Lifeboat Foundation

No industry will be spared.

The pharmaceutical business is perhaps the only industry on the planet, where to get the product from idea to market the company needs to spend about a decade, several billion dollars, and there is about 90% chance of failure. It is very different from the IT business, where only the paranoid survive but a business where executives need to plan decades ahead and execute. So when the revolution in artificial intelligence fueled by credible advances in deep learning hit in 2013–2014, the pharmaceutical industry executives got interested but did not immediately jump on the bandwagon. Many pharmaceutical companies started investing heavily in internal data science R&D but without a coordinated strategy it looked more like re-branding exercise with the many heads of data science, digital, and AI in one organization and often in one department. And while some of the pharmaceutical companies invested in AI startups no sizable acquisitions were made to date. Most discussions with AI startups started with “show me a clinical asset in Phase III where you identified a target and generated a molecule using AI?” or “how are you different from a myriad of other AI startups?” often coming from the newly-minted heads of data science strategy who, in theory, need to know the market.

However, some of the pharmaceutical companies managed to demonstrate very impressive results in the individual segments of drug discovery and development. For example, around 2018 AstraZeneca started publishing in generative chemistry and by 2019 published several impressive papers that were noticed by the community. Several other pharmaceutical companies demonstrated impressive internal modules and Eli Lilly built an impressive AI-powered robotics lab in cooperation with a startup.

Continue reading “Deep Dive Into Big Pharma AI Productivity: One Study Shaking The Pharmaceutical Industry” »

The 400 kilogram wheeled system moves about the lab guided by LIDAR laser scanners and has an industrial robotic arm made by German firm Kuka that it uses to carry out tasks like weighing out solids, dispensing liquids, removing air from the vessel, and interacting with other pieces of equipment.

In a paper in Nature, the team describes how they put the device to work trying to find catalysts that speed up reactions that use light to split water into hydrogen and oxygen. To do this, the robot used a search algorithm to decide how to combine a variety of different chemicals and updated its plans based on the results of previous experiments.

Continue reading “This Robotic Chemist Does Over 600 Experiments a Week and Learns From Its Own Work” »

A new study offers a better understanding of the hidden network of underground electrical signals being transmitted from plant to plant – a network that has previously been shown to use the Mycorrhizal fungi in soil as a sort of electrical circuit.

Through a combination of physical experiments and mathematical models based on differential equations, researchers explored how this electrical signalling works, though it’s not clear yet exactly what messages plants might want to transmit to each other.

The work builds on previous experiments by the same team looking at how this subterranean messaging service functions, using electrical stimulation as a way of testing how signals are carried even when plants aren’t in the same soil.

That in turn enables a massive software upgrade known as the “autonomy module,” a playbook of algorithms that tell the weapon how to respond to specific changes on the battlefield, whether that means the sighting of a new threat or the destruction of some of the collaborative weapons.

A new approach to designing motion plans for multiple robots grows “trees” in the search space to solve complex problems in a fraction of the time.

In one of the more memorable scenes from the 2002 blockbuster film Minority Report, Tom Cruise is forced to hide from a swarm of spider-like robots scouring a towering apartment complex. While most viewers are likely transfixed by the small, agile bloodhound replacements, a computer engineer might marvel instead at their elegant control system.

Continue reading “New Algorithm Coordinates Complex Behaviors Between Hundreds of Robots in a Fraction of the Time” »

A few scant equations can explain a variety of phenomena in our universe, over vast gulfs of space and time. Here’s a taste of just how powerful modern physics can be.

Neurons, specialized cells that transmit nerve impulses, have long been known to be a vital element for the functioning of the human brain. Over the past century, however, neuroscience research has given rise to the false belief that neurons are the only cells that can process and learn information. This misconception or ‘neurocomputing dogma’ is far from true.

An astrocyte is a different type of brain cell that has recently been found to do a lot more than merely fill up spaces between neurons, as researchers believed for over a century. Studies are finding that these cells also play key roles in brain functions, including learning and central pattern generation (CPG), which is the basis for critical rhythmic behaviors such as breathing and walking.

Although astrocytes are now known to underlie numerous brain functions, most existing computer systems inspired by the human brain only target the structure and function of neurons. Aware of this gap in existing literature, researchers at Rutgers University are developing brain-inspired algorithms that also account for and replicate the functions of astrocytes. In a paper pre-published on arXiv and set to be presented at the ICONS 2020 Conference in July, they introduce a neuromorphic central pattern generator (CPG) modulated by artificial astrocytes that successfully entrained several rhythmic walking behaviors in their in-house robots.

Big data company Openairlines claims to be helping make flying more eco-friendly through in-depth data analysis, but it may be saving more money than fuel.

Dimensionality reduction is an unsupervised learning technique.

Nevertheless, it can be used as a data transform pre-processing step for machine learning algorithms on classification and regression predictive modeling datasets with supervised learning algorithms.

There are many dimensionality reduction algorithms to choose from and no single best algorithm for all cases. Instead, it is a good idea to explore a range of dimensionality reduction algorithms and different configurations for each algorithm.