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AI models could help companies overcome human bias

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Machine learning algorithms can reinforce human bias, but representatives from HireVue Inc. and Kantar Millward…

Brown recently argued that they may also be able to remove our biases from processes.

HireVue CTO Loren Larsen said the AI models developed by the on-demand video interview platform company not only make the search for strong candidates more efficient, they also make it fairer for those applying.

The technology enables companies to scale the search process, meaning they can “take more chances and just let someone take [an interview] slot,” Larsen said at the recent Emotion AI Summit in Boston. By adding machine learning, HireVue is hoping to take things a step further and reduce human bias in the hiring process.

Take the example of how a candidate’s looks affect the job search. A HireVue data scientist developed an AI model to determine how much attractiveness might factor into hiring decisions. The model was trained on a public database of images and then was used to score attractiveness on a scale from one to 10.

“It turns out that if you got a seven or higher, you’re twice as likely to get hired than if you were a three,” Larsen said. That figure might be palatable if attractiveness equated to job performance, but, HireVue’s study couldn’t find a correlation between the two.

To that end, HireVue has striven to build AI models that can predict a job applicant’s potential performance — without a human in the loop. The models look for “traditional competencies,” according to Larsen, such as a candidate’s emotional awareness; negotiation skills; ability to collaborate, work with a team and learn.

HireVue’s AI models not only consider what’s being said by job candidates, but how it’s being said. They’re trained to factor in facial expressions and emotion — technology that’s powered by Affectiva, a software company spun out of the MIT Media Lab as well as the conference host.

AI models in advertising

At Kantar Millward Brown, a market research company based out of London, Affectiva’s software is helping make the case for more inclusive commercials. The company specializes in “advertising development work.” It helps clients understand how their ads are likely to be received by viewers and then finds ways to make them better.

“Some of that is done in what this audience may think of as a relatively old-school way: We show people the ads and ask them questions,” said Graham Page, executive vice president and head of global research solutions, at the summit.

Some of the work is done in a decidedly modern way. The firm films participants in a focus group as they watch an advertisement, and then it analyzes facial expressions and other  physiological data using Affectiva’s software “to understand the emotional response to the ad as it plays and what the key moments are that really resonated with people,” Page said.

For example, an analysis of advertisements done for Unilever, one of Kantar Millward Brown’s biggest clients, found that the ads categorized as “more progressive,” or more diverse, were 25% more effective than advertisements categorized as “less progressive,” or more stereotypical. And ads categorized as the least progressive were twice as likely to achieve the lowest scores on effectiveness, according to Page.

He described this study and others that have shown similar findings as “instructive” in that they help build a case for other businesses that “things like progressive advertisements are not only ethically the right thing to do, they’re also good for business,” he said.

‘IT departments suck’

IT’s reputation is still dubious, at least according to the VC panelists at the conference. When the moderator asked what advice the VCs could provide startups on how to sell to corporations, Krishna Gupta from Romulus Capital didn’t mince words: “IT departments suck.” He described integration as a rate limiter for many companies.

Janet Bannister, partner at Real Ventures in Montreal, suggested startups fret less about selling against other startups and more about selling against incumbents. She said large companies might understand that a startup can solve a problem better than the technology they’re currently using, but see the startup’s future as uncertain. “Having a strong use case, other customers using the product and great investors that will speak on the company’s behalf” may help assuage a large company’s concerns, she said.

Say what?!?

“Humans are unique. We’re awesome. Let’s get beyond that point and look at the attributes that we need in an artificial intelligence system that would enable us to trust it with more and more functionality. I think it’s a continuum. Just like ethics is a continuum. Morality is a continuum. … And I think we need to invite our machines into that continuum, that struggle, that wrestle that we’re in.” — Babak Hodjat, founder and chief scientist, Sentient Technology

“It’s kind of a tough time to think about how we encourage people to trust AI. And that’s particularly true given that some of the biggest businesses that use AI, particularly in the social sharing space, are at the absolute center of a massive crisis of trust.” — Graham Page, executive vice president and head of global research solutions, Kantar Millward Brown

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Source: https://searchcio.techtarget.com/news/252449410/AI-models-could-help-companies-overcome-human-bias

Big Data

Auto-ML – What, Why, When and Open-source packages

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Auto-ML – What, Why, When and Open-source packages – Analytics Vidhya





















Learn everything about Analytics


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Source: https://www.analyticsvidhya.com/blog/2021/05/auto-ml-some-prominent-automl-libraries/

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13 Most Important Pandas Functions for Data Science

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13 Most Important Pandas Functions for Data Science – Analytics Vidhya






















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AI-powered identity access management platform Authomize raises $16M

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Join Transform 2021 this July 12-16. Register for the AI event of the year.


Cloud-based authorization startup Authomize today announced that it raised $16 million in series A funding led by Innovation Endeavors, bringing the startup’s total raised to $22 million to date. CEO and cofounder Dotan Bar Noy says that the capital will be used to support Authomize’s R&D and hiring efforts this year, as expansion ramps up.

One study found that companies consider implementing adequate identity governance and administration (IGA) practices to be among the least urgent tasks when it comes to securing the cloud. That’s despite the fact that, according to LastPass, 82% of IT professionals at small and mid-size businesses say identity challenges and poor practices pose risks to their employers.

Authomize, which emerged from stealth in June 2020, aims to address IGA challenges by delivering a complete view of apps across cloud environments. The company’s platform is designed to reduce the burden on IT teams by providing prescriptive, corrective suggestions and securing identities, revealing the right level of permissions and managing risk to ensure compliance.

“As security has evolved from endpoints and networks, attention has increasingly moved to identity and access management, and specifically the authorization space. Many of the CISOs and CIOs we spoke with expressed the need for a system that would secure and manage permissions from a single platform. They took access decisions based on hunches, not data, and when they tried to take data-driven decisions, they found out that the data was outdated. Additionally, most, if not all, of the process has been manually managed, making the IT and security teams the bottleneck for growth,” Noy told VentureBeat in an interview via email.

Authomize’s secret sauce is a technology called Smart Groups that aggregates data from enterprise systems in real time and infers the right-sized permissions. Using this data in tandem with graph neural networksunsupervised learning methods, evolutionary systems, and quantum-inspired algorithms, the platform offers action and process automation recommendations.

AI-powered recommendations

Using AI, Authomize detects relationships between identities and company assets throughout an organization’s clouds. The platform offers an inventory of access policies, blocking unintended access with guardrails and alerting on anomalies and risks. In practice, Authomize constructs a set of policies for each identity-asset relationship. It performs continuous access modeling, self-correcting as it incorporates new inputs like actual usage, activities, and decisions.

Of course, Authomize isn’t the only company in the market claiming to automate away IGA. ForgeRock, for instance, recently raised $93.5 million to further develop its products that tap AI and machine learning to streamline activities like approving access requests, performing certifications, and predicting what access should be provisioned to users.

But Authomize has the backing of notable investor M12 (Microsoft’s venture fund), Entrée Capital, and Blumberg Capital, along with acting and former CIOs, CISOs, and advisers from Okta, Splunk, ServiceNow, Fidelity, and Rubrik. Several undisclosed partners use the company’s product in production, Authomize claims — including an organization with 5,000 employees that tapped Smart Groups to cut its roughly 50,000 Microsoft Office 365 entitlements by 95%. And annual recurring revenue growth is expected to hit 600% during 2021.

Authomize recently launched an integration with the Microsoft Graph API to provide explainable, prescriptive recommendations for Microsoft services permissions. Via the API, Authomize can evaluate customers’ organization structure and authorization details, including role assignments, group security settings, SharePoint sites, OneDrive files access details, calendar sharing information, applications, and service principal access scopes and settings.

“Our technology is allowing teams to make authorization decisions based on accurate and updated data, and we also automate day-to-day processes to reduce IT burden … Authomize currently secures more than 7 million identities and hundreds of millions of assets, and our solution is deployed across dozens of customers,” Noy said. “Using our proprietary [platform], organizations can now strike a balance between security and IT, ensuring human and machine identity have only the permission they need. Our technology is built to connect easily to the entire organization stack and help solve the increasing complexity security, and IT teams face while reducing the overall operational burden.”

Authomize, which is based in Tel Aviv, Israel, has 22 full-time employees. It expects to have more than 55 by the end of the year as it expands its R&D teams to develop new entitlement eligibility engine and automation capabilities and increases its sales and marketing operations in North America.

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Source: https://venturebeat.com/2021/05/13/ai-powered-identity-access-management-platform-authomize-raises-22m/

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Why We Should Have Different Databases

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Artem Gogin Hacker Noon profile picture

@artemgArtem Gogin

Data Engineer, Teacher and Technical Writer.

Today there are hundreds of SQL and NoSQL databases. Some of them are popular, some are ignored. Some are user-friendly and well documented and some are hard to use. Some are open-sourced and some are proprietary. And, perhaps, the most important – some are scalable, optimized, highly available and some are difficult to scale or maintain.

There comes the natural question: how to choose a database? To answer it, we should decide, what we want to achieve with a database. To create a view, we should answer questions like these:

  1. Do we need analytical access to the database?
  2. Do we need real-time writing or reading?
  3. How many tables/records we want to store?
  4. What availability do we need?
  5. Do we need columns?
  6. Will we access tables with filter by column or with filter by row?

When the decision is done, we need to keep in mind, what one or another database able to offer. Particular features of each database may vary, but in general, there are only a few types of databases. Within these types, we can achieve mostly the same goals. Let’s look at them closely.

1. SQL relational databases.

If you have ever worked with databases, most likely you have begun with this type of database. This type is the most popular and widespread. These databases allow storing data in relational tables with defined columns of a particular type. Relational tables are good normalization and joins.

Advantages

  • SQL support
  • ACID transactions (Atomicity, Consistency, Isolation, and Durability)
  • Indexing and partitioning support

Disadvantages

  • Poor support of unstructured data / complex types
  • Bad optimization for event processing
  • Difficult/expensive scaling

Examples: Oracle DB, MySQL, PostgreSQL.

2. Document-oriented databases.

If we don’t want to join several tables to retrieve desired data, we can look at the document-oriented databases. These databases allow storing records in JSON-like format. With this format, we can create complex value for any key and include all the data structure in one record at once.

Advantages

  • Schema free
  • No need to always write all the fields in every record
  • Good complex types support
  • OLTP suited

Disadvantages

  • Poor transactions support
  • Poor analytics support
  • Difficult/expensive scaling

Examples: MongoDB

3. In-memory databases.

Databases of this type can provide real-time response for selecting and inserting particular records. Most of them mainly store data into RAM but also offer persistent storage on HDD or SSD for some cases. Most of these databases operate with key/value records, so the values may recall document-oriented format. But some databases also operate with columns and allow secondary indexing in the same table. Using RAM allows to process data rapidly but makes it more unstable and expensive.

Advantages

  • Fast writing
  • Fast reading

Disadvantages

  • Difficult reliability
  • Expensive scaling

Examples: Redis, Tarantool, Apache Ignite

4. Wide-column databases.

These databases store data as key/value records on HDD or SSD. These solutions are designed to scale well enough to manage petabytes of data across thousands of commodity servers in a distributed system. They represent the SSTable architecture. This architecture was designed for two use cases: fast access by key and fast, highly available writing.

Advantages

  • Fast writing row by row
  • Fast reading by key
  • Good scalability
  • High availability

Disadvantages

  • Key/value format
  • No analytics support

Examples: Cassandra, HBase

5. Columnar databases.

Sometimes we need to access data fast not with particular keys, but with particular columns. In this case, we better get rid of inserting row by row and move to batch writing. Batch inserts allow columnar databases to prepare the data for rapid read by columns.

Advantages

  • Fast reading by column
  • Good analytics support
  • Good scalability

Disadvantages

  • Only good for batch inserts

Examples: Vertica, Clickhouse

6. Search engine

If we want to access the data with filter by any value and even with any word in column, we should remember search engines. These databases perform indexing of every word in columns and allow full-text search. They are perfect for storing and analyzing logs or large text values.

Advantages

  • Quick access by any word
  • Good scalability

Disadvantages

  • Only good for batch inserts
  • Poor analytics support

Examples: ElasticSearch, Apache Solr

7. Graph databases

For some use cases exist graph data structures. We can find their realization in graph databases. If your tasks require working with graphs, there are special databases designed to satisfy your needs.

Advantages

  • Graph data structure
  • Manageable relations between entities
  • Flexible structures

Disadvantages

  • Special query language
  • Difficult to scale

Examples: Neo4j

Conclusion

Almost every task can be done with almost any type of database. The question is how expensive and optimized it would be. Choosing the tool you are used to can reduce your time to market, but it also can cost you an enormous amount of money to maintain and expand your hardware, which may be used inefficiently. Always try to use a database in the way it was meant to use. Perhaps, a solution that suits your needs already exists.

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