In applications of diffusion models, controllable generation is of practical significance, but is also challenging. Current methods for controllable generation primarily focus on modifying the score function of diffusion models, while Mean Reverting (MR) Diffusion directly modifies the structure of the stochastic differential equation (SDE), making the incorporation of image conditions simpler and more natural. However, current training-free fast samplers are not directly applicable to MR Diffusion. And thus MR Diffusion requires hundreds of NFEs (number of function evaluations) to obtain high-quality samples. In this paper, we propose a new algorithm named MRS (MR Sampler) to reduce the sampling NFEs of MR Diffusion. We solve the reverse-time SDE and the probability flow ordinary differential equation (PF-ODE) associated with MR Diffusion, and derive semi-analytical solutions. The solutions consist of an analytical function and an integral parameterized by a neural network. Based on this solution, we can generate high-quality samples in fewer steps. Our approach does not require training and supports all mainstream parameterizations, including noise prediction, data prediction and velocity prediction. Extensive experiments demonstrate that MR Sampler maintains high sampling quality with a speedup of 10 to 20 times across ten different image restoration tasks. Our algorithm accelerates the sampling procedure of MR Diffusion, making it more practical in controllable generation.

Is your machine learning project being sped up by your data scientist team? Or would you rather spend more time experimenting with various machine learning techniques and less time maintaining and debugging code? Or are you thinking about incorporating machine learning into your business operations but don't have the funds to hire a swarm of data scientists and engineers? Then, you may be ready to explore low code machine learning systems for your next project. Traditional machine learning model creation and deployment are complicated, time-consuming, and costly, necessitating the hiring of hard-to-find ML experts.

The Geoscience and Remote Sensing Society -- one of the well-known communities to learn and contribute to Geospatial Science has sponsored the DeepGlobe machine vision challenge in 2018, which includes the deep analysis of satellite images of Earth. As part of this, I picked up the problem of Road Extraction as roads have always been a crucial part in various aspects be it transportation, traffic management, city planning, road monitoring, GPS navigation, etc. The challenges of DeepGlobe are purely research-based and focus on the real problems. This is something we need to predict. The one caveat here is that we need to have an equal number of classes to consider this metric.

However, we only ever hear of using AI to understand big data sets. This is because small data sets are usually easily understood by people, and applying AI to analyze and interpret them isn't necessary. These days, many businesses and manufacturers integrate AI into the production line, slowly creating data scarcity. And unlike big companies, many setups cannot collect massive training sets due to risk, time, and budget limitations. As most companies don't know how to benefit from AI application on small data sets correctly, they blindly apply it to make future predictions based on previous files.

Every day, businesses of all sizes, sectors and locations are learning more about the data they hold and the opportunities it can unlock. As organisations once again look ahead to a new year, what are the major data trends the tech industry can expect in 2022? The rise of application automation Dan Sommer, Senior Director, Global Market Intelligence Lead at Qlik, believes 2022 will be the year application automation will trigger actions. He explains, "the API economy opens up entirely new ways for businesses, partners, customers, and even competitors to unite for joint initiatives while reducing the relevancy of buy-versus-build. With an opportunity to assemble and orchestrate, application automation is a strongly emerging area that removes the need to code these integrations, making the opportunity much more accessible to a wider variety of actors."

Siummary: In 2017, AI and analytics M&A activity will accelerate, data lakes will finally become useful, and data monetization strategies will mature. These are some of the predictions Ramon Chen, CMO of data management innovator, Reltio, has for the coming year. Major players as diverse as Google, Apple, Salesforce and Microsoft to AOL, Twitter and Amazon drove the acquisition trend this year. Due to the short operating history of most of the startups being acquired, these moves are as much about acquiring the limited number of AI experts on the planet as the value of what each company has produced to date. The battle for AI enterprise mindshare has clearly been drawn between IBM Watson, Salesforce Einstein, and Oracle's Adaptive Intelligent Applications.

Major players as diverse as Google, Apple, Salesforce and Microsoft to AOL, Twitter and Amazon drove the acquisition trend this year. Due to the short operating history of most of the startups being acquired, these moves are as much about acquiring the limited number of AI experts on the planet as the value of what each company has produced to date. The battle for AI enterprise mindshare has clearly been drawn between IBM Watson, Salesforce Einstein, and Oracle's Adaptive Intelligent Applications. What's well understood is that AI needs a consistent foundation of reliable data upon which to operate. With a limited number of startups offering these integrated capabilities, the quest for relevant insights and ultimately recommended actions that can help with predictive and more efficient forecasting and decision-making will lead to even more aggressive M&A activity in 2017.