Any number derived from real observation is made up of three components. The first of these is the intended signal, the “perfect” value from the object being observed. The second is error (or noise) caused by environmental disturbance and/or interference. The third is bias, a regular and consistent deviation from the perfect value.

O = S + N + B, or observation equals signal plus noise plus bias

The signal usually is predictably constant, as is the bias. Identifying and eliminating bias requires a set of techniques beyond the scope of this article, so for the remainder of this, we will consider both as components of the signal, leaving a somewhat simpler equation for our observation.

O = S + N, or observation equals signal plus noise

This article focuses on removing the random noise component from the observation and leaving the signal component. The noise is in the form of chance variation, which sometimes enhances the signal and sometimes detracts from it. If we could separate the noise from the signal and eliminate it, our observation would be pure signal, or a precise and consistent value.

When your own inbox is overflowing with unread messages, it may not seem like the best tactic, but with the right approach, email can be a powerful negotiation tool, not least in the B2B realm. According to 2019 research by IACCM, a global contract management association, about 75 percent of contract negotiations are completely virtual. 

Nowadays, many B2B sales negotiations involve an open-bid process with a standardized communication where relationship bonds are less important. In that context, emails offer a number of advantages. For instance, they can be instantly accessed, often by many parties in an organization, thus creating transparency. Emails also allow a rich diversity of materials to be used as attachments.

Negotiations via email can be particularly suitable when gender, age, or racial biases—or linguistic issues such as a strong accent—could mar the process. It can also help when there is a power distance between parties, or when some voices risk being unheard.

The future of advanced manufacturing in the United States is being built at innovative facilities that enable experimentation in process and product development. The people and organizations at these next-generation facilities are part of a collaborative effort to remove barriers of entry and create an ecosystem to build supply chains and provide a path for the commercialization of emerging technologies.

These facilities are working on initiatives that include:
• Using advanced fiber technology to make programmable backpacks that have no wires or batteries but connect to the digital world.
• Using light instead of electronics to power cloud-based data centers, increasing the speed of transfer tenfold while drastically reducing energy use and cost.
• Extending the range of electric vehicles by reducing weight and mitigating energy loss during transfers.

This would not be possible without Manufacturing USA, a network of 16 manufacturing innovation institutes and their sponsoring federal agencies—the Departments of Commerce, Defense, and Energy. Manufacturing USA was created in 2014 to secure U.S. global leadership in advanced manufacturing by connecting people, ideas, and technology.

First published Dec. 7, 2020, on MIT Computer Science & Artificial Intelligence Lab (CSAIL) news.

In a classic experiment on human social intelligence by Warneken and Tomasello, an 18-month-old toddler watches a man carry a stack of books toward an unopened cabinet. When the man reaches the cabinet, he clumsily bangs the books against the door of the cabinet several times, then makes a puzzled noise.

Something remarkable happens next: The toddler offers to help. Having inferred the man’s goal, the toddler walks up to the cabinet and opens its doors, allowing the man to place his books inside. But how is the toddler, with such limited life experience, able to make this inference?

Recently, computer scientists have redirected this question toward computers: How can machines do the same?

A team of Lawrence Livermore National Laboratory (LLNL) scientists has simulated the droplet-ejection process in an emerging metal 3D-printing technique called “liquid metal jetting” (LMJ), a critical aspect to the continued advancement of liquid metal printing technologies.

In their paper, which was published in the journal Physics of Fluids, the team describes the simulating of metal droplets during LMJ, a novel process in which molten droplets of liquid metal are jetted from a nozzle to 3D-print a part in layers. The process does not require lasers or metal powder and is more similar to inkjet printing techniques.

Using the model, researchers studied the primary breakup dynamics of the metal droplets, essential to improving the understanding of LMJ. LMJ has advantages over powder-based approaches in that it provides a wider material set and does not require production or handling of potentially hazardous powders, researchers said.

This story was originally published by Knowable Magazine.

When Stefanie Tellex was 10 or 12, around 1990, she learned to program. Her great-aunt had given her instructional books, and she would type code into her father’s desktop computer. One program she typed in was a famous artificial intelligence program called ELIZA, which aped a psychotherapist. Tellex would tap out questions, and ELIZA would respond with formulaic text answers.

“I was just fascinated with the idea that a computer could talk to you, that a computer could be alive, like a person is alive,” Tellex says. Even ELIZA’s rote answers gave her a glimmer of what might be possible.

Advanced metal alloys are essential in key parts of modern life, from cars to satellites, from construction materials to electronics. But creating new alloys for specific uses, with optimized strength, hardness, corrosion resistance, conductivity, and so on, has been limited by researchers’ fuzzy understanding of what happens at the boundaries between the tiny crystalline grains that make up most metals.

When two metals are mixed together, the atoms of the secondary metal might collect along these grain boundaries, or they might spread out through the lattice of atoms within the grains. The material’s overall properties are determined largely by the behavior of these atoms, but until now there has been no systematic way to predict what they will do.

Researchers at MIT have now found a way, using a combination of computer simulations and a machine-learning process, to produce the kinds of detailed predictions of these properties that could guide the development of new alloys for a wide variety of applications. The findings are described today in the journal Nature Communications, in a paper by graduate student Malik Wagih, postdoc Peter Larsen, and professor of materials science and engineering Christopher Schuh.

As someone who has helped companies in a wide variety of industries for the last 30 years solve many problems using risk-based thinking, I cannot think of an issue that I have worked on that is more important than preventing the spread of Covid-19. With three high-risk people in my home, I have spent considerable time studying Covid-19 since February 2020. By applying the risk-based thinking techniques I have used, I believe there is a method for saving 100,000 lives before we get the protection the new vaccines are going to provide during the next three or four months.

The ongoing global Covid-19 pandemic has forced companies of all types to rapidly update policies and procedures governing how they share information in response to a world that is constantly changing around them. For the manufacturing sector in particular, their workforce is more spread out than it has ever been, but communication remains essential.

Many knew that telecommuting was the future of the workforce in the United States—it’s just that few could have predicted that “the future” would have come along quite as quickly as it did. In April 2020, at the peak of the first wave of the pandemic, a massive 51 percent of people were working from home. Although that number had dropped to 33 percent by the following October, it’s still enormous, creating a challenge for the manufacturing sector in particular.

Thankfully, a wide range of digital tools have emerged for manufacturing companies that not only enable the rapid communication need right now, but that may also put them in a better decision-making position than they were in before all of this began.

Whether you subscribe to the scientific definition of data (information on which operations are performed by a computer and transmitted in the form of electrical signals) or the philosophical definition (that which is known and used as the basis of reasoning or calculation), I think most people use the word “data” incorrectly.

If you’re a data scientist, or you become upset that this will be the only time I use the singular form “datum,” this article will probably disgust you, and I apologize. On the other hand, if you’re in marketing, sales, or just about any other department, then I hope this will help clarify the overused but super-useful word “data.”

One of the roots of the problem with the word is when it’s used as a generic noun. It’s overused and causes confusion. Confusion is the enemy, particularly in sales and marketing. Granted, data is a useful word because it’s short. When used among a group of people who work with the same type of data, the term works as a reference for what everyone knows you’re talking about.