As artificial intelligence(AI) and deep learning evolves to become more mainstream in software solutions, they are going to carry with them other disciplines in the technology space. Security is one of those areas that needs to quickly evolve to keep up with the advancements in deep learning technology. While we typically think about deep learning in a positive context with algorithms trying to improve the intelligence of the solution, deep learning models can also be used to orchestrates security sophisticated attacks. Even more interesting is the fact that deep learning models can be used to compromise the safety of other intelligent model.
The idea of deep neural networks attacking other neural networks seems like an inevitable fact in the evolution of the space. As software becomes more intelligent, the security techniques used to attack and defend that software are likely to natively leverage a similar level of intelligence. Deep learning posses challenges for the security space that we haven’t seen before, as we can have software that is able to rapidly adapt and generate new forms of attacks. The deep learning space includes a subdiscipline known as adversarial networks that focuses on creating neural networks that can disrupt the functionality of other models. While adversarial networks are often seen as a game theory artifact to improve the robustness of a deep learning model, they can also be used to create security attacks.
One of the most common scenarios of using adversarial examples to disrupt deep learning classifiers. Adversarial examples are inputs to deep learning models that another network has designed to induce a mistake. In the context of classification models, you can think of adversarial attacks as optical illusions for deep learning agents 😊 The following image shows you how a small change in the input dataset causes a model to misclassify a washing machine for a speaker.
If all adversarial attacks were like the example above they wouldn’t be a big deal, However, imagine the same technique used to disrupt an autonomous vehicle by using stickers or paint that project the image of a stop sign. Deep learning luminary Ina Goodfellow describes that approach in a research paper titled Practical Black-Box Attacks Against Machine Learning published a few years ago.
Adversarial attacks are more effective in unsupervised architectures such as reinforcement learning. Unlike supervised learning applications, where a fixed dataset of training examples is processed during learning, in reinforcement learning(RL) these examples are gathered throughout the training process. In simpler terms, an RL model trains a policy and, despite the model objectives being the same, training policies can be significantly different. From the adversarial examples perspective, we can imagine the attack techniques are very different whether it has access to the policy network than when it doesn’t. Using that criterial, deep learning researchers typically classify adversarial attacks in two main groups: black-box vs. white-box.
#artificial-intelligence #invector-labs #machine-learning #deep-learning #data-science #data analysis
If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition
Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.
In this article, we list down 50 latest job openings in data science that opened just last week.
(The jobs are sorted according to the years of experience r
Skills Required: Real-time anomaly detection solutions, NLP, text analytics, log analysis, cloud migration, AI planning, etc.
Skills Required: Data mining experience in Python, R, H2O and/or SAS, cross-functional, highly complex data science projects, SQL or SQL-like tools, among others.
Skills Required: Data modelling, database architecture, database design, database programming such as SQL, Python, etc., forecasting algorithms, cloud platforms, designing and developing ETL and ELT processes, etc.
Skills Required: SQL and querying relational databases, statistical programming language (SAS, R, Python), data visualisation tool (Tableau, Qlikview), project management, etc.
**Location: **Bibinagar, Telangana
Skills Required: Data science frameworks Jupyter notebook, AWS Sagemaker, querying databases and using statistical computer languages: R, Python, SLQ, statistical and data mining techniques, distributed data/computing tools such as Map/Reduce, Flume, Drill, Hadoop, Hive, Spark, Gurobi, MySQL, among others.
#careers #data science #data science career #data science jobs #data science news #data scientist #data scientists #data scientists india
The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.
This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.
As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).
This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.
#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management
Around once a month, I get emailed by a student of some type asking how to get into Data Science, I’ve answered it enough that I decided to write it out here so I can link people to it. So if you’re one of those students, welcome!
I’ll segment this into basic advice, which can be found quite easily if you just google ‘how to get into data science’ and advice that is less common, but advice that I’ve found very useful over the years. I’ll start with the latter, and move on to basic advice. Obviously take this with a grain of salt as all advice comes with a bit of survivorship bias.
#big data & cloud #data science #data scientist #statistics #aspiring data scientist #advice for aspiring data scientists
According to a recent study on analytics and data science jobs, the number of vacancies for data science-related jobs in India has increased by 53 per cent, since India eased the lockdown restrictions. Moreover, India’s share of open data science jobs in the world has seen a steep rise from 7.2 per cent in January to 9.8 per cent in August.
Here is a list of 5 such companies, in no particular order, in India that are currently recruiting Data Scientists in bulk.
#careers #data science #data science career #data science jobs #data science recruitment #data scientist #data scientist jobs