Analytics Insight explains the unexpected challenges from Explainable AI in 2021.

The emergence of cutting-edge technologies has introduced another form of AI known as Explainable AI or XAI in the global market. It is a set of frameworks that help human users understand and trust the interpreted predictions and solutions from machine learning algorithms. The advancements of AI technologies are creating challenges for humans to comprehend the entire process of receiving specific outcomes from these machine learning algorithms. The black box models are created from real-time data that are making it impossible for humans to understand the calculation process. Sometimes the functionalities of ML models or neural networks are difficult to comprehend due to the complicated process. But it is essential for companies and start-ups to have a complete understanding of the rapid decision-making process. It is not often suggestible to blindly trust the AI models because their performance can change if there is a shift in the type of data or biased results based on the demographic and geographic segments. Thus, Explainable AI is the key requirement to promote end-users trust in large-scale implementation of AI models with appropriate explainability and accountability.

• EXPLAINABLE AI: MAKING DECISION-MAKING TRANSPARENT AND INNOVATIVE
• HOW DO WE CREATE TRUSTWORTHY AI WITH AI ETHICS AND TRANSPARENCY?
• MICROSOFT IS PUTTING AI TO WORK FOR A SUSTAINABLE PLANET
• SPOTLIGHT ON AI: LATEST DEVELOPMENTS IN THE FIELD OF ARTIFICIAL INTELLIGENCE

Explainable AI helps organizations to make the stakeholders understand the types of behaviors of AI models through monitoring model insights. There are multiple benefits of Explainable AI such as simplifying the complicated process of model evaluation, continuous monitoring and managing AI models to optimize business insights, and mitigating risks of unintended bias by keeping the models explainable and transparent. That being said, certain concerns with Explainable AI are rising too.

The first concern is the primary function of Explainable AI – explanation with transparency. This policy is becoming a threat for organisations that are continuously innovating new AI models or technologies with machine learning algorithms. The reason is that the creators have to explain and be transparent about the whole process and performance of the whole model to the stakeholders for a better understanding. The firms do not want to disclose all types of confidential information, trade secrets, and source codes to the public for security concerns. Then what will happen to the intellectual property rights that distinguish each company from one another? This is one of the unexpected challenges from the Explainable AI to innovators and entrepreneurs.

The second concern is that machine learning algorithms are highly complex and intangible in nature. Software developers or machine learning engineers can make common people understand the process of creating algorithms but the inner tangible process is very difficult to explain. Customers use these AI products subconsciously in their daily life such as face recognition locks, voice assistants, virtual reality headsets, and so on. But do they really need to know the complicated process in this fast-paced life? This information tends to become a little uninteresting and time-consuming to some stakeholders.

The third concern is for organizations to tackle different forms of explanation for different users with different contexts. Even if any company wants to follow the Explainable AI policy of making people understand the algorithms, different stakeholders can ask about different explanations such as technical details, functionalities, data management, factors affecting the result, and so on. The explanation should reflect the needs and wants of the stakeholders effectively for better stakeholder engagement. But sometimes it is impossible for organizations to answer so many questions at one time.

The fourth concern is receiving unreliable outcomes from these black boxes. Users should trust business insights from AI models, but it consists of potential risks. The system can generate misleading explanations due to a change in data. Then, the users will trust the error with utmost confidence that can lead to a massive failure in the market. These explanations are useful for the short term but not for the long-term plans.

That being said, despite the unexpected challenges from Explainable AI, companies can consider these five essential points to drive appropriate insights from AI models— monitor fairness and debiasing, analyze the models to drift mitigation, apply model risk management, explain the dependencies of machine learning algorithms as well as deploy the projects across different types of clouds.

The post EXPECT THE UNEXPECTED FROM EXPLAINABLE AI IN THE 21ST CENTURY appeared first on Artificial Intelligence.

According to a recent study, almost 27% of the advertised jobs require Python as a core skill, up from 18.5% at the beginning of the year.

Python is one of the TIOBE Index programming languages of the year. It has become the go-to language for developers working on data science and machine learning for a few years now. 

What sets Python apart is its intuitive features like libraries, high productivity, ease of learning etc. According to a recent study, almost 27% of the advertised jobs require Python as a core skill — up from 18.5% at the beginning of the year.

Last October, Python released version 3.9. The updates included improvements in Python internals, performance boosts, dictionary union operators, handy new string functions, new type operations, consistent and stable internal APIs and more.

What To Expect From 3.10

Python started work on the upcoming version, Python 3.10 pre-alpha, in May 2020. Python version 3.10 is set to be released on 4 October 2021. 

However, with the pre-alpha, the company has unveiled an early developer preview of Python 3.10.

The current alpha version of 3.10 is 3.10.0a6, which is the sixth of seven planned alpha releases. During the alpha phase, features may be added up until the start of the beta phase and, if necessary, may be modified or deleted until the release candidate phase. 

The significant new features of the Python 3.10 version are mentioned below:

Structural Pattern Matching

The pattern matching feature has been added in the form of a match statement and case statements of patterns with associated actions. Patterns usually consist of sequences, primitive data types mappings, as well as class instances. Pattern matching will help developers extract information from various complex data types, branch on the structure of data, and apply specific actions based on different data forms.

Better Error Messages In The Parser

When parsing code that contains unclosed parentheses or brackets the interpreter now includes the location of the unclosed bracket of parentheses instead of displaying SyntaxError: unexpected EOF while parsing or pointing to some incorrect location. Previous versions of the interpreter reported confusing places like the location of the syntax error but in Python3.10 a more informative error is emitted.

Parameter Specification Variables

Parameter Specification Variables feature supports forwarding the parameter types of one callable over to another callable, making it difficult to annotate function decorators. This feature proposes typing.ParamSpec and typing.Concatenate that help in specifying the type of a callable. 

Precise Line Numbers for Debugging and Other Tools

According to its developers, Python should guarantee that when tracing is turned on, “line” the tracing events are generated for all lines of code executed and only for lines of code that are executed. A side effect of ensuring correct line numbers is that some bytecodes will need to be marked as artificial and to assist such tools, a new co_lines attribute will be added that describes the mapping from bytecode to source.

Deprecate Distutils Module

Distutils is an undocumented and unmaintained collection of utilities for packaging and distributing Python packages, including the compilation of native extension modules. This feature defines a configuration format that describes a Python distribution and provides the tools to convert a directory of source code into a source distribution and binary distribution forms.

In both the version of Python 3.10 and 3.11, distutils will be formally marked as deprecated. Meaning, all the known issues will be closed at this time, and import distutils will raise a deprecation warning. During Python 3.10 and 3.11, uses of distutils within the standard library may change to use alternative APIs. However, in Python 3.12, distutils will no longer be installed by make install or any of the first-party distributions. 

The post Python 3.10: What To Expect appeared first on Artificial Intelligence.