UWA Living Lab Kicks Off

‘Innovators find it difficult to access sites to prove up results and miners are averse to trialling/introducing innovation without proven results’ -METS Ignited 2017

The UWA Living Lab project aims to help bridge this gap.

The Living Lab project funding was officially announced recently at the METS Ignited event at the CORE Innovation Hub. The launch funding is being supplied through the METS Ignited Collaboration Project Fund, from the BHP Fellowship for Engineering for Remote Operations. This is a partnership with CORE Innovation Hub and the UWA Facilities Management group.

More details are available on the UWA website.

FLOC – Machine Learning meets Formal Methods workshop, Oxford

This one-day workshop 13 July 2018 brought together the Machine Learning and Formal Methods communities. Here is a summary of some take-aways. Highlights were the talks by Pushmeet Kohli from Google DeepMind UK, Alison Lowndes from NVIDIA, and Adnan Darwiche UCLA. Melinda Hodkiewicz (SHL)and Ashwin D’Cruz (ex-SHL now working for Calipsa in London) attended. https://www.floc2018.org/summit-on-machine-learning/.

Pushmeet Kohli (Google DeepMind): Challenges for AI are to ensure it is a) robust to adversaries, b) generalises well to variations in the real world, c) it is fair, d) it is compliant with regulations. When talking about ‘fairness’, he split the discussion into the “What do we mean by fair?” and “How to make AI fair”. He did not answer the “what” question. Instead saying that “this needed to be set in regulations”.   As far as the “how”, Kohli suggested three steps 1) rigorous testing, 2) developing robust AI, and 3) verifying AI systems. A significant challenge for AI is that test set evaluation approaches commonly used in ML are inappropriate for 1) adversarial environments and 2) safety critical domains. In safety critical domains loss functions are unbounded. Also you would need a lot of samples of bad events for test set evaluation, and we can’t afford to do this. He then gave some examples of work his team at Google is doing (see his recent ICML, ICLR and NIPS papers) and left us with the idea that we might need a new language for AI which has suitable inductive bias (set of assumptions that the learning algorithm uses to predict outputs given inputs it has not encountered) and with the right expressiveness to describe what’s going on.

Comment: Melinda asked the room if there was anyone at the workshop working for legislators or regulators; there was not. It is not clear how legislators are going to develop workable regulations and regulators have the capacity to assess practice against these regulations without a good understanding of the issues being discussed at these types of events.

Alison Lowndes (NVIDIA): NVDIA has developed massive simulation platforms and Alison talked about their work on Jetson Xavier, an AI computer for autonomous machines delivering GPU workstation performance in a single embedded module https://developer.nvidia.com/jetson-xavier-devkit. She observed that while Reinforcement Learning was highly fashionable (80 papers/day published on arXiv), it is not commercial yet. Classical ML (SVM, MLP, GBDT) are still very relevant and widely used. Convolution neural networks are widely used. She expressed concern about the “common person’s voice in the room” and suggested that philosophy and psychology will become more important. Finally  relevant for the SHL and Makers she said that educational institutions can get a free DevKit from NVDIA https://developer.nvidia.com/teaching-kits

Andre Platzer (Carnegie Mellon) talked on ‘safe’ reinforcement learning via formal methods with a focus on safety critical systems. How do you demonstrate that the algorithm is “provably safe”? He talked about the need to 1) learn safety, 2) learn a safety policy, and 3) verify and about the issue of “what if the model is incorrect”? As far as safety policy, this appears to be based on the idea of “have we seen this output before and it was ok, then it should be safe, given the same context”, how do we know to trust this and how do we know if the context has changed?   Andre runs the Logical Systems Labs http://www.ls.cs.cmu.edu/ and has a text book on Logical Foundations of Cyber-Physical Systems.

Sumit Gulwani from Microsoft talked abotu their PROSE kit https://microsoft.github.io/prose/. This is about programming by examples – the automatic generation of programs from input-output examples. It can build programs in various languages such as Python/R/C# and some of its functionality is baked in Excel. Some scripts that are now tedious to write can be automated.

Marta Kwiatkowska (Oxford) talked about her team’s work on ‘safety verification for deep learning networks with proven guarantees’. She made the point that while there are an infinite set of possible outcomes we only measure ‘accuracy’ on a finite data set.  She demonstrated how deep learning networks are unstable to adversarial perturbations using image processing of a car sign (plenty of papers on this on arxiv) and asked ‘how can we verify that such behaviour cannot occur’.  Marta’s group at Oxford is involved in modelling and automated verification techniques for software systems. One of the current projects in her group is safety and trust for mobile autonomous robots.   http://www.cs.ox.ac.uk/people/marta.kwiatkowska/research.html.

Adnan Darwiche from UCLA presented on “what just happened in AI”.  It draws on his recent paper “Human-Level Intelligence or Animal-Like Abilities” https://arxiv.org/abs/1707.04327.  He made a number of interesting observations 1) Lots on new AI applications, 2) AI has been around >50 years, 3) The AI curriculum is almost unchanged. Essentially every behaviour can be captured to some extent by a function. We are now building bigger functions and we have more data.  A deep learning NN is a function and architecting the structure of a NN is function engineering.  Next he moved onto how our perception of value has changed. Model-based approaches try and understand a system whereas ML models translate without insight. We have realised in many cases (e.g. social media) you don’t need the understanding to be useful. The ease at which we can get results that may be the same or only slightly better than what we can do with model based methods is very attractive.  However he warned about the growing gap between hype and reality and reminded the audience of a period he described as the “AI winter”. He warned about a lost generation of AI researchers who are well versed in NN models but not in Logic, the need to understand the limitations of function-based approaches and to characterise deep learning functions in a scientifically precise manner. It’s worth reading his paper from the link above for a fully discussion of his concerns.



Connecting to an Access Database using PHP

So recently I started using Microsoft (MS) Access which is the Database Management System (DBMS) in the MS Office Suite. While it presented a very interactive and pleasant graphical user interface (GUI), I started running into problems when I wanted to access the data online. While it was straightforward enough to do so with SharePoint, my team and I didn’t want to go down that road as we wanted the database to integrate well with other parts of web development and not be constrained to Sharepoint. That’s when a colleague of mine, Joanna Sirkoska landed upon ADOdb which allowed us to use PHP to connect to an Access Database.

In this post, I’ll briefly go through how you can connect to your chosen Access database and display the information within it using queries. The script to do so is quite straightforward and you can find it at this link. With this script you’ll either need to modify it to use your database or use the same database that I did which I have provided here.


I’ll run through some of the code here which should hopefully make things clearer if the comments in the script are not clear enough.


First of all you need to make sure you have the following line before trying anything else:


To do so, check out the ADOdb link at the start and download the package. Then decide where you want to put it and make sure you change the line to reflect your path choice.


Then we need to connect to the chosen database:

$conn = new COM(“ADODB.Connection”) or die(“Cannot start ADO”);

$connStr = “Driver={Microsoft Access Driver (*.mdb, *.accdb)};Dbq=C:\Apache24\htdocs\DataCodes.accdb”;


The first line creates an instance of the ADO connection object. You’ll need to edit the second line to match the database you choose to use. Finally the last line opens a connection to this database.


Next we have to execute a query:

$query = “SELECT * FROM tblEquipClass”;

$rs= $conn->execute($query);

I’ve chosen a fairly simple query which which get all the information contained within the table ‘tblEquipClass’. Then I execute the query storing the results within the variable $rs.

An easy way to view the information contained within $rs is printing out the information within it:

while (!$rs->EOF) {
print $rs->fields[2].'<BR>’;

This while loop allows you to iterate through each line of data from the table. We then print only information from the third column out.

Finally we can close the connections made previously but that is optional.

There you have it! Now you can design your tables using the nice GUI MS Access provides but allow greater connection to your database using PHP. Leave a comment if you found this useful or have any queries.