It's already possible to take "snapshots" of the BIM as it evolves over time, using digital authoring software. When viewed as stand-alone files, these snapshots may not provide much information about workflow. However, when the files are compared automatically and continuously over time, they increase a project manager's understanding of the workflow involved, thus adding value. Kouhestani and Nik-Bakht saw an opportunity to improve BIM capability by automating an archiving and analyzing process. "We created an algorithm to make'event logs' that track changes in consecutive files," says Kouhestani. "Once we created the event logs, we used them as input for process mining."

As artificial intelligence has become a growing force in business, today's top AI companies are leaders in this emerging technology. Often leveraging cloud computing, AI companies mix and match myriad technologies. Foremost among these is machine learning, but today's AI leading firms tech ranging from predictive analytics to business intelligence to data warehouse tools to deep learning. Entire industries are being reshaped by AI. RPA companies have completely shifted their platforms. AI in healthcare is changing patient care in numerous – and major – ways. AI companies are attracting massive investment from venture capitalist firms and giant firms like Microsoft and Google. Academic AI research is growing, as are AI job openings across a multitude of industries. All of this is documented in the AI Index, produced by Stanford University's Human-Centered AI Institute. Consulting giant Accenture believes AI has the potential to boost rates of profitability by an average of 38 percentage points and could lead to an economic boost of $14 trillion in additional gross value added (GVA) by 2035. In truth, artificial intelligence holds not just possibilities, but a plethora of risks. "It will have a huge economic impact but also change society, and it's hard to make strong predictions, but clearly job markets will be affected," said Yoshua Bengio, a professor at the University of Montreal, and head of the Montreal Institute for Learning Algorithms. To keep up with the AI market, we have updated our list of top AI companies playing a key role in shaping the future of AI. We feature artificial intelligence companies that are commercially successful as well as those that have invested significantly in artificial intelligence. AI companies in the years ahead are forecast to see exponential growth in deep learning, machine learning and natural language processing.

Recent work on few-shot learning [1,2,3] shows that simply fine-tuning a well-trained feature extractor outperforms prior meta-learning methods. I think this phenomenon is not limited to the few-shot classification/detection but is a general problem of machine learning: that most (if not all) problems can be formulated as the two-step (input- feature, feature- output) approach. I feel that a pretty large amount of the "method" papers are stuck in the local minima or revisiting the known observation to the slightly different settings. In my understanding, the machine learning problems essentially break down into the 1) representation learning (input- feature) and 2) prediction (feature- output) problems, hence any proposed "method" paper should outperform the combination of the SOTA feature extractor and the SOTA prediction network (e.g., MoCo/SimCLR cosine classifier for few-shot classification). However, frustratingly few papers provide such a good ablation study.

Similarly, in the paper "Towards Debiasing Fact Verification Models," a related group of researchers identified a problem in the FEVER database that caused the unexpected result that claim-only classifiers performed competitively against evidence-aware classifiers. The authors identify strong cues for predicting labels solely based on the claims made. The FEVER dataset was constructed using crowdsourcing, allowing human bias to introduce human artifacts into the data. For example, the presence of negatively phrased statements was highly predictive of text flagged as fake news, yet this was not representative of fake news in the real world. Modern machine learning algorithms quickly identified these artifacts of human bias.

There's a lot of buzz around Artificial Intelligence and Machine Learning, as supply chain leaders look for the killer app within their organizations.

Artificial intelligence is one thing, but artificial creativity is far more difficult to achieve.

According to a report in the UK's Charged Retail, Alibaba is offering up its cloud based artificial intelligence technology to medical personnel around …

Using machine learning methods on EPs shows promising predictive performance.

We infer and generate three-dimensional (3D) scene information from a single input image and without supervision. This problem is under-explored, with most prior work relying on supervision from, e.g., 3D ground-truth, multiple images of a scene, image silhouettes or key-points. We propose Pix2Shape, an approach to solve this problem with four components: (i) an encoder that infers the latent 3D representation from an image, (ii) a decoder that generates an explicit 2.5D surfel-based reconstruction of a scene from the latent code (iii) a differentiable renderer that synthesizes a 2D image from the surfel representation, and (iv) a critic network trained to discriminate between images generated by the decoder-renderer and those from a training distribution. Pix2Shape can generate complex 3D scenes that scale with the view-dependent on-screen resolution, unlike representations that capture world-space resolution, i.e., voxels or meshes. We show that Pix2Shape learns a consistent scene representation in its encoded latent space and that the decoder can then be applied to this latent representation in order to synthesize the scene from a novel viewpoint. We evaluate Pix2Shape with experiments on the ShapeNet dataset as well as on a novel benchmark we developed, called 3D-IQTT, to evaluate models based on their ability to enable 3d spatial reasoning. Qualitative and quantitative evaluation demonstrate Pix2Shape's ability to solve scene reconstruction, generation, and understanding tasks.

The head of the longest-established agency personnel consultancy in the UK says caution should be exercised over the use of Artificial Intelligence in recruitment processes. Property Personnel managing director Anthony Hesse thinks that estate agency should never forget that it is primarily a people business, and technology would have stark weaknesses identifying the right person for the job. "At its heart, estate agency is and has always been about people. We may deal in property, but it is people who are doing the buying and selling. And we are at risk of missing out on some of our best potential recruits if we forget that" explains Hesse. "Artificial Intelligence is all very well; but I seriously doubt that technology will be able to identify some of the subtle factors which only come to light in a one on one relationship between two human beings.