“Immuta Discover is the foundational component to making any data security program work within your organization and to remain compliant. It’s the first step that must happen in order to protect your data. Data is constantly changing in today’s digital age. Organizations must fully understand what data they have – with accurate classification – and maintain a trustworthy inventory of sensitive data across cloud platforms in order to meet growing data security and access demands. And Immuta Discover helps our customers accomplish just this”, said Mo Plassnig, chief product officer at Immuta.

Immuta Discover leverages cutting-edge technology to identify structured data within cloud platforms, harnessing metadata from renowned enterprise data catalogs, including the likes of Alation, a distinguished data intelligence catalog. This advancement promises to elevate the efficiency and accuracy of data management within the cloud, empowering organizations to navigate the complex data landscape with ease.

Immuta Discover revolutionizes data management by automating manual processes, strengthening data security and providing unprecedented data visibility. This transformative solution empowers organizations to uncover the true value of their data by delivering important insights and improving data protection.

The key to effective data security, as Immuta highlights, lies in accurate data discovery, tagging and classification, coupled with comprehensive metadata. In a recent post, Immuta highlighted common pitfalls of existing solutions, pointing to accuracy and granularity issues that can lead to security vulnerabilities and compliance issues.

Immuta Discover aims to strengthen data protection and encourage informed decision making, offering a brighter and more secure future for data-driven organizations.

“The ability to create a survey at lightning speed will allow brands, researchers, and marketers to quickly get insights from our millions of engaged global respondents.”

said John Papadakis, founder and chief executive at Pollfish.

“We’re especially thrilled about the release of Pollfish AI, which furthers Prodege’s mission to make research accessible for all, by making the process of creating surveys simple, accurate, and fun.”

said Mendy Orimland, chief revenue officer at Prodege.

Pollfish AI is the easiest to use and one of the fastest platforms for creating surveys using artificial intelligence. It can generate a professional survey questionnaire in 30 seconds or even less, all based on a single sentence the user types on the topic of interest.

Pollfish generates full surveys from commands like, “create a pricing survey for my new health app,” “consumer attitudes about buying a home in these macroeconomic conditions,” or “what’s the best way to compete with the leading coffee chains?”

Surveys can be quickly and easily enhanced with additional queries such as “add more pricing options” or “make this more applicable to the healthcare industry”. Pollfish AI also adds value to surveys by bringing new insights, illuminating meaningful insights from questions you wouldn’t have thought to ask.

This new programming language is trending on the Internet because of its key feature of producing correct results. No matter what your syntax, Noulith claims to be more than accurate. Brian Chen explains that he will constantly update the code for the sake of improving it.

Here are the key features of Noulith

• Operator precedence is resolved at runtime.
• Everything here is an infix operator, so, nearly everything can be partially applied. 
• Noulith doesn’t distinguish between sets and dictionaries. Here, the lists are brackets. Dictionaries are curly braces. It doesn’t care about a separate set type, but dictionaries often behave quite like their sets of keys.
• Operator precedence is customisable and resolved at runtime, so you will always get error-free results. 
• It is dynamically typed with no whitespace or indentation-sensitive. 
• Here, you can declare variables with ‘:=.’ and everything is an expression.
• No classes or members or whatever; it’s just global functions all the way down. Or up. At the highest level, statements are C/Java/Scala-style 
• For loops, you can use left arrows and double-headed arrows for index-value or key-value pairs.
• Prefix operators might be tricky. So, when you are in doubt, parenthesise the operand.

However, it seems that the developer community is not impressed with this new programming language and calls it a rudimentary “screaming thought” – rather an improved version of C++.

“We will continue to cooperate with the anti-monopoly authorities to curb monopolies and actively deal with algorithm discrimination and other new forms of anti-competition behavior.” 

The People’s Bank of China (PBOC) will continue to strengthen the regulation of the payments sector and ask all financial services companies, including personal information businesses, to be licensed, Mr Yi said.

Mr Yi named three principles to guide the regulatory clean-up of fintech: financial businesses must be licensed to operate; firewalls must be set up between different parts of the business, such as insurance and wealth management, to prevent cross-sector risks; and the direct link between non-banks and banking information services must be cut.

The banking and insurance regulator has also told fintech platforms offering banking services that they must comply with the same capital requirements as those imposed on traditional lenders to curb risks.

Mr Yi said financial institutions should collect, use and store information in strict accordance with the law, and ensure they protect the personal privacy and rights of consumers. The central bank will look at defining data ownership in a more accurate manner, better facilitate data transactions and promote the fair use of data, he said.

Python 3.10 doesn’t have many new key features, but one of the few features it has, structural pattern matching, could be the only most important addition to the language syntax.

Structural pattern matching

The result of an additional attempt that previously failed switch/case-Similar to Python syntax, structural pattern matching allows variables to match one of a set of possible values ​​(switch/case in other languages). However, you can also collate with a pattern of values. This greatly expands the range of possibilities and allows you to write code that quickly covers different scenarios.

More accurate error reporting

Python error reporting has long been at the mercy of the parser’s whims. Python 3.9 rolled out All new parser — The Python team is faster, more robust, easier to maintain, and less plagued by internal hacks.

Parameter specification variable

The Python typing module used to annotate code with type information allows you to describe the types of callable objects (such as functions). However, that type of information cannot be propagated between callable objects. This makes it difficult to annotate function decorators and the like.

Two new additions to typing, typing.ParamSpec – when typing.Concatenate, it allows you to annotate callable objects with more abstract type definition information.

Other major changes in Python 3.10.

• Union types can be expressed as: X|Y, Excluding that Union[X,Y], To be concise (PEP 604).
• NS zip The built-in that combines the results of multiple repeatable objects strict keyword. When set to True, That’s the cause zip Raises an exception if one of the iterables is exhausted before the other iterables (PEP 618).
• You can now declare variables as type aliases. This improves forward references, more robust type-related errors, and the distinction between in-scope type declarations (PEP 613).
• OpenSSL 1.1.1 or later is required to build CPython. This will update one of CPython’s major dependencies (PEP 644).

Autonomous vehicles rely on three main technologies, the first of which is high-precision localization, robust and reliable environment perception and last but not least the prediction and motion planning algorithms for safe manoeuvres. The motion planner is easier to achieve as long as the accuracy of the localization and perception data is higher.

Photo Credits: Qualcomm

• The unified localization solution

An autonomous vehicle must be able to localize itself within a few centimetres of accuracy to plan and execute complex manoeuvres required in real-world road conditions. A way to reach higher levels of accuracy is by HD Maps. Qualcomm shared:

“Our approach is to develop advanced and optimized modules that solve such challenges for our automotive OEM customers and partners.”

One such component is Qualcomm Vision Enhanced Precise Positioning (VEPP) software, which can provide lane-level positioning and a precise heading in the global coordinates by tightly fusing GNSS, IMU, camera, and vehicle odometry information in real-time and in virtually all environments.

The localization solution fuses the global pose with camera perception and HD map information and also provides an ability to generate a local map, by producing identification of gaps or errors in HD maps on the fly — all while the vehicle can operate in an autonomous mode.

• Novel techniques for efficient environment perception

One of the main focus areas for Qualcomm is to scale environmental perception technologies in a more efficient way.  Their aim is to harness all sensor types including cameras, radar, and lidar, as well as maps to build novel ways of solving environment perception challenges.

There is multisensor data fusion, which includes data association, as well as object-state estimation. Data association is basically the ability of the autonomous vehicle to correlate data from different views. State estimation involves tracking real-world objects and estimates their size, speed, and position relative to the autonomous vehicle itself.

Another Qualcomm’s focus area is the accurate prediction of the pose and tracking the size of objects on the road with 3D estimations. This happens by the use of 2D data from cameras, radar and maps. 3D estimations leveraging both deep learning and concepts from multi-view geometry. Such accurate 3D estimations can enable human-like manoeuvres for the advanced safety of the autonomous vehicle.

• Accelerating autonomous driving solutions

Qualcomm Technologies has been at the forefront of providing connectivity solutions. Building on this solid understanding of the automotive industry, the representatives of Qualcomm are now helping drive some of the biggest innovations in AD.

Their comprehensive frameworks and tools around cross-sensory calibration, active learning, big data management, and automated verification/validation can significantly reduce the time-to-market for autonomous vehicle manufacturers.