Use of AI can be critical when developing systems to support social good, with some inspiring examples using Splunk in healthcare and higher education organisations. According to our State of Dark Data report, however, only 15% of organisations admit they are utilising AI solutions today due to lack of skills. So how can we help organisations unlock the potential of AI?
Artificial intelligence refers to the machine's ability to learn and think. Given that it sort of mimics how humans think and reason, AI's application is virtually endless. AI reduces human error, do a task that is risky for humans to do, help humans solve complex, and so much more. With the emergence of artificial intelligence, concerns about data privacy have been brought into the light. Artificial intelligence relies on our personal information to learn.
A growing number of car companies have made their autonomous vehicle (AV) datasets public in recent years. Daimler fueled the trend by making its Cityscapes dataset freely available in 2016. Baidu and Aptiv respectively shared the ApolloScapes and nuScenes datasets in 2018. Lyft, Waymo and Argo followed suit in 2019. And more recently, automotive juggernauts Ford and Audi released datasets from their AV research programs to the public.
How annoying is it when you get a telemarketing call from a random phone number? Even if you block it, it won’t make a difference because the next one will be from a brand new number. Cyber attackers employ the same dirty tricks. Using domain generated algorithms (DGAs), malware creators change the source of their command and control infrastructure, evading detection and frustrating security analysts trying to block their activity.
Most IT and business leaders know that despite the economic and human disruption of the COVID-19 pandemic, digital transformation will ultimately speed up, not slow down. The immediate challenges of the pandemic have led companies to find innovative ways to get things done, relying on data-driven decisions and technologies.
If you are a software engineer, there's a good chance that deep learning will inevitably become part of your job in the future. Even if you're not building the models that directly use CNNs, you might have to collaborate with data scientists or help business partners better understand what is going on under the hood. In this article, Julie Kent dives into the world of convolutional neural networks and explains it all in a not-so-scary way.
We're excited to announce that the Splunk Machine Learning Toolkit (MLTK) version 5.2 is available for download today on Splunkbase! Earlier this month, I discussed how the release of version 5.2 will make machine learning more accessible to more users. Splunk’s MLTK lets our customers apply machine learning to the data they're already capturing in Splunk, develop models, and operationalize these algorithms to glean new insights and make more informed decisions.
For teams that deal with machine learning (ML), there comes a point in time where training a model on a single machine becomes untenable. This is often followed by the sudden realization that there is more to machine learning than simply model training. There are a myriad of activities that have to happen before, during and after model training. This is especially true for teams that want to productionize their ML models.
In sync with the upcoming release of Splunk’s Machine Learning Toolkit 5.2, we have launched a new release of the Deep Learning Toolkit for Splunk (DLTK) along with a brand new “golden” container image. This includes a few new and exciting algorithm examples which I will cover in part 2 of this blog post series.
