Robots are replacing human workers at a faster pace than any other point in history. Most of these robots are in factories, but a new kind of mechanized worker has hit apple orchards. Abundant Robotics in California has built an automated apple picker, that uses a vacuum system to suck the fruit straight off of the trees. "As a kid in Louisiana I was inspired by agricultural equipment such as combines, cotton pickers, and tractors," Abundant Robotics CEO Dan Steere told the Newshour. "The work we're doing is an extension of several hundreds of years of technology innovation for agriculture."

The Future of Humanity Institute (FHI) will be joining the Partnership on AI, a non-profit organisation founded by Amazon, Apple, Google/DeepMind, Facebook, IBM, and Microsoft, with the goal of formulating best practices for socially beneficial AI development. We will be joining the Partnership alongside technology firms like Sony as well as third sector groups like Human Rights Watch, UNICEF, and our partners in Cambridge, the Leverhulme Centre for the Future of Intelligence. The Partnership on AI is organised around a set of thematic pillars, including Fair, transparent, and accountable AI, and AI and social good; FHI is will focus its work on the first of these pillars: Safety-critical AI. Where AI tools are used to supplement or replace human decision-making, we must be sure that they are safe, trustworthy, and aligned with the ethics and preferences of people who are influenced by their actions. Professor Nick Bostrom, director of FHI, said in response to the news, "We're delighted to be joining the Partnership on AI, and to be expanding our industry and nonprofit collaborations on AI safety."

We are at an inflection point in the evolution of artificial intelligence. For decades, AI has been incubating in research labs and at the same time capturing popular imagination with science fiction portrayals of AI. The reality is that thanks to a convergence of increasing compute power, big data and algorithmic advances, AI is becoming mainstream and finding practical applications in nearly every facet of our personal lives. Facebook identifies which friends to tag in photos, algorithms are improving medical diagnosis and saving lives, and GPS-based apps are predicting traffic patterns to optimize driving routes. The AI revolution is also taking hold in our business lives.

I'm an Airbnb host and while reviewing my most recent Airbnb guest, James from Australia, I saw the section again that said "Anything you would like to tell Airbnb? It'll be private…" and it got me thinking about what happens behind the scenes at any company that receives customer feedback. For me, I've only used that section for extremes. The 90 and the 10. "BEST GUESTS EVER" and "He made me feel icky". I mean why bother the great folks at Airbnb to say "They were great. The stars are there for that.

Swinburne University of Technology and the University of Melbourne are partnering with CA Technologies for a three year project to advance service virtualisation. Backed too by an Australian Research Council grant, the researchers will seek to find a method of using machine learning to automatically derive a service virtualisation model. "By using machine learning, development teams can write software without needing all the other systems within their environment. This will ultimately increase software development speed and reliability," CA explained in a statement. They will also look at ways of modelling a whole network of services, which takes into account their interdependencies.

Randomized dimensionality reduction techniques, such as random projection (RP) [7, 15] and Ho's random subspace method (RS) [12] are popular approaches for data compression, with many empirical studies showing the utility of both for machine learning and data mining tasks in practice [26, 11, 21, 19, 18, 27]. For RP a key theoretical motivation behind their use is the Johnson-Lindenstrauss lemma (JLL), the usual constructive proof of which also implies an algorithm with high-probability geometry preservation guarantees for projected data. However RP is costly to apply to large or high-dimensional datasets since it requires a matrix-matrix multiplication to implement the projection, and furthermore the projected features may be hard to interpret. On the other hand RS is a particularly appealing approach for dimensionality reduction because it involves simply selecting a subset of data feature indices randomly without replacement, and so does not require a matrix-matrix multiplication to implement the projection and it retains (a subset of) the original features. RS is therefore computationally far more efficient in practice, and more interpretable than RP, but there is little theory to explain its effectiveness. Focusing on this latter problem, here we prove data-dependent norm-preservation guarantees for data projected onto a random subset of the data features.

Principal Component Analysis (PCA) is a very successful dimensionality reduction technique, widely used in predictive modeling. A key factor in its widespread use in this domain is the fact that the projection of a dataset onto its first $K$ principal components minimizes the sum of squared errors between the original data and the projected data over all possible rank $K$ projections. Thus, PCA provides optimal low-rank representations of data for least-squares linear regression under standard modeling assumptions. On the other hand, when the loss function for a prediction problem is not the least-squares error, PCA is typically a heuristic choice of dimensionality reduction -- in particular for classification problems under the zero-one loss. In this paper we target classification problems by proposing a straightforward alternative to PCA that aims to minimize the difference in margin distribution between the original and the projected data. Extensive experiments show that our simple approach typically outperforms PCA on any particular dataset, in terms of classification error, though this difference is not always statistically significant, and despite being a filter method is frequently competitive with Partial Least Squares (PLS) and Lasso on a wide range of datasets.

Artificial intelligence is a booming business in 2017, but one that also comes with significant baggage in the form of public misunderstanding, potential job losses, and fear. Last fall, A.I. competitors Amazon, Microsoft, Facebook, IBM, and Google banded together to form the Partnership on AI to Benefit People and Society, an industry-led attempt to get ahead of the many social, ethical, and economic issues presented by the advent of technology with increasingly human-like capabilities. Apple joined the group as another founding member earlier this year. On Tuesday, the Partnership on AI (PAI) announced nearly two dozen new members, including more of the tech industry's biggest names--Intel, eBay, Salesforce, and SAP among them--and many of the world's foremost A.I. research institutions, such as the Seattle-based Allen Institute for Artificial Intelligence. Also joining are nonprofits focused on digital privacy, human rights, and freedom.

New research from Rocket Fuel on consumer perceptions of artificial intelligence (AI) shows younger generations welcome suggestions and predictions for products and services. Millennials are receptive to the use of AI in advertising, according to'Consumer Perceptions of AI' survey results from predictive marketing company Rocket Fuel. The survey found that over two-thirds of millennials see the benefits of brands using AI to help inform and direct their buying decisions. "Our research provides a snapshot of consumer attitudes towards AI and the fast-changing digital landscape," says Mailee Creacy, country manager at Rocket Fuel ANZ. "This is especially true for Millennials who are aware of the value exchange that takes place – they provide brands with personal data and expect to see their information used in ways that provides them with tangible benefits. Being able to engage with Millennials in a personalised way is the next frontier as brands seek to maintain and increase relevancy in the digital age," she says.

We look forward to collaborating with other industry-leading Partnership on AI members to address the challenges and opportunities within the AI field including companies, nonprofits and institutions and with founding members Apple, Amazon, Facebook, Google / DeepMind, IBM and Microsoft; existing Partners AAAI, ACLU, OpenAI; and new Partners: AI Forum of New Zealand (AIFNZ), Allen Institute for Artificial Intelligence (AI2), Centre for Democracy & Tech (CDT), Centre for Internet and Society, India (CIS), Cogitai, Data & Society Research Institute (D&S), Digital Asia Hub, eBay, Electronic Frontier Foundation (EFF), Future of Humanity Institute (FHI), Future of Privacy Forum (FPF), Human Rights Watch (HRW), Intel, Leverhulme Centre for the Future of Intelligence (CFI), McKinsey & Company, SAP, Salesforce, Sony, UNICEF, Upturn, XPRIZE Foundation and Zalando.