IXICO PLC (LON:IXI) has been chosen as a small-to-medium enterprise (SME) partner in the London Medical Imaging & Artificial Intelligence Centre for Value-Based Healthcare. The data analytics company focused on delivering insights in neuroscience said the newly established centre, launched on 28 February, is part of the UK Government's Industrial Strategy Challenge Fund. Led by King's College London, the new Centre will develop and train sophisticated artificial intelligence algorithms from NHS medical images and patient data to provide tools for clinicians to speed up and improve diagnosis and care across a number of patient pathways including dementia, heart failure and cancer. "We are excited to be selected to be part of this flagship UK Government initiative to ensure that UK science is at the forefront of the application and impact of artificial intelligence to the future of healthcare," said Giulio Cerroni, the chief executive officer of IXICO. "The development and application of artificial intelligence technology will enable IXICO to develop highly valued, innovative solutions to our biopharmaceutical clients in support of their neuroscience clinical development programmes. We are looking forward to collaborations with other leaders across this expanding field of research," he added.

There are already networks in place that can detect seismic activity and send an alert as soon as an earthquake is underway. But the current technology doesn't actually send the alert until all of the sensors in the network covering a given area have detected seismic waves. And it could take about a minute from the moment activity is initially detected until an alert hits the wire. A minute is a long time in an emergency. Government agencies, public works, and local utilities ideally need to alert the populace and do things like halt trains and shut off power lines to potentially mitigate damage – every second counts.

An ever-present threat to any given country's national security is that of cybersecurity. There are always hackers that want to use technology for malicious purposes, not to say the long list of adversaries that a country can pile up along the years. That's so as what it is at stake is millions of sensible data from citizens, companies, directories, senior officers and members of the government, state's papers and more. Unfortunately, not all Governments take this peril as seriously as they should, and the efforts towards creating cyber-defense strategies – in most countries – lack budget, personnel and even real, field knowledge. Before this absence of real policies, Artificial Intelligence might be well seen as a good starting point where to build the walls that keep out any possible threats.

The changes that artificial intelligence will bring to the technology landscape could pale in comparison to what it wreaks on global society. Humans need not be taken over by intelligent machines, as some doomsday soothsayers predict, to face a brave new world in which they must revolutionize the way they conduct their daily existence. From employment upheaval to environmental maintenance, people may face hard choices as they adapt to the widespread influence of artificial intelligence advances. Experts offer that artificial intelligence (AI) itself will undergo significant growing pains as it transitions from childhood into adolescence. Where ongoing research largely focuses on applied AI, it eventually will expand to cover a broader range of aspects.

The vulnerabilities of machine learning models open the door for deceit, giving malicious operators the opportunity to interfere with the calculations or decision making of machine learning systems. Scientists at the Army Research Laboratory, specializing in adversarial machine learning, are working to strengthen defenses and advance this aspect of artificial intelligence. Often, in a data set, corrupted inputs or an adversarial attack enters a machine learning model undetected. Adversaries also impact a model whether or not they know the machine learning algorithm in use, training a substitute machine learning model for use on a "victim" model. Corruption can even occur on sophisticated machine learning models trained with an abundance of data to perform critical tasks.

SpaceX launched its Crew Dragon capsule for the first time late Friday in a test flight without humans aboard, a milestone that moves the Elon Musk-led company closer to ferrying NASA astronauts to the International Space Station. The capsule, some 400 pounds of cargo and a mannequin passenger named Ripley, lifted off on a SpaceX Falcon 9 rocket at 11:49 p.m. Pacific time from the same Florida pad where Apollo and space shuttle programs once began their missions. The Crew Dragon deployed from the rocket's second-stage about 11 minutes after lift-off, sending it on a trajectory to the space station where it is scheduled to dock early Sunday morning. The Falcon 9 rocket landed on a floating sea platform in the Atlantic Ocean about 10 minutes after liftoff. Friday's launch was the first test flight for NASA's commercial crew program, a public-private partnership involving Hawthorne-based SpaceX and Boeing Co., which have contracts worth a combined total of $6.8 billion to build separate craft to transport astronauts to the space station.

The US is about to take a major step towards being able to fly its astronauts into space once again. California's SpaceX firm is performing a demonstration of a new rocket and capsule system, which, if it works well, will be approved to carry people. Routine crew missions to the space station could start later this year. Not since the retirement of the shuttles in 2011 has America been able to put humans in orbit. It's had to pay to use Russian Soyuz vehicles instead.

We may remember 2018 as the year when technology's dystopian potential became clear, from Facebook's role enabling the harvesting of our personal data for election interference to a seemingly unending series of revelations about the dark side of Silicon Valley's connect-everything ethos. It's been enough to exhaust even the most imaginative sci-fi visionaries. "It doesn't so much feel like we're living in the future now, as that we're living in a retro-future," novelist William Gibson wrote this month on Twitter. More awaits us in 2019, as surveillance and data-collection efforts ramp up and artificial intelligence systems start sounding more human, reading facial expressions and generating fake video images so realistic that it will be harder to detect malicious distortions of the truth. But there are also countermeasures afoot in Congress and state government -- and even among tech-firm employees who are more active about ensuring their work is put to positive ends.

This blog is syndicated from The New Rules of Privacy: Building Loyalty with Connected Consumers in the Age of Face Recognition and AI. To learn more click here. Since the invention of face recognition in the 1960s, has any single technology sparked more fascination for public safety officials, companies, journalists and Hollywood? When people learn that I'm the CEO of a face recognition company, they commonly reference its fictional use in shows like CSI, Black Mirror or even films such as the 1980s James Bond movie A View to a Kill. Most often, however, they mention Minority Report starring Tom Cruise. For the uninitiated, the film is based on a futuristic Phillip K. Dick short story and is set in the year 2054.

Looking to leverage AI in your organization? Don't miss the Business Summit at the AI Conference in New York, April 15–18, 2019. Register before March 1 to save with Early Price. In a recent post, we described what it would take to build a sustainable machine learning practice. By "sustainable," we mean projects that aren't just proofs of concepts or experiments. A sustainable practice means projects that are integral to an organization's mission: projects by which an organization lives or dies. These projects are built and supported by a stable team of engineers, and supported by a management team that understands what machine learning is, why it's important, and what it's capable of accomplishing. Finally, sustainable machine learning means that as many aspects of product development as possible are automated: not just building models, but cleaning data, building and managing data pipelines, testing, and much more. Machine learning will penetrate our organizations so deeply that it won't be possible for humans to manage them unassisted. Organizations throughout the world are waking up to the fact that security is essential to their software projects. Nobody wants to be the next Sony, the next Anthem, or the next Equifax.