标签：#ics

1. Introduction Excel is much more than a spreadsheet for entering numbers. It can be used as a data-analysis tool that helps analysts inspect, validate, filter, summarize, and prepare raw data before deeper analysis begins. In typical analytics, the quality of the final work depends heavily on the quality of the data used; therefore, data cleaning is not an optional step—it is the foundation of effective data analysis. This article demonstrates key Week 1 Excel concepts _using an employee dataset containing _employee IDs, names, departments, gender, marital status, hire dates, salaries, educational level, performance score among others. The raw file intentionally contains common data-quality issues: inconsistent capitalization on the First and Last names, blank records, duplicate employee records, varying department names, currency and dates that need review. By working through these issues, the article shows how Excel’s formatting tools, text functions, filters, conditional formatting, numerical functions, conditional summaries, and date functions can turn a messy workbook into an analysis-ready dataset. 2. Why Data Cleaning Matters Data cleaning is more than just about removing errors. By standardizing formats and categories, we make datasets more transparent, usable, and valuable for management analysis and reporting purposes. Data analysis is simple – garbage in, garbage out. A dashboard or prediction can appear professional, but can be misleading if the underlying data has duplicates, blank values, inconsistent categories or incorrectly formatted text and dates. For example, “IT” “I.T.” and “Information Tech” can be viewed as different department values if naming is not standardized. Duplication of an employee ID can inflate employee counts and department totals. A blank performance score might mean that something is missing and should be looked into and dates saved as text cannot be reliably used in calculations such as employee tenure checks. A good practice

Alt-right troll Milo Yiannopoulos was arrested by ICE on Thursday, and today the Department of Homeland Security confirmed to Reuters and the Washington Post that he had been deported to the UK. Yiannopoulos came to the US legally in 2019, but overstayed his visa. After he failed to appear in court for an immigration hearing, […]

I just wrote about the billion-dollar rounds flooding into humanoid robotics. Here is the story from the other end of the scale, and I find it more encouraging. Hugging Face's open-source robot, a 25-centimeter bipedal machine with fifteen actuators and a sensor kit that includes a camera, speaker, LiDAR, NFC, Bluetooth, and WiFi, just passed a million dollars in sales. Fully open hardware, openly documented, quietly making real money. One of these robotics stories is funded like an industrial giant. The other is a small, open, shippable thing that people are actually buying. They are both true, and the small one is the one most builders can learn from. Open hardware turned out to be a business The reflexive assumption about open-source hardware is that you cannot make money on it, because anyone can copy the design. Hugging Face's robot is a live counterexample. The plans are open, the software stack is open through their LeRobot ecosystem, and it crossed a million in sales anyway. That is worth sitting with, because it means openness and revenue are not the opposites people assume. The reason it works is the same reason open-source software companies work. Most buyers do not want to source fifteen actuators, fabricate a chassis, and debug a sensor stack to save money on a robot that already exists and is affordable. They want the finished thing, they want it to work out of the box, and they are happy to pay the people who designed it. Openness is not the giveaway that kills the business. It is the trust and the ecosystem that make the business, because you can see exactly what you are buying, modify it, and build on a platform other people are also building on. Why this is the better story for builders The mega-funded humanoid companies are placing a bet only a handful of players can place: billions of dollars, years of runway, factories. That is a real path, and it is not your path or mine. The Hugging Face robot is the other path, and it is copyable. Small, open

Technical progress has encouraged a new batch of companies to jump in on the promise of profits from humanoid robots. And they're all Chinese automakers.

The longtime far-right operator and troll, a UK citizen, is being held in ICE custody pending his removal from the United States.

“Do we need to look at the ceiling before going to the bathroom? Can we actually trust the water is safe to drink?” asks one GSA worker.

This essay was written with Kasra Rafi, and originally appeared in The Guardian. Earlier this month, about 40 top mathematicians gathered at OpenAI’s offices to discuss the future of their profession. The meeting was off-the-record, but if recent articles by mathematicians are any guide, it was mostly pretty glum. People fear for their jobs, their careers and the work they love. We think the contrary view is more likely, at least in the short-term. AI models are nowhere near as capable as experienced academic mathematicians. This isn’t to say that AIs aren’t producing stunning mathematical results at the level of PhD researchers. In mid-May, OpenAI ...

Influential right-wing conspiracy theorists and MAGA-aligned pundits are falsely claiming that the Covid vaccine killed Dolly Parton, the vaccination advocate and philanthropist.

A federal judge has called the Department of Defense’s designation of Anthropic as a national security supply-chain risk “illegal and baseless.”

Hugging Face is taking orders for the Microduck, a $399 tiny open-source duck robot that developers can train at home out of the box.

If you've ever wanted a robotic duck as a companion, now's your chance.

You have questions about data centers, and WIRED has answers. Join our livestream on September 10 and our panel of experts will tell you everything you need to know.

Strategists in both parties have long thought that everything would need to go right for Democrats to take over Congress this fall. Their map, though, is becoming more forgiving.

In June, a FEMA official infamous for claiming to have teleported to a Waffle House was reportedly ousted. He’s still employed by the government, though—and meeting with a Trump fixer.

Meta just agreed to make sweeping changes for teens as part of a child safety settlement reached with attorneys general across the US. Under the agreement, Meta must apply new safeguards across Instagram and Facebook that will change how teens interact with the social media platforms, as well as restrict their use during certain times. […]

Beijing’s endlessly delightful Robot Games featured tons of impressive stunts. But the most mind-blowing tricks challenged the humanoid’s brain, not its brawn.

More than 15 politicians from across the country have signed on to the AI Pact, vowing to regulate data centers and AI. “We’ve got to get this right,” says Senate candidate Dan Osborn of Nebraska.

The Three-Eyed Raven Problem: What Bran Stark could see the past, understand the present, and glimpse what might come next. Modern logistics AI is being asked to do something surprisingly similar. There is a moment in Game of Thrones when Bran Stark stops being merely a person who remembers events and becomes something much more powerful. As the Three-Eyed Raven, Bran has access to an enormous history of people, places, decisions, betrayals, and consequences. He does not simply possess information. He can retrieve the right information from the past and use it to understand what is happening now . That distinction matters. Because the logistics industry is beginning to face its own Three-Eyed Raven problem. We already have enormous amounts of data. Shipment events. GPS signals. Carrier performance. Customs documentation. warehouse scans. Purchase orders. invoices. weather feeds. port congestion. customer commitments. emails. SOPs. tariffs. exception histories. The problem is no longer simply: Can AI access all of this information? The more important question is: Can an AI system remember the right things, at the right time, for the right shipment—and forget what it should not retain? That question may become one of the defining problems of enterprise AI. AI Is Moving From Intelligence to Memory Much of the first wave of Generative AI focused on what models know . The next wave is increasingly about what AI systems can remember, retrieve, reason about, and act upon over time . This distinction becomes particularly important with AI agents. A chatbot might answer: “What documents are normally required for this shipment?” An AI logistics agent needs to understand something much harder: Which shipment are we discussing? What happened to it yesterday? Which carrier is moving it? Has this lane experienced similar delays before? What did the customer request? What customs rules apply? Was an exception already escalated? What action worked the last time this happened? Has a

Messing with the genetic code might have gotten a bit easier to do.

Perceptron offers an AI model that it says can help machines navigate the world while also providing in-depth visual intelligence.