Amazon has scrapped a "sexist" tool that used artificial intelligence to decide the best candidates to hire for jobs. Members of the team working on the system said it effectively taught itself that male candidates were preferable. The artificial intelligence software was created by a team at Amazon's Edinburgh office in 2014 as a way to automatically sort through CVs and select the most talented applicants. But the algorithm rapidly taught itself to favour male candidates over female ones, according to members of the team who spoke to Reuters. Amazon wage increase could result in lower pay for some employees Black Friday 2018: The best Amazon deals Will Amazon's deliver-on-demand smart homes be the future of housing? Will Amazon's deliver-on-demand smart homes be the future of housing?

But first, let's dive a little deeper into understanding AI and how it uses various technologies to create practical applications for the CPG industries: Making intelligent business decisions is critical for any industry, but particularly so for the CPG segment. Effective decision-making across a variety of business areas forms the backbone of the CPG business. These decisions have to be made fast and with utmost accuracy. But how can businesses achieve this when they're bogged down by massive amounts of data? Coupled with the sheer amount of data is the challenge of not being able to leverage any manner of advanced technology to convert it into actionable intelligence.

New facial recognition technology has identified three impostors at Washington Dulles International Airport. Citing a U.S. Customs and Border Protection release, The Washington Post reports a woman arriving on a Monday flight from Accra, Ghana, presented a U.S. passport, but the facial recognition technology reported a mismatch. A secondary inspection and biometric examination identified her as a 26-year-old citizen of Cameroon, not the United States. The release says the Metropolitan Washington Airports Authority partnered with CBP to use biometric entry and exit technology using facial comparison to bolster security and efficiency for international travelers. Officers at Dulles previously intercepted a Congolese man using a French passport Aug. 22 and a Ghanaian woman using a U.S. passport Sept. 8. Posing as another person when entering the United States violates immigration law.

In this, the first episode of the Deep Learning Indaba series, we're joined by Sara Hooker, AI Resident at Google Brain. I had the pleasure of speaking with Sara in the run-up to the Indaba about her work on interpretability in deep neural networks. We discuss what interpretability means and when it's important, and explore some nuances like the distinction between interpreting model decisions vs model function. We also dig into her paper Evaluating Feature Importance Estimates and look at the relationship between this work and interpretability approaches like LIME. We also talk a bit about Google, in particular, the relationship between Brain and the rest of the Google AI landscape and the significance of the recently announced Google AI Lab in Accra, Ghana, being led by friend of the show Moustapha Cisse.

Abstract-- Feature Selection (FS) plays an important role in learning and classification tasks. The object of FS is to select the relevant and non-redundant features. Considering the huge amount number of features in real-world applications, FS methods using batch learning technique can't resolve big data problem especially when data arrive sequentially. In this paper, we propose an online feature selection system which resolves this problem. More specifically, we treat the problem of online supervised feature selection for binary classification as a decision-making problem. A philosophical vision to this problem leads to a hybridization between two important domains: feature selection using online learning technique (OFS) and automated negotiation (AN). The proposed OFS system called MOANOFS (Multi-Objective Automated Negotiation based Online Feature Selection) uses two levels of decision. In the first level, from n learners (or OFS methods), we decide which are the k trustful ones (with high confidence or trust value). These elected k learners will participate in the second level. In this level, we integrate our proposed Multilateral Automated Negotiation based OFS (MANOFS) method to decide finally which is the best solution or which are relevant features. We show that MOANOFS system is applicable to different domains successfully and achieves high accuracy with several real-world applications. Index Terms-- Feature selection, online learning, multi-objective automated negotiation, trust, classification, big data. URING the last three decades, Feature Selection (FS) has been extensively studied in Data Mining [1], [2], Pattern Classification [3], [4] and Machine Learning [5], [6]. FS is defined as the process of selecting a subset of relevant features and removing the redundant ones from a dataset for building effective prediction models. In recent years, an enormous increase in data (news, medical imaging) has been observed which allows an increase in redundant information. Even worse, the redundancy of irrelevant data has a negative impact on the performance of classification methods associated. With the rapid development of the Internet, current tremendous amounts of data up to millions or billions, can be collected for training machine learning models.

The summit will focus on understanding the financially excluded segment, use of alternative data (geospatial, social media, mobile footprint, psychographics) in developing credit risk scoring algorithm, and building simpler AI-enabled financial delivery interfaces. The Summit is scheduled to hold on Wednesday, 10 October 2018 at the Oriental Hotel, Victoria Island, Lagos and with the theme, "New Algorithms for the Financially Excluded Segment". This is a broad based stakeholder session focused on understanding emerging trends and advanced data analytics use cases applied to issues of financial inclusion. The one-day Summit will be followed by a five-day residential, all-expenses-paid Artificial Intelligence Bootcamp and Hackathon on emerging trends in machine learning and deep learning between 10 and 14 October 2018. The intent of the bootcamp and hackathon is to build world-class capacity in advanced data analytics, upskill financial inclusion data analysts and researchers in emerging best practices, and to support the development of contextually relevant algorithm and tech innovation.

Thin film processing by means of sputter deposition inherently depends on the interaction of energetic particles with a target surface and the subsequent particle transport. The length and time scales of the underlying physical phenomena span orders of magnitudes. A theoretical description which bridges all time and length scales is not practically possible. Advantage can be taken particularly from the well-separated time scales of the fundamental surface and plasma processes. Initially, surface properties may be calculated from a surface model and stored for a number of representative cases. Subsequently, the surface data may be provided to gas-phase transport simulations via appropriate model interfaces (e.g., analytic expressions or look-up tables) and utilized to define insertion boundary conditions. During run-time evaluation, however, the maintained surface data may prove to be not sufficient. In this case, missing data may be obtained by interpolation (common), extrapolation (inaccurate), or be supplied on-demand by the surface model (computationally inefficient). In this work, a potential alternative is established based on machine learning techniques using artificial neural networks. As a proof of concept, a multilayer perceptron network is trained and verified with sputtered particle distributions obtained from transport of ions in matter based simulations for Ar projectiles bombarding a Ti-Al composite. It is demonstrated that the trained network is able to predict the sputtered particle distributions for unknown, arbitrarily shaped incident ion energy distributions. It is consequently argued that the trained network may be readily used as a machine learning based model interface (e.g., by quasi-continuously sampling the desired sputtered particle distributions from the network), which is sufficiently accurate also in scenarios which have not been previously trained.

One of the most important issues we face today is global accessibility to healthcare. The WHO recently revealed that at least half of the world's population isn't able to access essential healthcare services. With large numbers of others being drawn into poverty by healthcare bills. According to the report, as covered by Reuters, there are 800 million people across the world that spend at least 10% of their household income on healthcare. A staggering 100million of those left with less than $1.90 a day to survive on.

Do you have an idea that could make you South Africa's most successful fintech entrepreneur? Are you looking to acquire the skills to launch a data-driven fintech business? A new one-year data science and business skills programme aims to assist aspirant SA fintech entrepreneurs. In an announcement today, the Explore Data Science Academy (Edsa) and Rand Merchant Investments' (RMI) fintech division, AlphaCode, said their Explore 10X programme would assist 20 aspiring SA future fintech entrepreneurs. Successful candidates will go through an intensive six-month data science-training programme, where they will learn how to design a fast-growing business along with the core digital skills needed to build a fintech organisation.

The Cape Town International Convention Centre hosted Miss Pepper, a humanoid robot capable of detecting human emotions during the recent BIOMIN World Nutrition Forum 2018. Developed by SoftBank Robotics, "Miss Pepper" who is capable of interacting with humans and can adapt her interaction according to a human's emotions, was unveiled during the three-day international conference. According to SoftBank Robotics, the Pepper robot is the "first humanoid robot capable of recognising the principal human emotions and adapting his behaviour to the mood of his interlocutor". Herbert Kneissl, chief marketing officer at Erber AG, the parent company of Biomin, said robotics, artificial intelligence and big data will become an everyday topic in the livestock industry. "She (Miss Pepper) is able to perceive emotions, and what is a conference like the Biomin World Nutrition Forum without emotions? The digital experts of Biomin additionally trained her to be the little star on the stage - to welcome the audience, break the tensions, but in first line to demonstrate that the future of using humanoid robots has started now. "By using Miss Pepper we have the intention to create the image overflow from technological developments of other industries to biotechnology and animal nutrition, an overflow that develops already towards a merger, when we see the modern technologies like Farm 4.0, one of the big topics of this conference," said Kneissl. Miss Pepper is capable of recognising faces, speech, and can hear; she can also identify when someone is experiencing joy, sadness, is surprised and filled with anger. Over and above recognising these emotions, Miss Pepper can also detect tone of voice, smiles, and frowns. Facilitating emerging technologies "At the CTICC, we are committed to facilitating the introduction of emerging technologies such as Miss Pepper to delegates and visitors.