Monthly Archives: April 2020

Unknown's avatar Rick's Cafe AI 9:19 pm on April 27, 2020
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Artificial Intelligence and UK National Security

The research has found that AI offers numerous opportunities for the UK national security community to improve efficiency and effectiveness of existing processes. AI methods can rapidly derive insights from large, disparate datasets and identify connections that would otherwise go unnoticed by human operators. However, in the context of national security and the powers given to UK intelligence agencies, use of AI could give rise to additional privacy and human rights considerations which would need to be assessed within the existing legal and regulatory framework. For this reason, enhanced policy and guidance is needed to ensure the privacy and human rights implications of national security uses of AI are reviewed on an ongoing basis as new analysis methods are applied to data. The research highlights three ways in which intelligence agencies could seek to deploy AI:

  1. The automation of administrative organisational processes could offer significant efficiency savings, for instance to assist with routine data management tasks, or improve efficiency of compliance and oversight processes.


  2. For cybersecurity purposes, AI could proactively identify abnormal network traffic or malicious software and respond to anomalous behaviour in real time.


  3. For intelligence analysis, ‘Augmented Intelligence’ (AuI) systems could be used to support a range of human analysis processes, including:

    • Natural language processing and audiovisual analysis, such as machine translation, speaker identification, object recognition and video summarisation.

    • Filtering and triage of material gathered through bulk collection.

    • Behavioural analytics to derive insights at the individual subject level.


Read More #ic
Unknown's avatar Rick's Cafe AI 12:58 pm on April 26, 2020
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Defining The Services-As-Software Business Model For AI

My angel investment in Botkeeper has been one of the most influential in my thinking on how AI strategy is evolving. When new high impact technologies come along, they often shake up status quo business models and because no one understands what business models might emerge on the other side, it’s a wonderful time to make a few bets on some startups. As an investor, these initial bets help me learn how the space is evolving, which means when the real wave of startups comes that are embracing this new tech, I’m much more educated than most people who sat out the initial round. And on top of that, sometimes you get lucky on the early bets too. Read More

#investing

Unknown's avatar Rick's Cafe AI 11:07 am on April 24, 2020
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Why Data Is Not The Next Oil

Marketing, at least in the IT sector, has been replaced by memes. Every so often a Gartner slide deck goes viral, and the next thing anyone knows, pithy and mostly meaningless phrases and sayings are driving Fortune 500 strategies. Execs commit to multi-billion-dollar initiatives to make sure that their companies are perceived as being hip or cool (or, to use the more typical phrases, competitive and lean), big projects get greenlit, and at the end of the day, after a forced death march, the system goes live with a great big “meh”. Those same execs may see one or two quarters boost from the system of a couple of percentage points, but then the hemorrhaging begins anew.

The meme-factories recently spit out the meme “Data is the next Oil”. Translating from the Memespeak, what I believe this expression is intended to imply is that the data within your organization is valuable and that if you do not transform your organization to more effectively utilize that data, you will get left behind.

The problem with this is that it is true only for a small percentage of companies or for a limited period of time. Read More

#data-lake
Unknown's avatar Rick's Cafe AI 11:02 am on April 24, 2020
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How COVID-19 is Likely to Change Your AI Strategy

COVID-19 and the changes it creates in the business environment for the next 12 to 24 months means our current AI strategies need to thoroughly reviewed and probably retargeted. Read More

#strategy

Unknown's avatar Rick's Cafe AI 4:35 pm on April 20, 2020
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The Future of Work: Will Our Children Be Prepared?

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#videos

Unknown's avatar Rick's Cafe AI 4:28 pm on April 20, 2020
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Secure the software development lifecycle with machine learning

Every day, software developers stare down a long list of features and bugs that need to be addressed. Security professionals try to help by using automated tools to prioritize security bugs, but too often, engineers waste time on false positives or miss a critical security vulnerability that has been misclassified. To tackle this problem data science and security teams came together to explore how machine learning could help. We discovered that by pairing machine learning models with security experts, we can significantly improve the identification and classification of security bugs.

At Microsoft, 47,000 developers generate nearly 30 thousand bugs a month. These items get stored across over 100 AzureDevOps and GitHub repositories. To better label and prioritize bugs at that scale, we couldn’t just apply more people to the problem. However, large volumes of semi-curated data are perfect for machine learning. Since 2001 Microsoft has collected 13 million work items and bugs. We used that data to develop a process and machine learning model that correctly distinguishes between security and non-security bugs 99 percent of the time and accurately identifies the critical, high priority security bugs, 97 percent of the time. This is an overview of how we did it. Read More

#devops
Unknown's avatar Rick's Cafe AI 12:49 pm on April 19, 2020
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XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization

Much recent progress in applications of machine learning models to NLP has been driven by benchmarks that evaluate models across a wide variety of tasks. However, these broad-coverage benchmarks have been mostly limited to English, and despite an increasing interest in multilingual models, a benchmark that enables the comprehensive evaluation of such methods on a diverse range of languages and tasks is still missing. To this end, we introduce the Cross-lingual TRansfer Evaluation of Multilingual Encoders (XTREME) benchmark, a multi-task benchmark for evaluating the cross-lingual generalization capabilities of multilingual representations across 40 languages and 9 tasks. We demonstrate that while models tested on English reach human performance on many tasks, there is still a sizable gap in the performance of cross-lingually transferred models, particularly on syntactic and sentence retrieval tasks. There is also a wide spread of results across languages. We release the benchmark to encourage research on cross-lingual learning methods that transfer linguistic knowledge across a diverse and representative set of languages and tasks. Read More

#nlp
Unknown's avatar Rick's Cafe AI 11:48 am on April 18, 2020
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AutoML-Zero: Evolving Machine Learning Algorithms From Scratch

Machine learning research has advanced in multiple aspects, including model structures and learning methods. The effort to automate such research, known as AutoML, has also made significant progress. However, this progress has largely focused on the architecture of neural networks, where it has relied on sophisticated expert-designed layers as building blocks—or similarly restrictive search spaces. Our goal is to show that AutoML can go further: it is possible today to automatically discover complete machine learning algorithms just using basic mathematical
operations as building blocks. We demonstrate this by introducing a novel framework that significantly reduces human bias through a generic search space. Despite the vastness of this space, evolutionary search can still
discover two-layer neural networks trained by backpropagation. These simple neural networks can then be surpassed by evolving directly on tasks of interest, e.g. CIFAR-10 variants, where modern techniques emerge in
the top algorithms, such as bilinear interactions, normalized gradients, and weight averaging. Moreover, evolution adapts algorithms to different task types: e.g., dropout-like techniques appear when little data is available. We
believe these preliminary successes in discovering machine learning algorithms from scratch indicate a promising new direction for the field. Read More

#automl, #mlaas

Unknown's avatar Rick's Cafe AI 11:34 am on April 18, 2020
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Quantifying the effect of quarantine control in Covid-19 infectious spread using machine learning

Since the first recording of what we now call Covid-19 infection in Wuhan, Hubei province, China on Dec 31, 2019 (CHP 2020), the disease has spread worldwide and met with a wide variety of social distancing and quarantine policies. The effectiveness of these responses is notoriously difficult to quantify as individuals travel, violate policies deliberately or inadvertently, and infect others without themselves being detected (Li et al. 2020a; Wu & Leung 2020; Wang et al. 2020; Chinazzi et al. 2020; Ferguson et al. 2020; Kraemer et al. 2020). Moreover, the publicly available data on infection rates are themselves unreliable due to limited testing and even possibly under-reporting (Li et al. 2020b). In this paper, we attempt to interpret and extrapolate from publicly available data using a mixed first-principles epidemiological equations and data-driven neural network model. Leveraging our neural network augmented model, we focus our analysis on four locales: Wuhan, Italy, South Korea and the United States of America, and compare the role played by the quarantine and isolation measures in each of these countries in controlling the effective reproduction number Rt of the virus. Our results unequivocally indicate that the countries in which rapid government interventions and strict public health measures for quarantine and isolation were implemented were successful in halting the spread of infection and prevent it from exploding exponentially. In the case of Wuhan especially, where the available data were earliest available, we have been able to test the predicting ability of our model by training it from data in the January 24th till March 3rd window, and then matching the predictions up to April 1st.

Even for Italy and South Korea, we have a buffer window of one week (25 March – 1 April) to validate the predictions of our model. In the case of the US, our model captures well the current infected curve growth and predicts a halting of infection spread by 20 April 2020. We further demonstrate that relaxing or reversing quarantine measures right now will lead to an exponential explosion in the infected case count, thus nullifying the role played by all measures implemented in the US since mid March 2020. Read More

#neural-networks
Unknown's avatar Rick's Cafe AI 7:29 pm on April 14, 2020
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Learning To Explore Using Active Neural Slam

This work presents a modular and hierarchical approach to learn policies for exploring 3D environments, called ‘Active Neural SLAM’. Our approach leverages the strengths of both classical and learning-based methods, by using analytical path planners with learned SLAM module, and global and local policies. The use of learning provides flexibility with respect to input modalities (in the SLAM module), leverages structural regularities of the world (in global policies), and provides robustness to errors in state estimation (in local policies). Such use of learning within each module retains its benefits, while at the same time, hierarchical decomposition and modular training allow us to sidestep the high sample complexities associated with training end-to-end policies. Our experiments in visually and physically realistic simulated 3D environments demonstrate the effectiveness of our approach over past learning and geometry-based approaches. The proposed model can also be easily transferred to the PointGoal task and was the winning entry of the CVPR 2019 Habitat PointGoal Navigation Challenge. Read More

#deep-learning