Keeping on the reporting from TTM 2018.
Neuroscience and Brain
The evolution of Brain Machine Interfaces is expected to progress at an accelerated pace in the coming decade, thanks to a multitude of research endeavor in many parts of the world.
Work is progressing both in the area of non-invasive and invasive approaches.
The centrifugal (brain > machine) path is more developed than the centripetal (machine > brain) path. This latter is usually limited to activate some muscle movement and some basic image visualization (i.e. you cannot transfer a sentence or a thought from a machine to a brain).
Companies like Paradromics are working on implants and on software to make sense out if the data harvested through the implant.
A main focus is in helping people with disabilities (suffered from a stroke resulting in paralyses, blindness, deafness, ALS,…). Clinical studies are distributed in several part of the world and in 20 years time it can be expected that BMI will become a standard rehabilitation procedure.
Technologies are being explored to assess the potential of communicating with patient in a coma, exploiting the remaining sensitivity to aural reactions (differential from high tone and low tone). Experiments have shown that coma patients may react to stimuli and “hear”.
Clearly the brain area is fraught with ethical issues. Some general principles exist, derived from previous situations (like the Nurnberg trial after the II War World) and can be clustered under Respect for people, Benefiting people and society and Justice/fairness. Notice that even these self understandable principles are fuzzy and “slippery”.
A presentation in this area emphasized how each principles is actually tough to implement since it has some antagonist interpretation of subjective nature.
Benefitting by improving a person life seems a no brainer, but the criteria of feeling better are subjective and sometimes a procedure intended to benefit a person is actually perceived as an imposition by the person (that is not given the choice to back off).
Agricultural Food Systems
There are plenty of opportunity in applying advanced technology to agriculture by embracing a Digital Transformation, like using sensors and data analyses to identify presence of bugs and their location for focused spraying of insecticide. This would dramatically cut on cost and on pollutants. Artificial Intelligence and Machine Learning are paving the way for this transformation.
Data analyses can also lead to minimize risks, hence decreasing insurance cost. In Hungary a project has started for applying blockchain to the agriculture value chain so tracing all players and making it possible to bank to evaluate risk and provide funding to small players.
An interesting presentation was given on using AI to assess food quality. The whole process is “re-engineered” starting with automation in the field, using sensing devices to inspect packaged food (like strawberry in boxes ready for shipment) and having the data analyzed by AI based software to draw the assessment. The AI is replacing human assessment making the assessment much more objective and standardized. Sensors are replacing the human eyes and AI is replacing human assessment.
The sensors (computer vision) and the AI can also be used for sorting (like separating strawberries with white shoulders and seedy tip from nice strawberries) resulting in different quality packaging and of course over time the sorting process will feed back to the harvesting so that the quality will keep getting better over time.
Notice the complexity of the assessment handed over to the AI: for strawberry 4 different quality parameters are considered, whilst, as an example, for cashew the parameters are 20.
IEEE has launched a Smart Agriculture Initiative looking at the whole value chain, addressing, among other thing the enormous waste of food world-wide (estimated in 40%).
The demand for food is expected to grow by 60% by 2050 (9 billion people better fed), increase of water demand by 55% and increase in energy demand by 40%. The goal is to promote smart technologies for enhancing the whole agriculture and food supply chain.
All presentations were linearly disruptive pointing to significant changes in the value chain and on who will be involved (more and more machines and AI).
A great presentation on “what happens when data velocity, variety and volume overwhelm us?” closed the session. One point is that potentially, thanks to AI industry can predict the future “with high accuracy” and then change it.
It is quite different to manage risk and quite something else to eliminate it.
Blockchain can be considered as the art of provenance ensuring food safety. IBM has demonstrated that by applying this technology the time to track the whole supply chain, from production to shelves goes down from a week to a second. More than that. It is absolutely accurate, identifying bad products and only those.
There might be, in the longer term more disruptions in terms of approach: rather than changing the production side change the consumption side. Use genetic modification to let humans eat differently, use Augmented Reality to make the new food palatable in the transition phase…
- Digital Transformation is affecting every area
- Data are taking the lead everywhere
- Artificial intelligence is already replacing human intelligence (and gut feeling)
- The changes are disruptive and impactful on society. Education is both affected and needs to be reinvented since it is going to be needed even more
- Ethical issues permeate the whole landscape
- There is no silver bullet pointing to an evolution that will be seen beneficial by everybody.