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NYU professor and author Professor Scott Galloway has outlined seven antitrust questions Tim Cook may be asked when he appears before Congress on Wednesday.

Apple is one of four tech giants due to be grilled by the House Judiciary Committee – alongside Amazon, Facebook, and Google – and Galloway is well-qualified to discuss the antitrust issues around all of them …

Back in 2023, he wrote the NYT and USA Today bestselling book The Four: The Hidden DNA of Amazon, Apple, Facebook, and Google (Apple Books, Amazon). In it, he opens with some hard-hitting questions.

So, are these entities the Four Horsemen of god, love, sex, and consumption? Or are they the Four Horsemen of the apocalypse? The answer is yes to both questions. I’ll just call them the Four Horsemen.

How did these companies aggregate so much power? How can an inanimate, for-profit enterprise become so deeply ingrained in our psyche that it reshapes the rules of what a company can do and be? What does unprecedented scale and influence mean for the future of business and the global economy? Are they destined, like other business titans before them, to be eclipsed by younger, sexier rivals? Or have they become so entrenched that nobody-individual, enterprise, government, or otherwise-stands a chance?

Galloway has now written a blog post listing four key antitrust questions he thinks need to be put to all of the tech giants, with a further three for Apple specifically. The first four share a common theme: are the companies now so large that they make it almost impossible for smaller players to compete?

Q: Messrs. Bezos, Cook, Pichai, Zuckerberg, your firms have added greater market capitalization in the last five years than the largest retailers and CPG firms have in total. This is a large portion of the entire consumer economy. If you were public servants, would you be concerned that too many of the spoils are being registered by increasingly fewer firms and people?

Q: Your market capitalization per employee is thousands of times higher than that of other companies in your sectors. Do you think your companies contribute to income inequality?

Q: Small business formation is at a multi-decade low. The fastest-growing sectors receive scant funding from investors. Why should someone invest in a search engine right now, or a music streaming business, or a social media platform, or an e-commerce firm, given the sizes of your companies?

And the three antitrust questions for Apple specifically:

Q: Mr. Cook, monopoly rent is when a monopoly producer lacks competition and thus can sell its goods and services at a price far above what the otherwise competitive market price would be, at the expense of consumers. Our information age is often called “the app economy,” denoting how important apps have become to commerce and consumption. Your firm and Google dominate the app ecosystem, with 62% and 38% shares, respectively. Every media company that wants to reach a consumer online must pay you a toll or rent. Do you think any of these firms believe that paying you this rent is a choice?

Q: Apple TV+ is offering consumers $1 billion in original content for every .80c a month the consumer spends on your Apple TV+ streaming video service. Isn’t it your opportunity to differentiate your $1,300 phones and fund Apple TV+ from the revenues of an unrelated product that allows you to offer a media product at well below cost? In sum, isn’t Apple guilty of “dumping,” that is, buying market share with unfeasibly low prices?

Q: Spotify is consistently rated as a superior music service to your Apple Music, yet Apple Music is growing faster than Spotify in the US. Isn’t this a function of you owning the rails, and being able to levy a 30% tax on a competitor while illegally reducing their discoverability in the app store?

Galloway supports each question with illustrative graphics. You can see these – and the specific antitrust questions he wants to see put to the three other tech giants – in the blog post.

The hearing was postponed last week when it turned out to clash with a memorial service for the acclaimed civil rights leader Rep. John Lewis, and is now set to go ahead on July 29, starting at 12 noon ET. The coronavirus crisis means that Cook and the other CEOs will be allowed to give evidence via video link.

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Most Frequently Asked Nlp Interview Questions

This article was published as a part of the Data Science Blogathon.


Natural language processing (NLP) is the branch of computer science and, more specifically, the domain of artificial intelligence (AI) that focuses on providing computers the ability to understand written and spoken language in a way similar to that of humans.

Combining computational linguistics (rule-based modeling of human language) with statistical, machine learning, and deep learning models is natural language processing (NLP). Together, these technologies enable computers to ‘understand’ the whole meaning of human language in the form of text or speech data, including the speaker’s or writer’s purpose and emotion.

NLP is the driving force behind computer systems that translate text from one language to another, respond to spoken commands, and swiftly summarise massive amounts of information—even in real-time. There is a strong probability that you have engaged with NLP through voice-activated GPS systems, digital assistants, speech-to-text dictation software, customer service chatbots, and other consumer conveniences. NLP plays an increasing role too in corporate solutions that optimize business operations, boost staff productivity, and simplify mission-critical business procedures.

What is the importance of natural language processing?

Businesses use the massive volume of unstructured, text-heavy data and require a method for processing it efficiently. Most of the data produced online and saved in databases are natural human languages. Until recently, organizations were unable to inspect this data efficiently. Herein lies the utility of natural language processing.

NLP Interview Questions

1. What do you mean by NLTK?

NLTK, which stands for Natural Language Toolkit, is a Python library. We use NLTK to process spoken language data. NLTK facilitates the application of techniques like parsing, tokenization, lemmatization, and stemming from comprehending natural languages. It aids in text categorization, parsing linguistic structure, document analysis, etc.

2. What is Parsing in regards to NLP?

In natural language processing, parsing refers to a machine’s understanding of a sentence’s grammatical structure. Parsing enables a device to understand the meaning of a word in a sentence along with the grouping of words, phrases, nouns, subjects, and objects. Parsing facilitates the analysis of a text or document to uncover valuable information.

3. What do you mean by Syntactic Analysis?

Syntactic analysis is a method used to derive meaning from sentences. A machine can examine and comprehend the order of words in a phrase through syntactic analysis. NLP makes use of the grammar rules of a language to aid in the syntactic analysis of the combination and order of words in texts.

Sentence: the dog saw a man in the park


4. In NLP, what is Pragmatic Ambiguity?

Pragmatic ambiguity refers to words with several meanings whose usage in any given sentence is context-dependent. The same language may have several meanings due to pragmatic ambiguity. Most phrases we encounter contain words with several meanings, leaving them open to interpretation. This varied interpretation results in ambiguity and is referred to in NLP as Pragmatic Ambiguity.

5. What does Stemming imply in NLP?

Stemming is the process of eliminating suffixes from words to get their root form. It is comparable to chopping a tree’s branches into its trunk. For instance, the stem of eating, eats, and eaten is eat. Search engines index the words using stemming. Stemming is crucial for natural language comprehension (NLU) and natural language processing (NLP).

6. What do you mean by POS tagging?

Parts of speech tagging, often known as POS tagging, is the process of detecting individual words in a document and classifying them based on their context as parts of speech. POS tagging is also referred to as grammatical tagging since it requires understanding grammatical structures and identifying the corresponding chúng tôi tagging is a complex approach since the same word can have several meanings depending on context. For the same reason, the same basic approach employed for word mapping is unsuccessful for POS tagging.

7. What does Lemmatization imply in NLP?

Lemmatization is mapping a word’s different forms to its root (also known as the “lemma”). Although this may look similar to the definition of stemming, it is distinct. For instance, after stemming, the word “better” remains unchanged. Upon lemmatization, however, this should become “excellent.” Lemmatization requires a deeper understanding of language. Modeling and designing effective lemmatizers is still an open question in NLP research.

8. What does Text Normalization mean in NLP?

9. What does TF-IDF mean in Natural language processing?

TF-IDF, also known as Term Frequency-Inverse Document Frequency, is a method for determining the significance of a word relative to other terms in a corpus. It is a typical metric for information retrieval (IR) and summarization scoring. TF-IDF translates words to vectors and adds semantic information, resulting in weighted uncommon words that may be utilized in several NLP applications.

10. What is NES?

Named entity recognition, or NER, is finding entities in a written document that are more informative and have their context. These frequently signify locations, individuals, and organizations. Even though these items appear to be proper nouns, the NER method identifies much more than simply the nouns. In actuality, NER entails entity chunking or extraction, in which entities are split to classify them under several predetermined classifications. This process helps extract information further.


NLP is an area of computer science and AI (AI)

In NLP, parsing is a machine’s knowledge of a sentence’s grammar.

When you remove a word’s suffix, you return it to its original form.

TF-IDF stands for Term Frequency-Inverse Document Frequency

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Top Google Bigquery Frequently Asked Interview Questions


Suppose you are appearing in an interview for the Junior or senior role. In that case, it’s important to have a basic understanding of GCP and BigQuery. So, in this article, you will learn interview questions related to GCP.

You can start introducing BigQuery: “It is a powerful cloud-based data warehousing solution that can handle large-scale data processing tasks, including machine learning, predictive analytics, data visualization, and real-time data streaming.”


You might be asked to share a specific example of a business problem you solved using BigQuery, and prepare recent work and projects.

Note: These questions are just a few examples of the types of questions you might encounter during a GCP BigQuery interview, and answers may vary from person to person.

Table of Contents Q1. How does BigQuery differ from traditional data warehousings solutions like Oracle or SQL Server?

We can differentiate BigQuery from traditional data warehousing solutions in a few ways,

You start querying data right away without setting up infrastructure in BigQuery.

It handles large datasets and processes queries quickly using a distributed architecture. It’s serverless, so we don’t need to manage servers or infrastructure.

BigQuery is a modern cloud-based solution that allows for more flexibility and scalability than traditional data warehousing solutions and is easier to use and manage.

Q2.  How do you manage data security and privacy, especially when dealing with sensitive data?

To manage data security and privacy in BigQuery, you can explain to the interviewer:

Limit access with IAM roles

Encrypt data in transit and at rest

Enable audit logging, use data masking

Check for compliance certifications

Establish data retention policies.

We can help ensure our sensitive data’s confidentiality, integrity, and availability in BigQuery.

Q3.  How do you design a schema for a complex data model, such as a hierarchical or graph database?

Designing a BigQuery schema for a complex data model, such as a hierarchical or graph database, requires careful consideration of the data structure and relationships.

To design a BigQuery schema for a complex data model, you can explain to the interviewer:

Identify entities and relationships

Normalize the data

Choose an appropriate schema type

Optimize for query performance

Test and iterate as needed.

Q4.  How do you handle streaming data, and what are some best practices for real-time data processing?

We can use BigQuery’s streaming inserts, choose the appropriate data ingestion method, optimize data ingestion, optimize query performance, and implement real-time monitoring and alerting. By implementing these best practices, we can ensure that our real-time data is processed efficiently and accurately and that issues are detected and resolved quickly.

Integrating BigQuery with other data processing tools, like Apache Spark or Apache Beam, can help us to perform complex data analysis tasks. We can use BigQuery’s connectors, APIs, third-party tools, or data transfer services to integrate with these tools. By integrating BigQuery with other data processing tools, we can simplify and enhance our data processing and analysis capabilities.

Source: GCP

Q6.  How do you use BigQuery ML to perform machine learning tasks like regression or classification?

To perform machine learning tasks using BigQuery ML, we must prepare our data, choose a model type, create and train it using SQL statements, evaluate its performance, and make predictions. By following the below steps, We can perform machine learning tasks within BigQuery and gain insights from our data more efficiently.

Q7.  How do you monitor performance and usage?

We can track query performance with execution time, bytes processed, and slot usage also; we can Monitor CPU, memory, and network throughput for resource usage. We can also track job completion time, error rates, and concurrency for BigQuery operations.

Q8.  How do you handle versioning, and what are some best practices for data version control?

Version control can be managed using the BigQuery Data Catalog, source control tools like Git, and by maintaining clear documentation of our data pipeline and transformation processes. Best practices for data version control involve using tools like the BigQuery Data Catalog and source control tools, along with maintaining clear documentation of the data pipeline and transformation processes.

It can be used for data visualization and reporting by connecting it with visualization tools like Google Data Studio, Looker, Tableau, or Power BI. These tools allow us to create custom dashboards and reports by querying data directly from BigQuery. Common visualization techniques include creating charts, graphs, tables, and other interactive visualizations to help communicate insights from our data.


We covered a variety of questions related to GCP BigQuery. Understanding best practices for designing efficient schemas, managing data security and privacy, monitoring performance and usage, troubleshooting common issues, integrating with other data processing tools, and handling data from different sources and regions is important.

Key Takeaways:

Understanding how to optimize query performance, including techniques such as partitioning, clustering, and using appropriate data types.

Following best data security and privacy practices, such as using encryption and access controls to protect sensitive data.

Monitoring performance and usage metrics to identify bottlenecks and optimize resources.

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China’s Booming Middle Class Bodes Well For Apple, Says Tim Cook

Apple CEO Tim Cook still sees great potential in the Chinese market despite a drop in its revenue from the country in the first quarter.

In an interview to Jim Cramer of CNBC’s Mad Money program, Cook said Monday that the middle class in China is expected to boom from 50 million people five years ago to almost 500 million in the next five years. “This is an unprecedented growth of the middle class,” said Cook, adding that he “could not be more optimistic about China.”

To a question from Cramer, Cook said it was an error by him not to to mention the figures about the burgeoning Chinese middle class during the company’s recent earnings call.

Apple reported last month that the number of smartphones it sold worldwide fell by 16 percent to 51.2 million units in its fiscal second quarter ended March 26. The company saw a 26 percent year-on-year decline in revenue from Greater China, its second largest market. The revenue drop in mainland China was less at 11 percent.

The company’s overall revenue and profit for the quarter also fell from the same period last year. But Cook’s message on CNBC was that Apple was still doing fine, despite some investors and analysts expressing reservations about the outlook for the company.

Apple is facing iPhone fatigue and pressure is mounting for the company “to innovate a new wow design beyond its standard rectangle form factor,” said Neil Mawston, executive director at research firm Strategy Analytics, for example.

The company is to an extent the victim of its own success in the previous year with the iPhone 6. People are upgrading at a lower rate than they did last year but still higher than the year before, as the company had an “abnormally high upgrade rate” last year as people bought into the iPhone 6, Cook said. “Now we are comparing to that along with the other things going on that many companies are facing with currency rates and macro economics, etc.,” he added.

Cook said the company is seeing higher rates of people switching from Android phones to iPhones, which was the largest ever in the last six months.

In China, its second largest market by revenue, switchers were up 40 percent from the first half of last year to the first half of this year, Cook said. The new 4-inch iPhone SE is doing well in that market and the company hopes to resolve soon with Chinese government agencies and businesses the issues that forced it to take its books and movie services online.

Services such as books and movies is an area in which the company sees great potential. Revenue from services, including Apple Music and the App Store, grew 20 percent to US$6 billion in the first quarter, and is the second largest revenue segment for the company.

The relationship with Apple doesn’t stop when people buy an iPhone, but continues with users buying apps across the App Store, or subscribing to Apple Music, buying songs, renting moves, using Apple Pay and buying additional storage on iCloud, Cook said.  All this follows  from the number of people using Apple devices and there are currently over a billion in use, he added.

Services may be also an area for Apple’s merger and acquisitions. The company has said it may do bigger M&As than previously. “Could it come in services? Yes, and we’ve bought some companies to help us in services. But it can also come in a number of other areas,” Cook told Cramer.

The Roundup: The Boss May Be Watching

A report issued this week by the Denver-based Privacy Foundation says more than one in three online workers in the United States have their Internet or e-mail use under “continuous surveillance” by their employers.

The report says the monitoring targets 14 million employees in the U.S., meaning more than half of the estimated 27 million online workers worldwide have their usage overseen by the boss. Concerns over liability and productivity are prime motivators to implementing the software, which is becoming cheaper to install and run — now about $5.25 per employee per year.

The study, called the first attempt to estimate the extent of workplace monitoring, is based on self-reported user and revenue figures from publicly traded companies that sell e-mail and Internet monitoring software. The report covers only continuous, systematic monitoring, not random spot checks.

Websense is the most frequently used Internet monitoring product, with MIMEsweeper the most used e-mail monitoring product. Buyers of the software (according to vendors’ disclosures) include top companies and government agencies, including 20th Century Fox, Glaxo Wellcome, Nike, Duracell, Barclays, Marriott, Texaco, and American Express, as well as the Small Business Administration, the National Park Service, and the City of Boston. The Privacy Foundation reports that the U.S. Army recently purchased a 200,000-seat installation from Websense, for about $1.8 million, including hardware (about $9 per seat).

The Privacy Foundation says the report raises the question of whether employers are giving workers sufficient notice of continuous Internet and e-mail monitoring.

“Notice alone might not go far enough,” says Andrew Schulman, chief researcher for the Privacy Foundation’s Workplace Surveillance Project and the study’s author. He says employers “are basing firing and suspension decisions on the employee-monitoring reports. Yet employees are generally not told beforehand what information will be gathered and how it will be judged. Companies can use employee-monitoring logs as a kind of ‘wishing well’ to justify actions against employees, including dismissals and layoffs.”

CRM Investments: Money Well Spent

Internet research firm Jupiter Media Metrix recently reported that three out of four businesses will spend more money on customer relationship management (CRM) tools in 2001 than in 2000, as they respond to the increasing numbers of consumers seeking online customer service. Some companies will increase their CRM spending by as much as 25 to 50 percent.

Though the ailing economy is causing companies to cut costs in many areas of their business, customers still expect the same level of service, says Jupiter analyst David Daniels. “Customer satisfaction has always been a key metric for positive financial results. Businesses must not make CRM investments only to keep pace with growth — they should view their CRM spending as a strategic benefit that will bring higher levels of customer satisfaction and retention.”

The Return of Snail Mail?

Office products maker Pitney Bowes Inc. has released a survey of the most effective method for reaching customers, and the results may surprise you.

Direct mail, the offline world’s precursor to e-mail spam, is the most effective method, the company reports. (But keep this in mind: Pitney Bowes is a giant producer of postal meters, mailing equipment, and copy machines). The report is based on a collaborative study with Norwalk, Conn.-based management consultants Peppers and Rogers Group. More than 350 U.S. households with annual incomes greater than $35,000 were surveyed via telephone.

The results concluded that e-mail and the Internet are not as effective for building customer relationships as traditional direct mail. Direct mail solicitations currently account for 65 percent of the total mail received by a household, up from 56 percent in 1987.

App Server Market Soaring

According to the latest crop of statistics from IDC, the worldwide application server market bucked all trends and grew 128 percent, reaching almost $2.2 billion in 2000.

This comes on top of 1999’s worldwide revenue growth of 110 percent, to $957 million.

IDC expects this strong growth to continue through 2005.

“It’s difficult to overstate the significance of this growth rate. When a market reaches the level of maturity that the (application server software platform, or ASSP) market reached in 1999, annual growth usually slows, not accelerates,” said Steve Garone, IDC’s vice president of application development and deployment research.

BEA Systems and IBM claimed the largest chunk of this still-growing market, with revenue shares of 18 percent and 15 percent, respectively. Sun trailed in third with 8 percent.

“Survival in the ASSP market requires more than just ASSP products. Vendors need to provide an e-business platform that includes all the functions necessary to build and deploy e-business applications, leveraging the ASSP as the foundation layer,” Garone said. “BEA and IBM both understand this requirement.”

This item was first printed in ServerWatch, an chúng tôi site.

Microsoft’s Next Surface May Be A Chromebook Competitor For Schools

If a low-cost Microsoft Surface with a cloud-based Windows operating system sounds a lot like a Chromebook, that’s probably no coincidence. Windows PCs have lost ground in the U.S. education market to Google’s browser-based laptops. As Microsoft sends out invitations for a May 2 event in New York, rumors suggest the company will announce a Surface for schools—among other things.

Why this matters: Microsoft has repeatedly struggled to compete with Chromebooks. Little more than a year ago, for example, Acer, chúng tôi Lenovo all launched rugged clamshell PCs featuring spillproof keyboards, with prices beginning at $199. Microsoft launched Intune for Education, a version of its Intune management application that was supposed to make managing 30 or so PCs per classroom a snap. Apparently, it didn’t help: Chromebook momentum is still climbing, according to Jay Chou, who tracks PC sales for IDC. 


Lenovo’s ThinkPad 11e Chromebook. Microsoft wants to get hardware makers to start thinking of Windows instead.

The target: Classroom Chromebooks

First, though, Microsoft has to convince technology purchasers at individual school districts that Windows machines are a stronger value. It could be a tough assignment.

“Chromebooks are successful in education for three key reasons,” Forrester analyst J.P. Gownder said in an email. “First, a great deal of educational software is now delivered over the web, which satisfies the precondition for using a web-based OS. Second, price—from a total cost of ownership perspective, which includes the device but also the manageability—has tended to come in lower for most schools. Third (and related), they are secure and manageable: As nearly zero-state devices, it’s easy to pass a Chromebook from student to student without security issues. And they are far easier to manage and deploy than existing Windows PC management tools.”


Microsoft’s Intune for Education attempted to overcome the limitations of Windows and attack Chromebooks. 

The solution: a cheap, managed Surface?

Rob Schultz

It’s unlikely this Surface 3 could survive with kids in a classroom.

Here’s a quick rundown of other rumors around this event:

A Surface Pro refresh built around Intel’s Kaby Lake chip is still expected at some point, but the scuttlebutt seems to be turning away from a launch at the May event. A more likely venue for a Surface Pro announcement might be Microsoft’s Build developer conference, starting just days later in Seattle.

Though Microsoft’s Windows Mobile champion Joe Belfiore recently resurfaced from a nine-month sabbatical, there’s no buzz on a Surface Phone.

Windows Mobile is apparently alive, if not well. “We continue to develop Windows 10 Mobile and support Lumia phones such as the Lumia 650, Lumia 950, and Lumia 950 XL, and phones from OEM partners like Acer, Alcatel, HP, Trinity and VAIO,” a Microsoft spokesman said recently.

Could Windows Cloud replace Windows 10 Mobile? Probably not, but it’s an enticing thought if the rumored Surface Phone ends up being a foldable device.

Windows Central also brings word that Windows is revealing more about the Cortana-powered speaker that was announced last year. Device setup features are being baked into Windows. Could Microsoft show off the first Cortana-powered devices in May?

Whatever happens, we’ll be onsite at both events to bring you the full report. 

This story was updated at 10:36 AM with additional details. This story was corrected on April 24 with the correct date. 

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