Alan Blackwell's work takes us through the historical definitions of programming.1 Initially, it is presented as the process of drawing up the "schedule" for the sequence of individual operations required to carry out a calculation. Later definitions expand this to the process of translating from a language convenient for humans to a language convenient for the computer. If we, as a community, adopt these narrow definitions, the entirety of programming may well be made redundant by the ceaselessly growing capabilities of generative AI. However, during the 1970s, Donald Knuth, and similarly Edsger Dijkstra, argued programming was something more.2,4 They argued that programming was the "art" of composing programs, which requires awareness of aesthetics, use of ingenuity, and inherent creativity. From this perspective, programming is more about composing pieces of code to solve a problem rather than writing individual lines of instructions.

The Trump administration is prioritizing the critical role of artificial intelligence in creating and upholding freedom. Just three weeks in, Vice President JD Vance declared at a global AI summit in Paris that AI "will make people more productive, more prosperous, and more free. The United States of America is the leader in AI, and our administration plans to keep it that way." To achieve this, the White House is working toward an AI action plan and calling on leading American AI companies to submit our best ideas. OpenAI is pleased to submit proposals today on a range of important considerations for AI from national security, to infrastructure and energy, to the federal government's own use of AI.

NASA is about to publish the world's first off-world aircraft accident investigation. Aside from making history, the report will help the agency plan ahead for the next generation of flying vehicles that will help humanity explore Mars. NASA engineers only intended the Mars Perseverance Rover's Ingenuity helicopter to complete a maximum of five experimental test flights over 30 days in 2021. The experimental vehicle, however, proved much more durable than expected. Over nearly three more years, Ingenuity ultimately flew 72 more times, racking up more than two hours of aerial travel and traveling 30 times farther than planned.

With global volatility and inflation impacting organizations across all industries, business agility has never been more important. Leaders turn to finance teams to get real-time insight into business performance and recommendations on future initiatives that will help them thrive amid disruption. But finance teams are overwhelmed with manual tasks, cobbling together data, and disconnected teams. Achieving game-changing business agility begins with augmenting the human ingenuity of your people with intelligent process automation. When it comes to reimagining processes with AI, automation, and analytics, many finance leaders don't know where to start.

It's remarkable to watch a five-year-old draw, void of any anxiety about what the world will think. We all start our lives creatively confident, happy to create and share our work with pride. And then, as we age, our comfort with creative expression declines. We're discouraged by the learning curve of creative skills and tools, by our tendency to compare ourselves to others, and by the harsh opinions of critics. As Picasso famously quipped, "All children are born artists, the problem is to remain an artist as we grow up."

NASA's Ingenuity helicopter is still pushing boundaries long after its first Mars liftoff. As CNET notes, the space agency has shared video of Ingenuity's milestone 25th flight on April 8th, when it broke duration and speed records. The robotic helicopter flew at 12MPH for just over two minutes and 41 seconds, providing footage of the Red Planet's rippling sands and rock fields as part of the 2,310-foot journey. The footage you see below was sped up to cut the viewing time to 35 seconds. The video doesn't include the very start and end of the trip, but for good reason.

Stone once described arms races as the inevitable product of there being "no limit to the ingenuity of science and no limit to the deviltry of human beings." This dark truth about the era of human-controlled "kinetic" weapons of mass destruction that so concerned Stone remains true today of the emerging range of increasingly automated systems that may now be fusing scientific ingenuity with a silicon-based deviltry of all its own. For most of history, from stones to siege guns, warfare consisted of hurling some amount of mass with sufficient energy to do serious harm. The general trend has been toward increasing mass and energy, giving weapons greater range. Yet, until the first automated guidance systems came into play during World War II, the "information content" of weaponry was quite small, reducing accuracy.

As artificial intelligence and its capabilities continue to expand, AI may be able to broaden creativity for companies moving forward. Forrester found in a recent study that AI tools available to technology executives will assist with overcoming digital repetition and that AI can help empower more unconventional thinking. In addition, AI can help power a cooperative mindset for a team and make for better collaboration. "This is less about the technology and more about the way the use of the technology facilitates collaboration by breaking silos and enabling different roles to work together on a common goal," said Diego Lo Giudice, Forrester's vice president and principal analyst. "Like with agile, identifying AI opportunities and building AI solutions requires various disparate stakeholders and roles like data scientists, business domain experts, designers, developers and testers to sit around the table, brainstorm and let the creative ideas flow. In breaking the traditional siloed enterprise structures, the various stakeholders and roles are forced to work together on common objectives."

Artificial intelligence systems derive their power in learning to perform their tasks directly from data. As a result, AI systems are at the mercy of their training data and in most cases are strictly forbidden to learn anything beyond what is contained in their training data. Data by itself has some principal problems: It is noisy, nearly never complete, and it is dynamic as it continually changes over time. This noise can manifest in many ways in the data -- it can arise from incorrect labels, incomplete labels or misleading correlations. As a result of these problems with data, most AI systems must be very carefully taught how to make decisions, act or respond in the real world. This'careful teaching' involves three stages.

Artificial Intelligence refers to the ingenuity of machines. This compares with the natural instincts of humans and animals. With Artificial Intelligence, machines perform tasks such as learning, planning, consulting, and problem-solving. Most notably, Artificial Intelligence mimics human ingenuity.