This paper provides insight on how sustainable innovation testing affects consumer mindsets and which barriers consumers face when considering testing a sustainable innovation. More and more business organizations recognize the relevance of sustainable innovations as driving factor for their corporate strategies, products and processes. Insights about the nature of consumer’s willingness to test are extracted and recommendations for the design and use of information systems as facilitators for testing sustainable innovations are derived.
But while the concept of sustainability is generally ratified by employees and consumers, their willingness to actually use or buy such innovations can be low. One of the most important facilitators for the adoption of innovations is self-experience generated by testing the innovation.
9 Building a Business on Open Source
Building a Business on Open Source Building on open source gives you options that proprietary developers don’t have. There are a number of executives from open source- heavy software vendors who have openly remarked that it is impossible to create a successful business with a “pure” open source approach. Many companies have adopted a particular model around creating and selling open source software: create an open source platform or base and sell proprietary software that integrates with the underlying open source platform. It is not that there is no money in selling open source software, but rather that the business models have shifted. “It is not that there is no money in selling open source software, but rather that the business models have shifted.” Let’s use open source instead.” Since then, every single area of innovation in computing is now dominated by the open source platforms among us.
10 Photonic communication comes to computer chips
The chips could also be used in supercomputers, Wright-Gladstein adds, which have similar efficiency issues and speed constraints as data centers do. The unique design integrates speedy, efficient optical communications — with components that transmit data using light waves — into traditional computer chips, replacing less efficient copper wires. Ayar is also excited about what its new technology means for the field of optics, Wright-Gladstein says. Optical chips can, therefore, transmit more information using significantly less space. Backed by years of research at MIT and elsewhere, Ayar has developed chips that move data around with light but compute electronically.
The Radical Open Innovation weekly overview is a brief overview of innovation news on Digital Innovation and Management Innovation from all over the world. Your input for our next edition is welcome! Send it to [info] at [bm-support]dot[org]