Artificial intelligence is being used in IVF to select embryos with the highest chance of resulting in a successful pregnancy. The AI algorithm, called Ivy, analyses time-lapse videos of embryos as they are incubated after being fertilised, and identifies which ones have the highest likelihood of successful development. It was developed by Harrison.ai, Women who undergo IVF using Ivy are informed about the algorithm and consent to its use.

"Roman" and I haven't exchanged words for about 10 seconds, but you wouldn't know it from the look on his face. This artificially intelligent avatar, a product of New Zealand-based Soul Machines, is supposed to offer human-like interaction by simulating the way our brains handle conversation. Roman can interpret facial expressions, generate expressions of his own, and converse on a variety of topics--making him what Soul Machines calls a "digital hero." Right now, though, Roman is glitching, stuck in a routine of blinking, furrowing his eyebrows, and twisting his mouth into a polite half-smile. Moments ago, he'd asked me what music I would beam into deep space if I were in charge of NASA, but my answer--the seminal modern jazz fusion tune "Lingus" by Snarky Puppy, of course--seems to have caught him off guard.

Telecoms have evolved quite well with the times and are likely to continue to do so. The coming years will present the biggest technology trends, and telecoms will have to navigate their benefits and challenges in 2020 and beyond. Whichever side of the tracks a telecommunications business emerges as a result of upcoming technology trends, good or bad, you can be certain that a telco revolution is at hand. From 5G to AI and beyond, telcos will be met with a new world that creates more industries to service. This new world will open doors to markets that once had no need for telecoms, creating many new opportunities.

Computers have guided us to the moon and back but can't help us with us with the biggest decisions we face today. Should Donald Trump be impeached? Should Britain leave the EU? Should Australia stop exporting fossil fuels? Questions like these do not have yes or no answers, however tempting it is to think otherwise.

Artificial Intelligence (AI) has the power to completely transform the way we do things, personally and in business. We have seen many cases of small businesses that have used AI to deliver exceptional services and products that go on to outshine the competition, or examples where businesses have completely revolutionised an internal process. In October, we saw tech startup Devika, work with their client Equalution (health-tech app) to deliver a better service to users. Implementing AI gave the body transformation platform customised health plans for users, based on their mindsets. In the long run it made the service provided much more effective and affordable. With all of these discussions and stories, our aim is to remove some of the "unknown" around AI. Today we're asking people in business "What does AI mean for your organisation?" Keep up to date with our stories LinkedIn, Twitter, Facebook and Instagram. The ability for artificial intelligence (AI) to learn and process massive amount of data will continue to grow as we make greater strides in computer processing power and deep learning algorithms.

If you have tried to understand the maths behind machine learning, including deep learning, you would have come across topics from Information Theory – Entropy, Cross Entropy, KL Divergence, etc. The concepts from information theory is ever prevalent in the realm of machine learning, right from the splitting criteria of a Decision Tree to loss functions in Generative Adversarial Networks. If you are a beginner in Machine Learning, you might not have made an effort to go deep and understand the mathematics behind the ".fit()", but as you mature and stumble across more and more complex problems, it becomes essential to understand the math or at least the intuition behind the maths to effectively apply the right technique at the right place. When I was starting out, I was also guilty of the same. I'll see "Cross Categorical Entropy" as a loss function in a Neural Network and I take it for granted – that it is some magical loss function that works with multi-class labels. I'll see "entropy" as one of the splitting criterion in Decision Trees and I just experiment with it without understanding what it is. But as I matured, I decided to spend more time in understanding the basics and it helped me immensely in getting my intuitions right. This also helped in understanding the different ways the popular Deep Learning Frameworks, PyTorch and Tensorflow, have implemented the different loss functions and decide when to use what. This blog is me summarising my understanding of the underlying concepts of Information Theory and how the implementations differ across the different Deep Learning Frameworks.

If you have tried to understand the maths behind machine learning, including deep learning, you would have come across topics from Information Theory – Entropy, Cross Entropy, KL Divergence, etc. The concepts from information theory is ever prevalent in the realm of machine learning, right from the splitting criteria of a Decision Tree to loss functions in Generative Adversarial Networks. If you are a beginner in Machine Learning, you might not have made an effort to go deep and understand the mathematics behind the ".fit()", but as you mature and stumble across more and more complex problems, it becomes essential to understand the math or at least the intuition behind the maths to effectively apply the right technique at the right place. When I was starting out, I was also guilty of the same. I'll see "Cross Categorical Entropy" as a loss function in a Neural Network and I take it for granted – that it is some magical loss function that works with multi-class labels. I'll see "entropy" as one of the splitting criterion in Decision Trees and I just experiment with it without understanding what it is. But as I matured, I decided to spend more time in understanding the basics and it helped me immensely in getting my intuitions right. This also helped in understanding the different ways the popular Deep Learning Frameworks, PyTorch and Tensorflow, have implemented the different loss functions and decide when to use what. This blog is me summarising my understanding of the underlying concepts of Information Theory and how the implementations differ across the different Deep Learning Frameworks.

Markov Logic Networks (MLNs), which elegantly combine logic rules and probabilistic graphical models, can be used to address many knowledge graph problems. However, inference in MLN is computationally intensive, making the industrial-scale application of MLN very difficult. In recent years, graph neural networks (GNNs) have emerged as efficient and effective tools for large-scale graph problems. Nevertheless, GNNs do not explicitly incorporate prior logic rules into the models, and may require many labeled examples for a target task. In this paper, we explore the combination of MLNs and GNNs, and use graph neural networks for variational inference in MLN. We propose a GNN variant, named ExpressGNN, which strikes a nice balance between the representation power and the simplicity of the model. Our extensive experiments on several benchmark datasets demonstrate that ExpressGNN leads to effective and efficient probabilistic logic reasoning.

Different metrics have been proposed to compare Abstract Meaning Representation (AMR) graphs. The canonical Smatch metric (Cai and Knight, 2013) aligns variables from one graph to another and compares the matching triples. The recently released SemBleu metric (Song and Gildea, 2019) is based on the machine-translation metric Bleu (Papineni et al., 2002), increasing computational efficiency by ablating a variable-alignment step and aiming at capturing more global graph properties. Our aims are threefold: i) we establish criteria that allow us to perform a principled comparison between metrics of symbolic meaning representations like AMR; ii) we undertake a thorough analysis of Smatch and SemBleu where we show that the latter exhibits some undesirable properties. E.g., it violates the identity of indiscernibles rule and introduces biases that are hard to control; iii) we propose a novel metric S2match that is more benevolent to only very slight meaning deviations and targets the fulfilment of all established criteria. We assess its suitability and show its advantages over Smatch and SemBleu.

This paper discusses the problem of causal query in observational data with hidden variables, with the aim of seeking the change of an outcome when "manipulating" a variable while given a set of plausible confounding variables which affect the manipulated variable and the outcome. Such an "experiment on data" to estimate the causal effect of the manipulated variable is useful for validating an experiment design using historical data or for exploring con-founders when studying a new relationship. However, existing data-driven methods for causal effect estimation face some major challenges, including poor scalability with high dimensional data, low estimation accuracy due to heuristics used by the global causal structure learning algorithms, and the assumption of causal sufficiency when hidden variables are inevitable in data. In this paper, we develop theorems for using local search to find a superset of the adjustment (or confounding) variables for causal effect estimation from observational data under a realistic pretreatment assumption. The theorems ensure that the unbiased estimate of causal effect is obtained in the set of causal effects estimated by the superset of adjustment variables. Based on the developed theorems, we propose a data-driven algorithm for causal query. Experiments show that the proposed algorithm is faster and produces better causal effect estimation than an existing data-driven causal effect estimation method with hidden variables. The causal effects estimated by the algorithm are as good as those by the state-of-the-art methods using domain knowledge.