Human matting is an extremely interesting task where the goal is to find any human in a picture and remove the background from it. It is really hard to achieve due to the complexity of the task, having to find the person or people with the perfect contour. … The MODNet background removal technique can extract a person from a single input image, without the need for a green screen in real-time! Read More
Monthly Archives: January 2021
How explainable artificial intelligence can help humans innovate
The field of artificial intelligence (AI) has created computers that can drive cars, synthesize chemical compounds, fold proteins and detect high-energy particles at a superhuman level.
However, these AI algorithms cannot explain the thought processes behind their decisions. A computer that masters protein folding and also tells researchers more about the rules of biology is much more useful than a computer that folds proteins without explanation.
Therefore, AI researchers like me are now turning our efforts toward developing AI algorithms that can explain themselves in a manner that humans can understand. Read More
What Happens When AI Has An Overactive Imagination?
Defining enterprise AI: From ETL to modern AI infrastructure
The promise of enterprise AI is built on old ETL technologies, and it relies on an AI infrastructure effectively integrating and processing loads of data. … Effective data integration is critical for enterprise AI. Data is the lifeblood of enterprise AI applications and its extraction and storage must be optimized. Read More
Machine Learning Metadata (MLMD) : A Library To Track Full Lineage Of Machine Learning Workflow
Version control is used to keep track of modifications made in a software code. Similarly, when building machine learning (ML) systems, it is essential to track things, such as the datasets used to train the model, the hyperparameters and pipeline used, the version of tensorflow used to create the model, and many more.
ML artifacts’ history and lineage are very complicated than a simple, linear log. Git can be used to track the code to one extent, but we need something to track your models, datasets, and more. The complexity of ML code and artifacts like models, datasets, and much more requires a similar approach.
Therefore, the researchers have introduced Machine Learning Metadata (MLMD), a standalone library to track one’s entire ML workflow’s full lineage from data ingestion, data preprocessing, validation, training, evaluation, deployment, etc. MLMD also comes integrated with TensorFlow Extended. Read More
Discrete Latent Space World Models for Reinforcement Learning
Sample efficiency remains a fundamental issue of reinforcement learning. Model-based algorithms try to make better use of data by simulating the environment with a model. We propose a new neural network architecture for world models based on a vector quantized-variational autoencoder (VQ-VAE) to encode observations and a convolutional LSTM to predict the next embedding indices. A model-free PPO agent is trained purely on simulated experience from the world model. We adopt the setup introduced by Kaiser et al. (2020), which only allows100Kinteractionswith the real environment, and show that we reach better performance than their SimPLe algorithm in five out of six randomly selected Atari environments, while our model is significantly smaller. Read More
IMS unveils driverless Indy car that will race in October Indy Autonomous Challenge
It’s a car that, on the surface, will be familiar to mainstream IndyCar fans but a version that may have Tony Hulman doing a double-take from the grave.
The sleek, black Dallara IL-15 unveiled Monday to run in a 20-lap race later this year is an Indy Lights car in almost every way. Staring at the cockpit, you’d probably forget about the missing protective halo device that Lights drivers will run with in 2021, but look closer … and there’s no cockpit at all. Read More
Maintaining the Intelligence Edge
The U.S. Intelligence Community (IC) stands at the dawn of a new era of technological innovation and transformation unprecedented in its history. Driven by artificial intelligence (AI) and associated emerging technologies, including cloud computing, advanced sensors, and big data analytics, the approaching “AI era” will transform both the nature of the global threats the IC is responsible for assessing and the IC’s ability to accurately detect and assess them. Through all of this, the core mission of the IC will remain unchanged: to understand what is happening in the world, to deliver timely, accurate, and insightful analysis of those threats and developments to U.S. policymakers, and to provide U.S. leaders decision making advantage over competitors. What will change is the IC’s ability to fulfill this mission if it does not adapt to the new AI era. Read More
The very real fear of artificial intelligence
My job it seems is safe, for now. Stringing together reasonably coherent sentences may not be a particularly testing task, but at least the threat from artificial intelligence (AI), the newest jobs-killer in town, isn’t as potent as I feared.
In a recent paper published in the Findings of Empirical Methods in Natural Language Processing (EMNLP), Assistant Professor Xiang Ren and PhD student Yuchen Lin at the University of Southern California found that despite significant advances AI still doesn’t have the common sense needed to generate plausible sentences. As Lin explained to Science Daily, “Current machine text-generation models can write an article that may be convincing to many humans, but they’re basically mimicking what they have seen in the training phase”. Where these models failed was in describing everyday scenarios. Given the words dog, frisbee, throw, and catch, one model came up with the sentence “Two dogs are throwing frisbees at each other.” Nothing wrong in that except that it misses what we know through common sense, viz that a dog can’t throw frisbees. Read More
Top 25 Machine Learning Startups To Watch In 2021 Based On Crunchbase
The 25 startups to watch:
- Augury.
- Alation.
- Algorithmia.
- Avora.
- Boast.ai.
- ClosedLoop.ai.
- Cognino AI.
- Databand.
- DataVisor.
- Exceed.ai.
- Indico.
- JAXJOX.
- LeadGenius.
- Netra.
- Particle.
- ResurfaceLabs.
- RideVision.
- Savvie.
- SECURITI.ai.
- SkyHive.
- Stravito.
- Uniphore.
- Vertia.ai.
- V7.
- Zest.ai.
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